IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v22y2022i2p97-122n1.html
   My bibliography  Save this article

European Green Deal Implications on Country Level Energy Consumption

Author

Listed:
  • Jaržemskis Andrius

    (Ph.D., Associate professor Vilnius University, Faculty of Economics and Business Administration, Sauletekio avenue 9 Vilnius, Lithuania)

  • Jaržemskienė Ilona

    (Ph.D., Associate Professor Vilnius Gediminas Technical University, Faculty of Transport Engineering Department of Logistics and transport management Plytines 27, Vilnius, Lithuania)

Abstract

Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments. Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles. Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations. Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

Suggested Citation

  • Jaržemskis Andrius & Jaržemskienė Ilona, 2022. "European Green Deal Implications on Country Level Energy Consumption," Folia Oeconomica Stetinensia, Sciendo, vol. 22(2), pages 97-122, December.
    • Handle: RePEc:vrs:foeste:v:22:y:2022:i:2:p:97-122:n:1
    DOI: 10.2478/foli-2022-0021
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/foli-2022-0021
    Download Restriction: no
    File URL: https://libkey.io/10.2478/foli-2022-0021?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
    ---><---

    References listed on IDEAS

    as
    1. Lin, Chengtao & Wu, Tian & Ou, Xunmin & Zhang, Qian & Zhang, Xu & Zhang, Xiliang, 2013. "Life-cycle private costs of hybrid electric vehicles in the current Chinese market," Energy Policy, Elsevier, vol. 55(C), pages 501-510.
    2. Wu, Yang Andrew & Ng, Artie W. & Yu, Zichao & Huang, Jie & Meng, Ke & Dong, Z.Y., 2021. "A review of evolutionary policy incentives for sustainable development of electric vehicles in China: Strategic implications," Energy Policy, Elsevier, vol. 148(PB).
    3. Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.
    4. Bansal, Prateek & Kumar, Rajeev Ranjan & Raj, Alok & Dubey, Subodh & Graham, Daniel J., 2021. "Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles," Energy Economics, Elsevier, vol. 100(C).
    5. Musti, Sashank & Kockelman, Kara M., 2011. "Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 707-720, October.
    6. Noel, Lance & Papu Carrone, Andrea & Jensen, Anders Fjendbo & Zarazua de Rubens, Gerardo & Kester, Johannes & Sovacool, Benjamin K., 2019. "Willingness to pay for electric vehicles and vehicle-to-grid applications: A Nordic choice experiment," Energy Economics, Elsevier, vol. 78(C), pages 525-534.
    7. Tobias Haas & Hendrik Sander, 2020. "Decarbonizing Transport in the European Union: Emission Performance Standards and the Perspectives for a European Green Deal," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    8. Pasaoglu, Guzay & Honselaar, Michel & Thiel, Christian, 2012. "Potential vehicle fleet CO2 reductions and cost implications for various vehicle technology deployment scenarios in Europe," Energy Policy, Elsevier, vol. 40(C), pages 404-421.
    9. Prateek Bansal & Rajeev Ranjan Kumar & Alok Raj & Subodh Dubey & Daniel J. Graham, 2021. "Willingness to Pay and Attitudinal Preferences of Indian Consumers for Electric Vehicles," Papers 2101.08008, arXiv.org, revised May 2021.
    10. Gough, Rebecca & Dickerson, Charles & Rowley, Paul & Walsh, Chris, 2017. "Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage," Applied Energy, Elsevier, vol. 192(C), pages 12-23.
    11. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    12. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    13. Qian Zhang & Xunmin Ou & Xiaoyu Yan & Xiliang Zhang, 2017. "Electric Vehicle Market Penetration and Impacts on Energy Consumption and CO 2 Emission in the Future: Beijing Case," Energies, MDPI, vol. 10(2), pages 1-15, February.
    14. Kim, Ju-Hee & Kim, Hyo-Jin & Yoo, Seung-Hoon, 2019. "Willingness to pay for fuel-cell electric vehicles in South Korea," Energy, Elsevier, vol. 174(C), pages 497-502.
    15. Wu, Geng & Inderbitzin, Alessandro & Bening, Catharina, 2015. "Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis and projection across market segments," Energy Policy, Elsevier, vol. 80(C), pages 196-214.
    16. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
    17. Ma, Shao-Chao & Xu, Jin-Hua & Fan, Ying, 2019. "Willingness to pay and preferences for alternative incentives to EV purchase subsidies: An empirical study in China," Energy Economics, Elsevier, vol. 81(C), pages 197-215.
