IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v85y2023ipas0301420723005883.html
   My bibliography  Save this article

Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?

Author

Listed:
  • Wang, Jianda
  • Wang, Kun
  • Dong, Kangyin
  • Zhang, Shiqiu

Abstract

Improving natural resource utilization efficiency (NRUE) in China has become an inevitable requirement for sustainable development in the future, especially in the process of financial development and the application of emerging technologies. Therefore, this paper investigates the nexus among financial development, artificial intelligence (AI) technological innovation, and NRUE by using panel data from 30 provinces in China from 2006 to 2019. We also conduct heterogeneity analyses from the perspective of regions and resources. The main results indicate that financial development reduces NRUE due to inefficient use of natural resources. Moreover, AI technological innovation facilitates NRUE, thereby mitigating the negative influence of financial development on NRUE. Finally, the influence of AI technological innovation development on NRUE improvement is significantly greater in southern Chinese provinces and resource-based provinces. The results of this research provide valuable references for China to enhance the efficient use of natural resources in the future.

Suggested Citation

  • Wang, Jianda & Wang, Kun & Dong, Kangyin & Zhang, Shiqiu, 2023. "Assessing the role of financial development in natural resource utilization efficiency: Does artificial intelligence technology matter?," Resources Policy, Elsevier, vol. 85(PA).
    • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pa:s0301420723005883
    DOI: 10.1016/j.resourpol.2023.103877
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723005883
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103877?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. Francisco J. Buera & Yongseok Shin, 2013. "Financial Frictions and the Persistence of History: A Quantitative Exploration," Journal of Political Economy, University of Chicago Press, vol. 121(2), pages 221-272.
    2. Khan, Zeeshan & Ali, Shahid & Dong, Kangyin & Li, Rita Yi Man, 2021. "How does fiscal decentralization affect CO2 emissions? The roles of institutions and human capital," Energy Economics, Elsevier, vol. 94(C).
    3. Wang, Jianda & Shahbaz, Muhammad & Dong, Kangyin & Dong, Xiucheng, 2023. "Renewable energy transition in global carbon mitigation: Does the use of metallic minerals matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 181(C).
    4. Luo, Keyu & Wang, Qi & Liang, Chao, 2022. "The way to break the resource curse: New evidence from China," Resources Policy, Elsevier, vol. 79(C).
    5. Lara Waltersmann & Steffen Kiemel & Julian Stuhlsatz & Alexander Sauer & Robert Miehe, 2021. "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    6. Singh, Bhanu Pratap & Kannadhasan, M., 2020. "Corruption and capital structure in emerging markets: A panel quantile regression approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    7. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    8. Miao, Chenglin & Fang, Debin & Sun, Liyan & Luo, Qiaoling, 2017. "Natural resources utilization efficiency under the influence of green technological innovation," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 153-161.
    9. Croutzet, Alexandre & Dabbous, Amal, 2021. "Do FinTech trigger renewable energy use? Evidence from OECD countries," Renewable Energy, Elsevier, vol. 179(C), pages 1608-1617.
    10. Zhao, Congyu & Wang, Jianda & Dong, Kangyin & Wang, Kun, 2023. "How does renewable energy encourage carbon unlocking? A global case for decarbonization," Resources Policy, Elsevier, vol. 83(C).
    11. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
    12. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    13. Jianda Wang & Jun Zhao & Kangyin Dong & Xiucheng Dong, 2023. "How does the internet economy affect CO2 emissions? Evidence from China," Applied Economics, Taylor & Francis Journals, vol. 55(4), pages 447-466, January.
    14. Bansal, Sanchita & Singh, Shifali & Nangia, Priya, 2022. "Assessing the role of natural resource utilization in attaining select sustainable development goals in the era of digitalization," Resources Policy, Elsevier, vol. 79(C).
    15. Zhang, Xiumin & Zhao, Tianyu & Wang, Linhui & Dong, Zhiqing, 2022. "Does Fintech benefit financial disintermediation? Evidence based on provinces in China from 2013 to 2018," Journal of Asian Economics, Elsevier, vol. 82(C).
    16. Acemoglu, Daron & Zilibotti, Fabrizio, 1997. "Was Prometheus Unbound by Chance? Risk, Diversification, and Growth," Journal of Political Economy, University of Chicago Press, vol. 105(4), pages 709-751, August.
    17. Xu, Li & Tan, Junlan, 2020. "Financial development, industrial structure and natural resource utilization efficiency in China," Resources Policy, Elsevier, vol. 66(C).
    18. Asif, Muhammad & Khan, Khan Burhan & Anser, Muhammad Khalid & Nassani, Abdelmohsen A. & Abro, Muhammad Moinuddin Qazi & Zaman, Khalid, 2020. "Dynamic interaction between financial development and natural resources: Evaluating the ‘Resource curse’ hypothesis," Resources Policy, Elsevier, vol. 65(C).
    19. Yining Zhang & Zhong Wu, 2021. "Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    20. Yao, Xing & Yasmeen, Rizwana & Hussain, Jamal & Hassan Shah, Wasi Ul, 2021. "The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries," Energy, Elsevier, vol. 223(C).
    21. Wei Liu & Youfa Sun & Serhat Yüksel & Hasan Dinçer, 2021. "Consensus-based multidimensional due diligence of fintech-enhanced green energy investment projects," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    22. Tan, Qingmei & Yasmeen, Humaira & Ali, Sharafat & Ismail, Hina & Zameer, Hashim, 2023. "Fintech development, renewable energy consumption, government effectiveness and management of natural resources along the belt and road countries," Resources Policy, Elsevier, vol. 80(C).
