How do semiconductors, artificial intelligence, geopolitical risk, and their moderating effects shape renewable energy production in leading semiconductor manufacturing countries?

被引:2
作者
Rasheed, Muhammad Qamar [1 ]
Zhao, Yuhuan [1 ]
Nazir, Marina [1 ]
Ahmed, Zahoor [2 ,3 ,4 ,5 ]
Yu, Xiaohong [1 ]
机构
[1] Beijing Inst Technol, Sch Econ, Beijing 100081, Peoples R China
[2] Bahcesehir Cyprus Univ, Fac Econ Adm & Social Sci, Dept Business Adm, Nicosia, Turkiye
[3] Tashkent State Univ Econ, Dept Green Econ, Tashkent, Uzbekistan
[4] Korea Univ, Business Sch, Seoul 02841, South Korea
[5] Azerbaijan State Univ Econ UNEC, UNEC Res Methods Applicat Ctr, Istiqlaliyyat Str 6, Baku 1001, Azerbaijan
关键词
Semiconductors; Artificial intelligence; Geopolitical risk; Renewable energy production; TESTING SLOPE HOMOGENEITY;
D O I
10.1016/j.techsoc.2024.102761
中图分类号
D58 [社会生活与社会问题]; C913 [社会生活与社会问题];
学科分类号
摘要
Semiconductors, artificial intelligence (AI), and geopolitics may influence the future of environmentally friendly energy. This research aims to offer a novel perspective within this domain by assessing the interconnections between semiconductors, AI, geopolitical risk, and renewable energy production. The study analyzed panel data and cross-country statistics from 1999 to 2019 for 13 leading semiconductor manufacturing countries. According to the findings of the panel Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG), the Feasible Generalized Least Squares (FGLS), and the Panel-Corrected Standard Error (PCSE) methods semiconductors and AI have a significant long-term impact on accelerating renewable energy production. However, renewable energy production experiences substantial disruptions resulting from geopolitical risk. Apart from this, the combined effect of geopolitical risk and semiconductors decreases the strength of the advantageous interaction between semiconductors and renewable energy as compared to the direct influence of semiconductors. Likewise, the moderating effect of geopolitical risk and AI decreases the beneficial intensity between AI and renewable energy production as compared to the direct impact of AI. Finally, these statistical insights serve as an essential foundation and benchmark for policymakers seeking to align their strategies with renewable energy production goals by addressing the role of semiconductors, AI, geopolitical risks, and their combined impact.
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页数:14
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共 94 条
  • [1] Price response of top-five renewable energy firms to Russia-Ukraine conflict: An advanced quantile analysis to achieve net-zero in United States of America
    Abbas, Shujaat
    Saha, Tanaya
    Sinha, Avik
    [J]. JOURNAL OF CLEANER PRODUCTION, 2024, 442
  • [2] Experimental development of a biological photovoltaic cell (BPV) for energy conversion and simultaneous CO2 capture by utilizing marine microalgae on copper mesh
    Abdulla, Shamma Alasad Al
    Al Hammadi, Khalid
    Al -Ali, Hamad
    Alami, Abdul Hai
    Abdelkareem, Mohammad Ali
    Olabi, Abdul Ghani
    [J]. RENEWABLE ENERGY, 2024, 223
  • [3] Aboagye A., 2022, World Economic Forum
  • [5] Empowering Asia's sustainable future: Unraveling renewable energy dynamics with trade, carbon emission, governance, and innovative interactions
    Ahmad, Maaz
    Jan, Dil
    Ali, Sher
    Khan, Usman Ullah
    [J]. RENEWABLE ENERGY, 2024, 229
  • [6] Fintech, natural resources management, green energy transition, and ecological footprint: Empirical insights from EU countries
    Ahmad, Mahmood
    Pata, Ugur Korkut
    Ahmed, Zahoor
    Zhao, Ruiqi
    [J]. RESOURCES POLICY, 2024, 92
  • [7] Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter?
    Ahmad, Mahmood
    Ahmed, Zahoor
    Alvarado, Rafael
    Hussain, Nazim
    Khan, Sana Akbar
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 351
  • [8] A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation
    Akter, Afifa
    Zafir, Ehsanul Islam
    Dana, Nazia Hasan
    Oysoyal, Rahul J.
    Sarker, Subrata K.
    Li, Li
    Muyeen, S. M.
    Das, Sajal K.
    Kamwa, Innocent
    [J]. ENERGY STRATEGY REVIEWS, 2024, 51
  • [9] Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
    Allal, Zaid
    Noura, Hassan N.
    Salman, Ola
    Chahine, Khaled
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 354
  • [10] The impact of financial development and geopolitical risk on renewable energy consumption: evidence from emerging markets
    Alsagr, Naif
    van Hemmen, Stefan
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (20) : 25906 - 25919