    18. Jochem, Patrick & Doll, Claus & Fichtner, Wolf, 2016. "External costs of electric vehicles," MPRA Paper 91602, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    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. Jia, Wenjian & Jiang, Zhiqiu & Wang, Qian & Xu, Bin & Xiao, Mei, 2023. "Preferences for zero-emission vehicle attributes: Comparing early adopters with mainstream consumers in California," Transport Policy, Elsevier, vol. 135(C), pages 21-32.
    2. Almansour, Mohammed, 2022. "Electric vehicles (EV) and sustainability: Consumer response to twin transition, the role of e-businesses and digital marketing," Technology in Society, Elsevier, vol. 71(C).
    3. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    4. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    5. Shi, Lei & Wu, Rongxin & Lin, Boqiang, 2023. "Where will go for electric vehicles in China after the government subsidy incentives are abolished? A controversial consumer perspective," Energy, Elsevier, vol. 262(PA).
    6. Iogansen, Xiatian & Wang, Kailai & Bunch, David & Matson, Grant & Circella, Giovanni, 2023. "Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    7. Oryani, Bahareh & Koo, Yoonmo & Shafiee, Afsaneh & Rezania, Shahabaldin & Jung, Jiyeon & Choi, Hyunhong & Khan, Muhammad Kamran, 2022. "Heterogeneous preferences for EVs: Evidence from Iran," Renewable Energy, Elsevier, vol. 181(C), pages 675-691.
    8. Zhao, Xingrong & Ma, Ye & Shao, Shuai & Ma, Tieju, 2022. "What determines consumers' acceptance of electric vehicles: A survey in Shanghai, China," Energy Economics, Elsevier, vol. 108(C).
    9. Li, Ping & Zhang, ZhongXiang, 2023. "The effects of new energy vehicle subsidies on air quality: Evidence from China," Energy Economics, Elsevier, vol. 120(C).
    10. Jia, Wenjian & Chen, T. Donna, 2023. "Investigating heterogeneous preferences for plug-in electric vehicles: Policy implications from different choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    11. Tan, Yang & Fukuda, Hiroatsu & Li, Zhang & Wang, Shuai & Gao, Weijun & Liu, Zhonghui, 2022. "Does the public support the construction of battery swapping station for battery electric vehicles? - Data from Hangzhou, China," Energy Policy, Elsevier, vol. 163(C).
    12. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    13. Bhat, Furqan A. & Verma, Ashish, 2024. "Electric two-wheeler adoption in India – A discrete choice analysis of motivators and barriers affecting the potential electric two-wheeler buyers," Transport Policy, Elsevier, vol. 152(C), pages 118-131.
    14. Florian Ziel & Kevin Berk, 2019. "Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules," Papers 1910.07325, arXiv.org.
    15. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    16. Ashok Kumar Sar, 2023. "Price estimation for Amazon Prime video in India," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(4), pages 312-318, August.
    17. Evgeny Nefedov & Seppo Sierla & Valeriy Vyatkin, 2018. "Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings," Energies, MDPI, vol. 11(8), pages 1-18, August.
    18. Munshi, Talat & Dhar, Subash & Painuly, Jyoti, 2022. "Understanding barriers to electric vehicle adoption for personal mobility: A case study of middle income in-service residents in Hyderabad city, India," Energy Policy, Elsevier, vol. 167(C).
    19. Dhirendra Kumar Tiwari & Urvashi Tandon & Amit Mittal, 2024. "Analysing adoption of eVehicles among Indian consumers: a novel approach towards sustainable transport," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 22997-23023, September.
    20. Yijiao Wang & Guoguang Zhou & Ting Li & Xiao Wei, 2019. "Comprehensive Evaluation of the Sustainable Development of Battery Electric Vehicles in China," Sustainability, MDPI, vol. 11(20), pages 1-27, October.

    More about this item

    Keywords

    electricity demand; transport forecasts; policy measures; investment; scenario modelling;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O21 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Planning Models; Planning Policy
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    Statistics

    Access and download statistics

    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:vrs:foeste:v:22:y:2022:i:2:p:97-122:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.