IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v166y2021ics0040162521000834.html
   My bibliography  Save this article

Forecasting the development trend of low emission vehicle technologies: Based on patent data

Author

Listed:
  • Yuan, Xiaodong
  • Cai, Yuchen

Abstract

With the increasing awareness of environmental protection, there is a consensus on developing low emission vehicle (LEV) technologies but the trend is unclear. The LEV technologies, including hybrid electric vehicle (HEV), battery electric vehicle (BEV), and fuel cell electric vehicle (FCEV) technology, are considered as alternative technologies for the conventional internal combustion engine vehicles (ICEVs). The purpose of the paper is to forecast the future development trend of drivetrain technologies. In doing so, a revised method of technological forecasting is proposed on the basis of the S-curve simulation for growth curves and entropy weight method for ranking candidates, which leads to forecasting results are more objective and accurate. Our findings highlight that HEV has the most promising future, followed by BEV and ICEV, but FCEV develops slowly. The implications are that policymakers should maintain the principle of technology-neutral when implementing various policies, while enterprises should be aware of the hybridization trend of vehicles in the business strategy-making process.

Suggested Citation

  • Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000834
    DOI: 10.1016/j.techfore.2021.120651
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521000834
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2021.120651?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. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Ahn, Sang-Jin, 2020. "Three characteristics of technology competition by IoT-driven digitization," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    3. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    4. Ruzzenenti, F. & Basosi, R., 2009. "Evaluation of the energy efficiency evolution in the European road freight transport sector," Energy Policy, Elsevier, vol. 37(10), pages 4079-4085, October.
    5. Yuan, Xiaodong & Li, Xiaotao, 2020. "A network analytic method for measuring patent thickets: A case of FCEV technology," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    6. Zarazua de Rubens, Gerardo, 2019. "Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market," Energy, Elsevier, vol. 172(C), pages 243-254.
    7. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    8. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    9. Yamakawa, Peter & Rees, Gareth H. & Manuel Salas, José & Alva, Nikolai, 2013. "The diffusion of mobile telephones: An empirical analysis for Peru," Telecommunications Policy, Elsevier, vol. 37(6), pages 594-606.
    10. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    11. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    12. Chan-Yuan Wong & Kim-Leng Goh, 2010. "Modeling the behaviour of science and technology: self-propagating growth in the diffusion process," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 669-686, September.
    13. René Lezama-Nicolás & Marisela Rodríguez-Salvador & Rosa Río-Belver & Iñaki Bildosola, 2018. "A bibliometric method for assessing technological maturity: the case of additive manufacturing," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1425-1452, December.
    14. Zhang, Xian & Wang, Ke & Hao, Yu & Fan, Jing-Li & Wei, Yi-Ming, 2013. "The impact of government policy on preference for NEVs: The evidence from China," Energy Policy, Elsevier, vol. 61(C), pages 382-393.
    15. Joshua Lerner, 1994. "The Importance of Patent Scope: An Empirical Analysis," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 319-333, Summer.
    16. Chu, Wen-Lin & Wu, Feng-Shang & Kao, Kai-Sheng & Yen, David C., 2009. "Diffusion of mobile telephony: An empirical study in Taiwan," Telecommunications Policy, Elsevier, vol. 33(9), pages 506-520, October.
    17. Choi, Wonjae & Song, Han Ho, 2018. "Well-to-wheel greenhouse gas emissions of battery electric vehicles in countries dependent on the import of fuels through maritime transportation: A South Korean case study," Applied Energy, Elsevier, vol. 230(C), pages 135-147.
    18. Diamond, David, 2009. "The impact of government incentives for hybrid-electric vehicles: Evidence from US states," Energy Policy, Elsevier, vol. 37(3), pages 972-983, March.
    19. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    20. C. Gay & C. Le Bas & P. Patel & K. Touach, 2005. "The determinants of patent citations: an empirical analysis of French and British patents in the US," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(5), pages 339-350.
    21. Wesseling, J.H. & Faber, J. & Hekkert, M.P., 2014. "How competitive forces sustain electric vehicle development," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 154-164.
    22. Ozaki, Ritsuko & Sevastyanova, Katerina, 2011. "Going hybrid: An analysis of consumer purchase motivations," Energy Policy, Elsevier, vol. 39(5), pages 2217-2227, May.
    23. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    24. Reitzig, Markus, 2003. "What determines patent value?: Insights from the semiconductor industry," Research Policy, Elsevier, vol. 32(1), pages 13-26, January.
    25. Costantini, Valeria & Crespi, Francesco & Martini, Chiara & Pennacchio, Luca, 2015. "Demand-pull and technology-push public support for eco-innovation: The case of the biofuels sector," Research Policy, Elsevier, vol. 44(3), pages 577-595.
    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. Julia Mazzei & Tommaso Rughi & Maria Enrica Virgillito, 2023. "Knowing brown and inventing green? Incremental and radical innovative activities in the automotive sector," Industry and Innovation, Taylor & Francis Journals, vol. 30(7), pages 824-863, August.
    2. Li, Xiaotao & Yuan, Xiaodong, 2022. "Tracing the technology transfer of battery electric vehicles in China: A patent citation organization network analysis," Energy, Elsevier, vol. 239(PD).
    3. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    4. Sadik-Zada, Elkhan Richard & Gatto, Andrea & Scharfenstein, Manuel, 2023. "Sustainable management of lithium and green hydrogen and long-run perspectives of electromobility," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    5. Anqi Chen & Shibing You & Huan Liu & Jiaxuan Zhu & Xu Peng, 2023. "A Sustainable Road Transport Decarbonisation: The Scenario Analysis of New Energy Vehicle in China," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    6. Xiaodong Yuan & Weiling Song, 2022. "Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies," Information Technology and Management, Springer, vol. 23(2), pages 65-76, June.
    7. Anqi Chen & Shibing You, 2022. "The Fuel Cycle Carbon Reduction Effects of New Energy Vehicles: Empirical Evidence Based on Regional Data in China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    8. Ruifeng Hu & Weiqiao Xu, 2022. "Exploring the Technological Changes of Green Agriculture in China: Evidence from Patent Data (1998–2021)," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    9. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Peng Liu & Cheng Liu & Zhenpo Wang & Qiushi Wang & Jinlei Han & Yapeng Zhou, 2023. "A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    11. Natalia Wagner, 2023. "Inventive Activity for Climate Change Mitigation: An Insight into the Maritime Industry," Energies, MDPI, vol. 16(21), pages 1-23, November.
    12. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    13. Fanyu Pu & Songyan Jiang & Ling Zhang, 2023. "Future scenarios of China’s electric vehicle ownership: A modeling study based on system dynamic approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10017-10028, September.
    14. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    15. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    16. Bonnin Roca, Jaime, 2022. "Teaching technological forecasting to undergraduate students: a reflection on challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    17. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).
    18. Park, Changeun & Lim, Sesil & Shin, Jungwoo & Lee, Chul-Yong, 2022. "How much hydrogen should be supplied in the transportation market? Focusing on hydrogen fuel cell vehicle demand in South Korea," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    19. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

