IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6709-d1124408.html
   My bibliography  Save this article

Exploring the Direct and Spillover Effects of Aging on Green Total Factor Productivity in China: A Spatial Econometric Approach

Author

Listed:
  • Lei Jiang

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    Guangdong Provincial Center for Urban and Migration Studies, Guangzhou 510006, China)

  • Xingyu Chen

    (School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Yang Jiang

    (College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China)

  • Bo Zhang

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    Guangdong Provincial Center for Urban and Migration Studies, Guangzhou 510006, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China)

Abstract

China is a rapidly aging nation. Therefore, it is a matter of urgency to address the challenges of aging in China and to coordinate the relationships between population aging, environmental issues, and socio-economic development. However, few empirical studies have thus far analyzed the impact of aging on green total factor productivity (GTFP) in China. Hence, this study employs a global Malmquist–Luenberger index method (GMLI) to calculate the GTFP scores of 30 Chinese provinces from 2002 to 2018. We apply spatiotemporal analysis methods to identify the variations of population aging and GTFP scores and then build a spatial econometric model to examine the impact of population aging on GTFP. Our study findings are as follows. (1) Whereas at the beginning of the 21st century, provinces with deep aging were mostly situated in the east, the population aging issue in China is now spreading across the entire country. (2) From a dynamic perspective, the overall GTFP growth rate in China during the sample period depicts a U-shaped structure with time. (3) Results of the spatial Durbin model show that the impact of population aging in a given region on GTFP is negative, but the spatial spillover effect of aging in neighboring regions on GTFP in a given region is positive, resulting in the loss of younger local labor forces in some provinces due to low birth rates and migration to neighboring regions. Finally, to cope with a growing aging population and to possibly eliminate the negative impacts of population aging on high-quality sustainable development, the government should promote the establishment of the old-age security system; increased investment in R & D and wide use of advanced technology should also be urgently encouraged.

Suggested Citation

  • Lei Jiang & Xingyu Chen & Yang Jiang & Bo Zhang, 2023. "Exploring the Direct and Spillover Effects of Aging on Green Total Factor Productivity in China: A Spatial Econometric Approach," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6709-:d:1124408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6709/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/8/6709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2018. "Analysis of green total-factor productivity in China's regional metal industry: A meta-frontier approach," Resources Policy, Elsevier, vol. 58(C), pages 219-229.
    2. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    3. Houjian Li & Xiaolei Zhou & Mengqian Tang & Lili Guo, 2022. "Impact of Population Aging and Renewable Energy Consumption on Agricultural Green Total Factor Productivity in Rural China: Evidence from Panel VAR Approach," Agriculture, MDPI, vol. 12(5), pages 1-19, May.
    4. Bo Xie & Jie Zhou & Xiao Luo, 2016. "Mapping spatial variation of population aging in China's mega cities," Journal of Maps, Taylor & Francis Journals, vol. 12(1), pages 181-192, January.
    5. Xu, Su Xiu, 2021. "Overexploitation Risk in “Green Mountains and Clear Water”," Ecological Economics, Elsevier, vol. 179(C).
    6. Jiang, Yufan & Wang, Hongyan & Liu, Zuankuo, 2021. "The impact of the free trade zone on green total factor productivity ——evidence from the shanghai pilot free trade zone," Energy Policy, Elsevier, vol. 148(PB).
    7. Jin, Gang & Shen, Kunrong & Li, Jian, 2020. "Interjurisdiction political competition and green total factor productivity in China: An inverted-U relationship," China Economic Review, Elsevier, vol. 61(C).
    8. Chen, Feng-Wen & Tan, Yulu & Chen, Fengzhang & Wu, Yong-Qiu, 2021. "Enhancing or suppressing: The effect of labor costs on energy intensity in emerging economies," Energy, Elsevier, vol. 214(C).
    9. Wei Qian & Yongsheng Wang, 2022. "How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    10. Uschi Backes-Gellner & Stephan Veen, 2013. "Positive Effects of Ageing and Age-Diversity in Innovative Companies - Large Scale Evidence on Company Productivity," Economics of Education Working Paper Series 0093, University of Zurich, Department of Business Administration (IBW).
    11. Lee, Chi-Chuan & Lee, Chien-Chiang, 2022. "How does green finance affect green total factor productivity? Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    12. Tian, Ying & Feng, Chao, 2022. "The internal-structural effects of different types of environmental regulations on China's green total-factor productivity," Energy Economics, Elsevier, vol. 113(C).
    13. Börsch-Supan, Axel & Weiss, Matthias, 2016. "Productivity and age: Evidence from work teams at the assembly line," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 30-42.
    14. Mason, Andrew & Lee, Ronald & Jiang, Jennifer Xue, 2016. "Demographic dividends, human capital, and saving," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 106-122.
    15. Choi, Ki-Hong & Shin, Sungwhee, 2015. "Population aging, economic growth, and the social transmission of human capital: An analysis with an overlapping generations model," Economic Modelling, Elsevier, vol. 50(C), pages 138-147.
    16. Lu, Xin-hai & Jiang, Xu & Gong, Meng-qi, 2020. "How land transfer marketization influence on green total factor productivity from the approach of industrial structure? Evidence from China," Land Use Policy, Elsevier, vol. 95(C).
    17. Xia, Fan & Xu, Jintao, 2020. "Green total factor productivity: A re-examination of quality of growth for provinces in China," China Economic Review, Elsevier, vol. 62(C).
    18. Xie, Rui & Fu, Wei & Yao, Siling & Zhang, Qi, 2021. "Effects of financial agglomeration on green total factor productivity in Chinese cities: Insights from an empirical spatial Durbin model," Energy Economics, Elsevier, vol. 101(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wentao Hu & Xiaoxiao Li, 2023. "Financial Technology Development and Green Total Factor Productivity," Sustainability, MDPI, vol. 15(13), pages 1-28, June.

