IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v186y2023ipas0040162522006400.html
   My bibliography  Save this article

Consumers' attitudes toward low-carbon consumption based on a computational model: Evidence from China

Author

Listed:
  • Wu, Zhonghuan
  • Duan, Chunlin
  • Cui, Yuting
  • Qin, Rong

Abstract

The COP26 conference emphasized the “shift from commitment to action”, and the Chinese government has actively issued a series of policies related to the low-carbon economy, which have impacted Chinese consumers' attitudes toward low-carbon consumption. However, one of the current challenges is the perspectives used to understand attitudes toward low carbon consumption vary, and the research methodology relies heavily on traditional methods with a time lag. This paper presents a computational model that combines Sentiment Classification (SC), the Latent Dirichlet Allocation (LDA) model, and VOSviewer clustering to show persuasive topics from consumers. We illustrate the validity of the model and apply it to new energy vehicle posts from social media platforms (e.g., Zhihu) between January 2022 and March 2022. Finally, we observe 14 clusters with sentiment and find that consumers' attitudes are polarized, positive stakeholders consider the long-term development and macroscopic aspects, while negative stakeholders believe that there are real problems with new energy vehicles, especially batteries and charging, and then we provide some suggestions to the government, manufacturers, and investors.

Suggested Citation

  • Wu, Zhonghuan & Duan, Chunlin & Cui, Yuting & Qin, Rong, 2023. "Consumers' attitudes toward low-carbon consumption based on a computational model: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  • Handle: RePEc:eee:tefoso:v:186:y:2023:i:pa:s0040162522006400
    DOI: 10.1016/j.techfore.2022.122119
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522006400
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2022.122119?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. Dmitry Zhukov & Tatiana Khvatova & Carla Millar & Anastasia Zaltcman, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Post-Print hal-03188186, HAL.
    2. Reuter, Christian & Kaufhold, Marc-André & Schmid, Stefka & Spielhofer, Thomas & Hahne, Anna Sophie, 2019. "The impact of risk cultures: Citizens' perception of social media use in emergencies across Europe," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    3. Gozgor, Giray & Tiwari, Aviral Kumar & Khraief, Naceur & Shahbaz, Muhammad, 2019. "Dependence structure between business cycles and CO2 emissions in the U.S.: Evidence from the time-varying Markov-Switching Copula models," Energy, Elsevier, vol. 188(C).
    4. Dogan, Eyup & Chishti, Muhammad Zubair & Karimi Alavijeh, Nooshin & Tzeremes, Panayiotis, 2022. "The roles of technology and Kyoto Protocol in energy transition towards COP26 targets: Evidence from the novel GMM-PVAR approach for G-7 countries," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Zhang, Jingfei & Zheng, Zhicheng & Zhang, Lijun & Qin, Yaochen & Wang, Jingfan & Cui, Panpan, 2021. "Digital consumption innovation, socio-economic factors and low-carbon consumption: Empirical analysis based on China," Technology in Society, Elsevier, vol. 67(C).
    6. Yan Liu & Rong Liu & Xin Jiang, 2019. "What drives low-carbon consumption behavior of Chinese college students? The regulation of situational factors," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 173-191, January.
    7. Wu, Zhanglan & Shao, Qinglong & Su, Yantao & Zhang, Dan, 2021. "A socio-technical transition path for new energy vehicles in China: A multi-level perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    8. Yang, Xin & Zhou, Xiaohe & Deng, Xiangzheng, 2022. "Modeling farmers’ adoption of low-carbon agricultural technology in Jianghan Plain, China: An examination of the theory of planned behavior," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    9. Dianshu, Feng & Sovacool, Benjamin K. & Minh Vu, Khuong, 2010. "The barriers to energy efficiency in China: Assessing household electricity savings and consumer behavior in Liaoning Province," Energy Policy, Elsevier, vol. 38(2), pages 1202-1209, February.
    10. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Andrianova, Elena, 2022. "Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Chen, Hong & Long, Ruyin & Niu, Wenjing & Feng, Qun & Yang, Ranran, 2014. "How does individual low-carbon consumption behavior occur? – An analysis based on attitude process," Applied Energy, Elsevier, vol. 116(C), pages 376-386.
    12. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    13. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    14. Csikszentmihalyi, Mihaly, 2000. "The Costs and Benefits of Consuming," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(2), pages 267-272, September.
    15. Changyou Zhang & Wenyu Zhang & Weina Luo & Xue Gao & Bingchen Zhang, 2021. "Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition," Energies, MDPI, vol. 14(18), pages 1-23, September.
    16. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    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. Yuru Liu & Yan Wan, 2023. "Consumer Satisfaction with the Online Dispute Resolution on a Second-Hand Goods-Trading Platform," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    2. Ho, Kung-Cheng & Shen, Xixi & Yan, Cheng & Hu, Xiang, 2023. "Influence of green innovation on disclosure quality: Mediating role of media attention," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Amin, Nabila & Shabbir, Muhammad Salman & Song, Huaming & Farrukh, Muhammad Umar & Iqbal, Shahid & Abbass, Kashif, 2023. "A step towards environmental mitigation: Do green technological innovation and institutional quality make a difference?," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    4. Yang, Zaoli & Li, Qin & Charles, Vincent & Xu, Bing & Gupta, Shivam, 2023. "Supporting personalized new energy vehicle purchase decision-making: Customer reviews and product recommendation platform," International Journal of Production Economics, Elsevier, vol. 265(C).
    5. Wang, Zongrun & Fu, Haiqin & Ren, Xiaohang, 2023. "Political connections and corporate carbon emission: New evidence from Chinese industrial firms," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Liang, Chao & Wang, Qi, 2023. "The relationship between total factor productivity and environmental quality: A sustainable future with innovation input," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    7. Wang, Jie & He, Ya-qun & Wang, Heng-guang & Wu, Ru-fei, 2023. "Low-carbon promotion of new energy vehicles: A quadrilateral evolutionary game," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