    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. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    2. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    4. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    5. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    6. Kim, Juram & Hong, Suckwon & Kang, Yubin & Lee, Changyong, 2023. "Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis," Technovation, Elsevier, vol. 122(C).
    7. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Wang, Xiaoli & Daim, Tugrul & Huang, Lucheng & Li, Zhiqiang & Shaikh, Ruqia & Kassi, Diby Francois, 2022. "Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology," Technology in Society, Elsevier, vol. 71(C).
    9. Grimaldi, Michele & Cricelli, Livio & Di Giovanni, Martina & Rogo, Francesco, 2015. "The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 286-302.
    10. Kim, Juram & Lee, Gyumin & Lee, Seungbin & Lee, Changyong, 2022. "Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    12. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    13. Ruyu Xie & Liren An & Nosheena Yasir, 2022. "How Innovative Characteristics Influence Consumers’ Intention to Purchase Electric Vehicle: A Moderating Role of Lifestyle," Sustainability, MDPI, vol. 14(8), pages 1-24, April.
    14. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    15. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    16. Shanyong Wang & Jin Fan & Dingtao Zhao & Shu Yang & Yuanguang Fu, 2016. "Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model," Transportation, Springer, vol. 43(1), pages 123-143, January.
    17. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).
    18. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    19. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    20. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

    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:tefoso:v:166:y:2021:i:c:s0040162521000834. 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.sciencedirect.com/science/journal/00401625 .

    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.