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Robots for sustainability: Evaluating ecological footprints in leading AI-driven industrial nations

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  • Liu, Lei
  • Rasool, Zeeshan
  • Ali, Sajid
  • Wang, Canghong
  • Nazar, Raima

Abstract

By automating tasks with precision and efficiency, industrial robots help minimize resource utilization and emissions, making them indispensable allies in our quest to minimize our ecological footprint. The core intention of the present article is to scrutinize the impact of industrial robots on the ecological footprint in ten leading industrial artificial intelligence nations (Singapore, South Korea, Japan, Germany, Sweden, Denmark, USA, China, France, and Italy) from 2007 to 2020. Prior investigations have chosen panel data methodologies to detect the association between industrial robots and ecological footprint. Nonetheless, these studies often overlooked the variations in this relationship across different countries. In contrast, this article choses the Quantile-on-Quantile approach to assess this relationship on a country-specific basis. This methodology offers a comprehensive global perspective while delivering tailored insights relevant to each nation. The findings suggest that industrial robots improve environmental quality by decreasing ecological footprint across different data quantiles in chosen nations. The findings also underline that the asymmetries between our variables differ from country to country. These revelations underscore the importance of policymakers carefully assessing and skillfully managing strategies related to both industrial robots and the ecological footprint.

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  • Liu, Lei & Rasool, Zeeshan & Ali, Sajid & Wang, Canghong & Nazar, Raima, 2024. "Robots for sustainability: Evaluating ecological footprints in leading AI-driven industrial nations," Technology in Society, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:teinso:v:76:y:2024:i:c:s0160791x24000083
    DOI: 10.1016/j.techsoc.2024.102460
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    as
    1. Sorrell, Steve & Dimitropoulos, John, 2008. "The rebound effect: Microeconomic definitions, limitations and extensions," Ecological Economics, Elsevier, vol. 65(3), pages 636-649, April.
    2. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 353-377.
    3. Yu, Jinna & Tang, Yuk Ming & Chau, Ka Yin & Nazar, Raima & Ali, Sajid & Iqbal, Wasim, 2022. "Role of solar-based renewable energy in mitigating CO2 emissions: Evidence from quantile-on-quantile estimation," Renewable Energy, Elsevier, vol. 182(C), pages 216-226.
    4. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    5. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    6. Xiekui Zhang & Hongfei Zhu, 2023. "The Impact of Industrial Intelligence on Carbon Emissions: Evidence from the Three Largest Economies," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    7. Xinping Li & Qiongxia Qin & Yongliang Yang, 2023. "The Impact of Green Innovation on Carbon Emissions: Evidence from the Construction Sector in China," Energies, MDPI, vol. 16(11), pages 1-23, June.
    8. Yu Zhou & Caijiang Zhang & Zhangwen Li, 2023. "The impact of digital financial inclusion on household carbon emissions: evidence from China," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-21, December.
    9. Yaya Li & Yuru Zhang & An Pan & Minchun Han & Eleonora Veglianti, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Post-Print hal-04522085, HAL.
    10. Anna Papp & Douglas Almond & Shuang Zhang, 2023. "Bitcoin and Carbon Dioxide Emissions: Evidence from Daily Production Decisions," NBER Working Papers 31745, National Bureau of Economic Research, Inc.
    11. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
    12. Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Ghosh, Sudeshna & Doğan, Buhari, 2023. "Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    13. Hao Lv & Beibei Shi & Nan Li & Rong Kang, 2022. "Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    14. Harty D. Saunders, 1992. "The Khazzoom-Brookes Postulate and Neoclassical Growth," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 131-148.
    15. Anna Matysiak & Daniela Bellani & Honorata Bogusz, 2023. "Industrial Robots and Regional Fertility in European Countries," European Journal of Population, Springer;European Association for Population Studies, vol. 39(1), pages 1-36, December.
    16. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    17. Giovanni Dosi, 2000. "Sources, Procedures, and Microeconomic Effects of Innovation," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 2, pages 63-114, Edward Elgar Publishing.
    18. Guo, Qingbin & Wang, Yong & Dong, Xiaobin, 2022. "Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China," Applied Energy, Elsevier, vol. 313(C).
    19. Saikkonen, Pentti, 1991. "Asymptotically Efficient Estimation of Cointegration Regressions," Econometric Theory, Cambridge University Press, vol. 7(1), pages 1-21, March.
    20. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
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