IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v134y2024ics0140988324003244.html
   My bibliography  Save this article

Are artificial intelligence and blockchain the key to unlocking the box of clean energy?

Author

Listed:
  • Yang, Shengyao
  • Zhu, Meng Nan
  • Yu, Haiyan

Abstract

The fourth industrial revolution has brought about key technological breakthroughs represented by artificial intelligence (AI) and blockchain (BC), which provide an opportunity for the development of clean energy (CE). We employ a wavelet-based quantile-on-quantile approach to investigate the correlation among AI, BC and CE over different periods and time horizons. The results show that although the integration of AI and CE systems requires a lot of time, it can significantly boost CE development in the long run. However, BC cannot significantly promote CE development except for a few extreme periods. Our results are supported by the mechanism of interaction among AI, BC and CE development, which highlights their relationship. Therefore, the government should focus on promoting the development of new information technologies and their integration with the CE system, which is crucial for the implementation of energy transition strategies. In addition, low-carbon investors should focus on these technologies because of their relationship with CE.

Suggested Citation

  • Yang, Shengyao & Zhu, Meng Nan & Yu, Haiyan, 2024. "Are artificial intelligence and blockchain the key to unlocking the box of clean energy?," Energy Economics, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324003244
    DOI: 10.1016/j.eneco.2024.107616
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324003244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107616?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. Divya R. Nair & Manjula G. Nair & Tripta Thakur, 2022. "A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement," Energies, MDPI, vol. 15(15), pages 1-20, July.
    2. Jiang, Shangrong & Li, Yuze & Wang, Shouyang & Zhao, Lin, 2022. "Blockchain competition: The tradeoff between platform stability and efficiency," European Journal of Operational Research, Elsevier, vol. 296(3), pages 1084-1097.
    3. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    4. Erdogan, Sinan & Pata, Ugur Korkut & Solarin, Sakiru Adebola, 2023. "Towards carbon-neutral world: The effect of renewable energy investments and technologies in G7 countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    5. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    6. Pang, Lidong & Zhu, Meng Nan & Yu, Haiyan, 2022. "Is green finance really a blessing for green technology and carbon efficiency?," Energy Economics, Elsevier, vol. 114(C).
    7. Su, Chi-Wei & Pang, Li-Dong & Tao, Ran & Shao, Xuefeng & Umar, Muhammad, 2022. "Renewable energy and technological innovation: Which one is the winner in promoting net-zero emissions?," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    9. Jiang, Peng & Fan, Yee Van & Klemeš, Jiří Jaromír, 2021. "Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities," Applied Energy, Elsevier, vol. 285(C).
    10. Shahbaz, Muhammad & Siddiqui, Aaliyah & Ahmad, Shabbir & Jiao, Zhilun, 2023. "Financial development as a new determinant of energy diversification: The role of natural capital and structural changes in Australia," Energy Economics, Elsevier, vol. 126(C).
    11. Gozgor, Giray & Paramati, Sudharshan Reddy, 2022. "Does energy diversification cause an economic slowdown? Evidence from a newly constructed energy diversification index," Energy Economics, Elsevier, vol. 109(C).
    12. Christian Urom & Gideon Ndubuisi & Khaled Guesmi & Ramzi Benkraien, 2022. "Quantile co-movement and dependence between energy-focused sectors and artificial intelligence," Post-Print hal-03783409, HAL.
    13. Su, Chi Wei & Song, Xin Yue & Qin, Meng & Lobonţ, Oana-Ramona, 2024. "Is copper a safe haven for oil?," Resources Policy, Elsevier, vol. 91(C).
    14. Qin, Meng & Zhu, Yujie & Xie, Xin & Shao, Xuefeng & Lobonţ, Oana-Ramona, 2024. "The impact of climate risk on technological progress under the fourth industrial era," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    15. Qi, Shaozhou & Pang, Lidong & Qi, Tianbai & Zhang, Xiaoling & Pirtea, Marilen Gabriel, 2024. "The correlation between the green bond market and carbon trading markets under climate change: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    16. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled & Benkraien, Ramzi, 2022. "Quantile co-movement and dependence between energy-focused sectors and artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    17. Umar, Muhammad & Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Shao, Xue-Feng, 2021. "Bitcoin: A safe haven asset and a winner amid political and economic uncertainties in the US?," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. Dawar, Ishaan & Dutta, Anupam & Bouri, Elie & Saeed, Tareq, 2021. "Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression," Renewable Energy, Elsevier, vol. 163(C), pages 288-299.
    19. Gregor Dorfleitner & Diana Braun, 2019. "Fintech, Digitalization and Blockchain: Possible Applications for Green Finance," Palgrave Studies in Impact Finance, in: Marco Migliorelli & Philippe Dessertine (ed.), The Rise of Green Finance in Europe, chapter 0, pages 207-237, Palgrave Macmillan.
    20. Lund, Henrik & Thellufsen, Jakob Zinck & Sorknæs, Peter & Mathiesen, Brian Vad & Chang, Miguel & Madsen, Poul Thøis & Kany, Mikkel Strunge & Skov, Iva Ridjan, 2022. "Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    21. Abakah, Emmanuel Joel Aikins & Adeabah, David & Tiwari, Aviral Kumar & Abdullah, Mohammad, 2023. "Effect of Russia–Ukraine war sentiment on blockchain and FinTech stocks," International Review of Financial Analysis, Elsevier, vol. 90(C).