    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. Zhao, Jianyu & Yu, Lean & Xi, Xi & Li, Shengliang, 2023. "Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy," Omega, Elsevier, vol. 120(C).
    2. Song, Yuegang & Zhang, Bicheng & Wang, Jianhua & Kwek, Keh, 2022. "The impact of climate change on China's agricultural green total factor productivity," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    3. Yuhuan Xia & Yubo Liu & Changlin Han & Yang Gao & Yuanyuan Lan, 2022. "How Does Environmentally Specific Servant Leadership Fuel Employees’ Low-Carbon Behavior? The Role of Environmental Self-Accountability and Power Distance Orientation," IJERPH, MDPI, vol. 19(5), pages 1-17, March.
    4. Lauma Balode & Kristiāna Dolge & Dagnija Blumberga, 2023. "Sector-Specific Pathways to Sustainability: Unravelling the Most Promising Renewable Energy Options," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    5. Wang, Zongrun & Fu, Haiqin & Ren, Xiaohang, 2023. "Political connections and corporate carbon emission: New evidence from Chinese industrial firms," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    6. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Andrianova, Elena, 2022. "Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Zhao, Yuntong & Jian, Zhaoquan & Du, Yushen, 2024. "How can China's subsidy promote the transition to electric vehicles?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Song, Yanwu & Wang, Can & Wang, Zhaohua, 2023. "Climate risk, institutional quality, and total factor productivity," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    9. Xin Ma & Hong Jiang & Lijuan Tong & Jingyi Zhang & Mengyuan Dong, 2023. "Sustainability of the New Energy Automobile Industry: Examining the Relationship among Government Subsidies, R&D Intensity, and Innovation Performance," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    10. Yanbo Wang & Boyao Zhi & Shumin Xiang & Guangxin Ren & Yongzhong Feng & Gaihe Yang & Xiaojiao Wang, 2023. "China’s Biogas Industry’s Sustainable Transition to a Low-Carbon Plan—A Socio-Technical Perspective," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    11. Dmitry Zhukov & Julia Perova & Vladimir Kalinin, 2022. "Description of the Distribution Law and Non-Linear Dynamics of Growth of Comments Number in News and Blogs Based on the Fokker-Planck Equation," Mathematics, MDPI, vol. 10(6), pages 1-24, March.
    12. Man Yang & Tao Zhang, 2023. "Demand forecasting and information sharing of a green supply chain considering data company," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-28, July.
    13. Wang, Erhong & Gozgor, Giray & Mahalik, Mantu Kumar & Patel, Gupteswar & Hu, Guoheng, 2022. "Effects of institutional quality and political risk on the renewable energy consumption in the OECD countries," Resources Policy, Elsevier, vol. 79(C).
    14. Liang, Chao & Wang, Qi, 2023. "The relationship between total factor productivity and environmental quality: A sustainable future with innovation input," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    15. Kar, Sumi & Basu, Kajla & Sarkar, Biswajit, 2023. "Advertisement policy for dual-channel within emissions-controlled flexible production system," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    16. Huang, Haiping & Huang, Baolian & Sun, Aijun, 2023. "How do mineral resources influence eco-sustainability in China? Dynamic role of renewable energy and green finance," Resources Policy, Elsevier, vol. 85(PA).
    17. Fan, Fei & Dai, Shangze & Yang, Bo & Ke, Haiqian, 2023. "Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities," Technology in Society, Elsevier, vol. 72(C).
    18. Suresh Malodia & Alka Singh Bhatt, 2019. "Why Should I Switch Off: Understanding the Barriers to Sustainable Consumption?," Vision, , vol. 23(2), pages 134-143, June.
    19. Peng, Yue & Wang, Wei & Zhen, Shangsong & Liu, Yunqiang, 2024. "Does digitalization help green consumption? Empirical test based on the perspective of supply and demand of green products," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    20. Luigi Fortuna & Arturo Buscarino, 2022. "Sustainable Energy Systems," Energies, MDPI, vol. 15(23), pages 1-7, December.

    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:186:y:2023:i:pa:s0040162522006400. 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.