    23. Chen, Haiyang & Dai, Ya & Guo, David, 2023. "Financial literacy as a determinant of market participation: New evidence from China using IV-GMM," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 611-623.
    24. Atif Jahanger & Muhammad Usman & Daniel Balsalobre‐Lorente, 2022. "Linking institutional quality to environmental sustainability," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(6), pages 1749-1765, December.
    25. Rafei, Meysam & Esmaeili, Parisa & Balsalobre-Lorente, Daniel, 2022. "A step towards environmental mitigation: How do economic complexity and natural resources matter? Focusing on different institutional quality level countries," Resources Policy, Elsevier, vol. 78(C).
    26. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    27. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    28. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    29. Yang, Lihong & Wang, Shixun, 2022. "Do fintech applications promote regional innovation efficiency? Empirical evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    30. Zhang, Yonggang & Dilanchiev, Azer, 2022. "Economic recovery, industrial structure and natural resource utilization efficiency in China: Effect on green economic recovery," Resources Policy, Elsevier, vol. 79(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, Xing & Guo, Yifan & Feng, Tianchu, 2023. "Towards green recovery: Natural resources utilization efficiency under the impact of environmental information disclosure," Resources Policy, Elsevier, vol. 83(C).
    2. Liu, Yang & Wang, Jianda & Dong, Kangyin & Taghizadeh-Hesary, Farhad, 2023. "How does natural resource abundance affect green total factor productivity in the era of green finance? Global evidence," Resources Policy, Elsevier, vol. 81(C).
    3. Wang, Bo & Wang, Jianda & Dong, Kangyin & Dong, Xiucheng, 2023. "Is the digital economy conducive to the development of renewable energy in Asia?," Energy Policy, Elsevier, vol. 173(C).
    4. Yang, Senmiao & Wang, Jianda & Dong, Kangyin & Jiang, Qingzhe, 2023. "A path towards China's energy justice: How does digital technology innovation bring about a just revolution?," Energy Economics, Elsevier, vol. 127(PA).
    5. Li, ChangZheng & Razzaq, Asif & Ozturk, Ilhan & Sharif, Arshian, 2023. "Natural resources, financial technologies, and digitalization: The role of institutional quality and human capital in selected OECD economies," Resources Policy, Elsevier, vol. 81(C).
    6. Liu, Yang & Dong, Kangyin & Jiang, Qingzhe, 2023. "Assessing energy vulnerability and its impact on carbon emissions: A global case," Energy Economics, Elsevier, vol. 119(C).
    7. Li, Guoxiang & Wu, Haoyue & Jiang, Jieshu & Zong, Qingqing, 2023. "Digital finance and the low-carbon energy transition (LCET) from the perspective of capital-biased technical progress," Energy Economics, Elsevier, vol. 120(C).
    8. Liu, Yang & Dong, Kangyin & Wang, Jianda & Taghizadeh-Hesary, Farhad, 2023. "Towards sustainable development goals: Does common prosperity contradict carbon reduction?," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 70-88.
    9. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    10. Liu, Jiangtao & Zhang, Yi & Kuang, Jia, 2023. "Fintech development and green innovation: Evidence from China," Energy Policy, Elsevier, vol. 183(C).
    11. Ding, Yuanyi, 2023. "Does natural resources cause sustainable financial development or resources curse? Evidence from group of seven economies," Resources Policy, Elsevier, vol. 81(C).
    12. Talan, Amogh & Rao, Amar & Sharma, Gagan Deep & Apostu, Simona-Andreea & Abbas, Shujaat, 2023. "Transition towards clean energy consumption in G7: Can financial sector, ICT and democracy help?," Resources Policy, Elsevier, vol. 82(C).
    13. Carlson Mark A & King Thomas & Lewis Kurt, 2011. "Distress in the Financial Sector and Economic Activity," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(1), pages 1-31, June.
    14. Li, Na & Wu, Di, 2023. "Nexus between natural resource and economic development: How green innovation and financial inclusion create sustainable growth in BRICS region?," Resources Policy, Elsevier, vol. 85(PA).
    15. Yuxin Fang & Hongjun Cao & Jihui Sun, 2022. "Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect," IJERPH, MDPI, vol. 19(22), pages 1-27, November.
    16. António Antunes & Tiago Cavalcanti & Anne Villamil, 2015. "The effects of credit subsidies on development," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 58(1), pages 1-30, January.
    17. Angeletos, George-Marios & Panousi, Vasia, 2009. "Revisiting the supply side effects of government spending," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 137-153, March.
    18. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    19. Nassani, Abdelmohsen A. & Aldakhil, Abdullah Mohammed & Zaman, Khalid, 2021. "Ecological footprints jeopardy for mineral resource extraction: Efficient use of energy, financial development and insurance services to conserve natural resources," Resources Policy, Elsevier, vol. 74(C).
    20. Wu, Zihao & Gao, Jun & Xu, Hui & Shi, Guanqun & Zaidan, Amal Mousa & Ageli, Mohammed Moosa, 2023. "Visualizing symmetric and asymmetric settings in MMQR for natural resources extraction and economic performance: A COVID-19 perspective," Resources Policy, Elsevier, vol. 85(PB).

    More about this item

    Keywords

    Natural resource utilization efficiency; Financial development; Artificial intelligence technological innovation; Heterogeneity; China;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • Q26 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Recreational Aspects of Natural Resources

    Statistics

    Access and download statistics

    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:jrpoli:v:85:y:2023:i:pa:s0301420723005883. 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/inca/30467 .

    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.