    22. Lau, Chi Keung & Gozgor, Giray & Mahalik, Mantu Kumar & Patel, Gupteswar & Li, Jing, 2023. "Introducing a new measure of energy transition: Green quality of energy mix and its impact on CO2 emissions," Energy Economics, Elsevier, vol. 122(C).
    23. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    24. Jun-Ho Huh & Seong-Kyu Kim, 2019. "The Blockchain Consensus Algorithm for Viable Management of New and Renewable Energies," Sustainability, MDPI, vol. 11(11), pages 1-26, June.
    25. Zhang, Hongwei & Fang, Beixin & He, Pengwei & Gao, Wang, 2024. "The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19," Energy, Elsevier, vol. 291(C).
    26. Yildizbasi, Abdullah, 2021. "Blockchain and renewable energy: Integration challenges in circular economy era," Renewable Energy, Elsevier, vol. 176(C), pages 183-197.
    27. Mazzeo, Domenico & Herdem, Münür Sacit & Matera, Nicoletta & Bonini, Matteo & Wen, John Z. & Nathwani, Jatin & Oliveti, Giuseppe, 2021. "Artificial intelligence application for the performance prediction of a clean energy community," Energy, Elsevier, vol. 232(C).
    28. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
    29. Chishti, Muhammad Zubair & Sinha, Avik & Zaman, Umer & Shahzad, Umer, 2023. "Exploring the dynamic connectedness among energy transition and its drivers: Understanding the moderating role of global geopolitical risk," Energy Economics, Elsevier, vol. 119(C).
    30. Huru Hasanova & Ui‐jun Baek & Mu‐gon Shin & Kyunghee Cho & Myung‐Sup Kim, 2019. "A survey on blockchain cybersecurity vulnerabilities and possible countermeasures," International Journal of Network Management, John Wiley & Sons, vol. 29(2), March.
    31. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    32. Chi Wei Su & Xiaofeng Liu & Meng Qin & Muhammad Umar, 2024. "Is the Uncertainty Economic Policy an Impediment or an Impetus to Technological Innovation?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(11), pages 2579-2593, September.
    33. Sinha, Avik & Bekiros, Stelios & Hussain, Nazim & Nguyen, Duc Khuong & Khan, Sana Akbar, 2023. "How social imbalance and governance quality shape policy directives for energy transition in the OECD countries?," Energy Economics, Elsevier, vol. 120(C).
    34. Qin, Meng & Mirza, Nawazish & Su, Chi-Wei & Umar, Muhammad, 2023. "Exploring Bubbles in the Digital Economy: The Case of China," Global Finance Journal, Elsevier, vol. 57(C).
    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. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    2. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    3. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    4. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).
    5. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    6. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    7. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    8. Ye, Tuo & Zhao, Songyu & Lau, Chi Keung Marco & Chau, Frankie, 2024. "Social media sentiment of hydrogen fuel cell vehicles in China: Evidence from artificial intelligence algorithms," Energy Economics, Elsevier, vol. 133(C).
    9. Zhong, Yufei & Chen, Xuesheng & Wang, Zhixian & Lin, Regina Fang-Ying, 2024. "The nexus among artificial intelligence, supply chain and energy sustainability: A time-varying analysis," Energy Economics, Elsevier, vol. 132(C).
    10. Chishti, Muhammad Zubair & Xia, Xiqiang & Dogan, Eyup, 2024. "Understanding the effects of artificial intelligence on energy transition: The moderating role of Paris Agreement," Energy Economics, Elsevier, vol. 131(C).
    11. Zhao, Qiuyun & Jiang, Mei & Zhao, Zuoxiang & Liu, Fan & Zhou, Li, 2024. "The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation," Energy Economics, Elsevier, vol. 133(C).
    12. Lee, Chi-Chuan & Fang, Yuzhu & Quan, Shiyun & Li, Xinghao, 2024. "Leveraging the power of artificial intelligence toward the energy transition: The key role of the digital economy," Energy Economics, Elsevier, vol. 135(C).
    13. Song, Malin & Pan, Heting & Shen, Zhiyang & Tamayo-Verleene, Kristine, 2024. "Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value," Energy Economics, Elsevier, vol. 131(C).
    14. Su, Chi Wei & Wei, Shenkai & Wang, Yan & Tao, Ran, 2024. "How does climate policy uncertainty affect the carbon market?," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    15. Wu, Jiawen & Li, Jing-Ping & Su, Chi-Wei, 2024. "Can green bond hedges climate policy uncertainty in the United States: New insights from novel time-varying causality and quantile-on-quantile methods?," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 1158-1176.
    16. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Ma, Xin, 2024. "The butterfly effect of cloud computing on the low-carbon economy," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    17. Su, Chi-Wei & Yang, Shengyao & Ren, Yi-Shuai, 2024. "Economic policy uncertainty and company stock prices: Empirical evidence from blockchain companies," Finance Research Letters, Elsevier, vol. 65(C).
    18. Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).
    19. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.
    20. Cao, Fangzhi & Su, Chi-Wei & Sun, Dian & Qin, Meng & Umar, Muhammad, 2024. "U.S. monetary policy: The pushing hands of crude oil price?," Energy Economics, Elsevier, vol. 134(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:eneeco:v:134:y:2024:i:c:s0140988324003244. 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/locate/eneco .

    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.