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AI and Nuclear: A perfect intersection of danger and potential?

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  • Chen, Yan
  • Zhang, Ruiqian
  • Lyu, Jiayi
  • Hou, Yuqi

Abstract

This paper explores the intersection between artificial intelligence and nuclear energy, shedding light on the intriguing scenario when these two sectors jointly consturct. Through the application of both full-sample and sub-sample methodologies, this study identifies the time-dependent interrelationships between China's artificial intelligence index (AI) and nuclear energy indicator (NUC). The quantitative analysis presents that AI's influence on nuclear energy is twofold. On one hand, AI contributes positively by acting as a catalyst and enhancing safety measures in the nuclear sector. On the other, the impact might be perceived negatively, primarily when cost-effective alternative energy sources overshadow the benefits of nuclear energy. Additionally, the positive effect of NUC on AI highlights the benefits derived from nuclear's expansive and consistent energy output, catering efficiently to AI's substantial energy demands. In essence, AI and NUC are found to be complementary, with each having the potential to propel the other forward. This reciprocity paves the way for a synergistic relationship, promising mutual benefits. The study introduces a fresh perspective on the co-evolution of energy and technology, offering thought-provoking recommendations aimed at cultivating the collaborative growth of AI and NUC towards a common good.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:eneeco:v:133:y:2024:i:c:s0140988324002147
    DOI: 10.1016/j.eneco.2024.107506
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    as
    1. Balmer, Roberto E. & Levin, Stanford L. & Schmidt, Stephen, 2020. "Artificial Intelligence Applications in Telecommunications and other network industries," Telecommunications Policy, Elsevier, vol. 44(6).
    2. Anne-Laure Ligozat & Julien Lefevre & Aurélie Bugeau & Jacques Combaz, 2022. "Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    3. Azam, Anam & Rafiq, Muhammad & Shafique, Muhammad & Zhang, Haonan & Yuan, Jiahai, 2021. "Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis," Energy, Elsevier, vol. 219(C).
    4. Lu, Zhou & Gozgor, Giray & Mahalik, Mantu Kumar & Padhan, Hemachandra & Yan, Cheng, 2022. "Welfare gains from international trade and renewable energy demand: Evidence from the OECD countries," Energy Economics, Elsevier, vol. 112(C).
    5. Ren, Xiaohang & Zhong, Yan & Cheng, Xu & Yan, Cheng & Gozgor, Giray, 2023. "Does carbon price uncertainty affect stock price crash risk? Evidence from China," Energy Economics, Elsevier, vol. 122(C).
    6. Urpelainen, Johannes, 2011. "Export orientation and domestic electricity generation: Effects on energy efficiency innovation in select sectors," Energy Policy, Elsevier, vol. 39(9), pages 5638-5646, September.
    7. Wuqing Du & Wei You & Zhenqing Xu, 2022. "Review and Prospect of Legal Development in Commercial Nuclear Energy," Energies, MDPI, vol. 15(12), pages 1-16, June.
    8. Toda, Hiro Y. & Phillips, Peter C. B., 1993. "The spurious effect of unit roots on vector autoregressions : An analytical study," Journal of Econometrics, Elsevier, vol. 59(3), pages 229-255, October.
    9. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    10. Su, Chi-Wei & Qin, Meng & Tao, Ran & Shao, Xue-Feng & Albu, Lucian Liviu & Umar, Muhammad, 2020. "Can Bitcoin hedge the risks of geopolitical events?," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    11. 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).
    12. 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).
    13. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    14. Sjödin, David & Parida, Vinit & Palmié, Maximilian & Wincent, Joakim, 2021. "How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops," Journal of Business Research, Elsevier, vol. 134(C), pages 574-587.
    15. Alvin Camba & Janica Magat, 2021. "How Do Investors Respond To Territorial Disputes? Evidence From The South China Sea And Implications On Philippines Economic Strategy," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 66(01), pages 243-267, March.
    16. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    17. Li, Qiangyi & Zeng, Fu'e & Liu, Shaohui & Yang, Mian & Xu, Fei, 2021. "The effects of China's sustainable development policy for resource-based cities on local industrial transformation," Resources Policy, Elsevier, vol. 71(C).
    18. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
    19. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    20. Guo, Di & Guo, Yan & Jiang, Kun, 2016. "Government-subsidized R&D and firm innovation: Evidence from China," Research Policy, Elsevier, vol. 45(6), pages 1129-1144.
    21. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(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. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    24. Wang, Fan & Gu, Jibao & Wu, Jianlin, 2020. "Perspective taking, energy policy involvement, and public acceptance of nuclear energy: Evidence from China," Energy Policy, Elsevier, vol. 145(C).
    25. Hansen, Bruce E, 2002. "Tests for Parameter Instability in Regressions with I(1) Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 45-59, January.
    26. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    27. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    28. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
    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. Manuel Woschank & Erwin Rauch & Helmut Zsifkovits, 2020. "A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics," Sustainability, MDPI, vol. 12(9), pages 1-23, May.
    31. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    32. Jordan Bernhardt & Lauren Sukin, 2021. "Joint Military Exercises and Crisis Dynamics on the Korean Peninsula," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(5), pages 855-888, May.
    33. Ghazi Shukur & Panagiotis Mantalos, 2000. "A simple investigation of the Granger-causality test in integrated-cointegrated VAR systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(8), pages 1021-1031.
    34. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    35. Hu, Yucai & Ren, Shenggang & Wang, Yangjie & Chen, Xiaohong, 2020. "Can carbon emission trading scheme achieve energy conservation and emission reduction? Evidence from the industrial sector in China," Energy Economics, Elsevier, vol. 85(C).
    36. Jie Yang & Jie Wang & Xiaofeng Zhang & Chunqi Shen & Zhijuan Shao, 2022. "How Social Impressions Affect Public Acceptance of Nuclear Energy: A Case Study in China," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    37. 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).
    38. Xiang, Pianpian & Jiang, Kejun & Wang, Jiachen & He, Chenmin & Chen, Sha & Jiang, Weiyi, 2024. "Evaluation of LCOH of conventional technology, energy storage coupled solar PV electrolysis, and HTGR in China," Applied Energy, Elsevier, vol. 353(PA).
    39. 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).
    40. Darren J. Lim & Victor A. Ferguson, 2022. "Informal economic sanctions: the political economy of Chinese coercion during the THAAD dispute," Review of International Political Economy, Taylor & Francis Journals, vol. 29(5), pages 1525-1548, September.
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