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How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers

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  • Xu, Yingying
  • Shao, Xuefeng
  • Tanasescu, Cristina

Abstract

The dual role of artificial intelligence (AI) in carbon emissions has come under scrutiny. The feedback mechanism in the “AI-Carbon-Energy” system contains the enlightenment of coordinated development of environment and economy. Based on the dynamic connectedness index and network diagrams, we quantify how the AI industry is connected to the carbon market and the energy sector in the short-term and long-term. Our empirical findings suggest that the information spillover within the system changes over time and across frequency bands. The long-term component drives the overall information spillover. Both the carbon market and the energy sector are closely connected with the AI industry. Specifically, AI industry trading volume is a main information transmitter. Since the release of GPT-4, however, investor attention to the AI industry becomes more important. The carbon market receives a lot of information from the AI industry trading volume and investor attention to the AI industry, particularly since 2023. Nevertheless, the energy sector is only weakly connected to the other two markets. These findings have important implications for policy makers, investors, and producers.

Suggested Citation

  • Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001853
    DOI: 10.1016/j.eneco.2024.107477
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    References listed on IDEAS

    as
    1. Hanif, Waqas & Arreola Hernandez, Jose & Mensi, Walid & Kang, Sang Hoon & Uddin, Gazi Salah & Yoon, Seong-Min, 2021. "Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices," Energy Economics, Elsevier, vol. 101(C).
    2. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    3. Wei, Ping & Qi, Yinshu & Ren, Xiaohang & Gozgor, Giray, 2023. "The role of the COVID-19 pandemic in time-frequency connectedness between oil market shocks and green bond markets: Evidence from the wavelet-based quantile approaches," Energy Economics, Elsevier, vol. 121(C).
    4. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    5. Qin, Meng & Zhang, Xiaojing & Li, Yameng & Badarcea, Roxana Maria, 2023. "Blockchain market and green finance: The enablers of carbon neutrality in China," Energy Economics, Elsevier, vol. 118(C).
    6. Emilie Alberola & Julien Chevallier, 2009. "European Carbon Prices and Banking Restrictions: Evidence from Phase I (2005-2007)," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-80.
    7. Semeyutin, Artur & Gozgor, Giray & Lau, Chi Keung Marco & Xu, Bing, 2021. "Effects of idiosyncratic jumps and co-jumps on oil, gold, and copper markets," Energy Economics, Elsevier, vol. 104(C).
    8. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    9. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.
    10. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    11. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    12. Lin, Boqiang & Chen, Yufang, 2019. "Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: A case of Beijing CET market in China," Energy, Elsevier, vol. 172(C), pages 1198-1210.
    13. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    14. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    15. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan & Plakandaras, Vasilios, 2018. "Dynamic connectedness of uncertainty across developed economies: A time-varying approach," Economics Letters, Elsevier, vol. 166(C), pages 63-75.
    16. Xu, Yingying & Lien, Donald, 2024. "Together in bad times? The effect of COVID-19 on inflation spillovers in China," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 316-331.
    17. Xuewu (Wesley) Wang, 2017. "Investor Attention Strategy," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(4), pages 390-399, October.
    18. Yang, Zikun & Zhang, Mingming & Liu, Liyun & Zhou, Dequn, 2022. "Can renewable energy investment reduce carbon dioxide emissions? Evidence from scale and structure," Energy Economics, Elsevier, vol. 112(C).
    19. Gabauer, David & Gupta, Rangan, 2018. "On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach," Economics Letters, Elsevier, vol. 171(C), pages 63-71.
    20. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    21. Kow, Ken Weng & Wong, Yee Wan & Rajkumar, Rajparthiban Kumar & Rajkumar, Rajprasad Kumar, 2016. "A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 334-346.
    22. Dong, Kangyin & Ni, Guohua & Taghizadeh-Hesary, Farhad & Zhao, Congyu, 2023. "Does smart transportation matter in inhibiting carbon inequality?," Energy Economics, Elsevier, vol. 126(C).
    23. repec:dau:papers:123456789/5269 is not listed on IDEAS
    24. Tanattrin Bunnag, 2015. "Volatility Transmission in Oil Futures Markets and Carbon Emissions Futures," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 647-659.
    25. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    26. Chatziantoniou, Ioannis & Gabauer, David & Gupta, Rangan, 2023. "Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach," Resources Policy, Elsevier, vol. 84(C).
    27. Su, Chi-Wei & Yang, Shengjie & Qin, Meng & Lobonţ, Oana-Ramona, 2023. "Gold vs bitcoin: Who can resist panic in the U.S.?," Resources Policy, Elsevier, vol. 85(PA).
    28. Su, Chi Wei & Shao, Xuefeng & Jia, Zhijie & Nepal, Rabindra & Umar, Muhammad & Qin, Meng, 2023. "The rise of green energy metal: Could lithium threaten the status of oil?," Energy Economics, Elsevier, vol. 121(C).
    29. repec:dau:papers:123456789/6790 is not listed on IDEAS
    30. Xu, Yingying, 2021. "Risk spillover from energy market uncertainties to the Chinese carbon market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    31. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    32. Dan Yang & Tingyu Ma & Yuetang Wang & Guojun Wang, 2021. "Does Investor Attention Affect Stock Trading and Returns? Evidence from Publicly Listed Firms in China," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 368-381, October.
    33. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    34. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
    35. Pan, Yinghao & Zhang, Chao-Chao & Lee, Chien-Chiang & Lv, Suxiang, 2024. "Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms," Energy Economics, Elsevier, vol. 130(C).
    36. Wei, Taoyuan & Liu, Yang, 2017. "Estimation of global rebound effect caused by energy efficiency improvement," Energy Economics, Elsevier, vol. 66(C), pages 27-34.
    37. Qin, Meng & Wu, Tong & Ma, Xuecheng & Albu, Lucian Liviu & Umar, Muhammad, 2023. "Are energy consumption and carbon emission caused by Bitcoin? A novel time-varying technique," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 109-120.
    38. Xu, Yingying & Lien, Donald, 2022. "Which affects stock performances more, words or deeds of the key person?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    39. Wang, Xiaoqing & Qin, Chuan & Liu, Yufeng & Tanasescu, Cristina & Bao, Jiangnan, 2023. "Emerging enablers of green low-carbon development: Do digital economy and open innovation matter?," Energy Economics, Elsevier, vol. 127(PA).
    40. Zheng, Yan & Yin, Hua & Zhou, Min & Liu, Wenhua & Wen, Fenghua, 2021. "Impacts of oil shocks on the EU carbon emissions allowances under different market conditions," Energy Economics, Elsevier, vol. 104(C).
    41. Srivastava, Praveen Ranjan & Mangla, Sachin Kumar & Eachempati, Prajwal & Tiwari, Aviral Kumar, 2023. "An explainable artificial intelligence approach to understanding drivers of economic energy consumption and sustainability," Energy Economics, Elsevier, vol. 125(C).
    42. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 2207, Koc University-TUSIAD Economic Research Forum.
    43. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
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