    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. Huang, Hongyun & Mo, Renbian & Chen, Xingquan, 2021. "New patterns in China's regional green development: An interval Malmquist–Luenberger productivity analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 161-173.
    2. Lv, Chengchao & Song, Jie & Lee, Chien-Chiang, 2022. "Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 502-521.
    3. Decai Tang & Zhangming Shan & Junxia He & Ziqian Zhao, 2022. "How Do Environmental Regulations and Outward Foreign Direct Investment Impact the Green Total Factor Productivity in China? A Mediating Effect Test Based on Provincial Panel Data," IJERPH, MDPI, vol. 19(23), pages 1-32, November.
    4. Guang Chen & Akira Hibiki, 2022. "Can the Carbon Emission Trading Scheme Influence Industrial Green Production in China?," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    5. Yan Xiao & Yan Zhang & Jiekuan Zhang, 2023. "The Impact of Carbon Emission Trading on Industrial Green Total Factor Productivity," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    6. Wenhan Ren & Yu Chen, 2022. "Realizing the Improvement of Green Total Factor Productivity of the Marine Economy—New Evidence from China’s Coastal Areas," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    7. Zhuo, Chengfeng & Xie, Yuping & Mao, Yanhua & Chen, Pengqin & Li, Yiqiao, 2022. "Can cross-regional environmental protection promote urban green development: Zero-sum game or win-win choice?," Energy Economics, Elsevier, vol. 106(C).
    8. Wang, Feng & Wu, Min & Wang, Jingcao, 2023. "Can increasing economic complexity improve China's green development efficiency?," Energy Economics, Elsevier, vol. 117(C).
    9. Tan, Ruipeng & Pan, Lulu & Xu, Mengmeng & He, Xinju, 2022. "Transportation infrastructure, economic agglomeration and non-linearities of green total factor productivity growth in China: Evidence from partially linear functional coefficient model," Transport Policy, Elsevier, vol. 129(C), pages 1-13.
    10. Zhao, Congyu & Jia, Rongwen & Dong, Kangyin, 2023. "How does smart transportation technology promote green total factor productivity? The case of China," Research in Transportation Economics, Elsevier, vol. 101(C).
    11. Gao, Yuning & Zhang, Meichen & Zheng, Jinghai, 2021. "Accounting and determinants analysis of China's provincial total factor productivity considering carbon emissions," China Economic Review, Elsevier, vol. 65(C).
    12. Feng, Rui & Shen, Chen & Dai, Dandan & Xin, Yaru, 2023. "Examining the spatiotemporal evolution, dynamic convergence and drivers of green total factor productivity in China’s urban agglomerations," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 744-764.
    13. Dan Pan & Yi Yu & Fanbin Kong, 2023. "Quantifying the Effectiveness of Environmental Regulations on Green Total Factor Productivity: Evidence Based on China’s Environmental Protection Interview Program," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    14. Ma, Dan & Zhu, Qing, 2022. "Innovation in emerging economies: Research on the digital economy driving high-quality green development," Journal of Business Research, Elsevier, vol. 145(C), pages 801-813.
    15. Long Qian & Yunjie Zhou & Ying Sun, 2023. "Regional Differences, Distribution Dynamics, and Convergence of the Green Total Factor Productivity of China’s Cities under the Dual Carbon Targets," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    16. Hua Zhang & Qiwang Zhang, 2023. "How Does Digital Transformation Facilitate Enterprise Total Factor Productivity? The Multiple Mediators of Supplier Concentration and Customer Concentration," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    17. Salman, Muhammad & Long, Xingle & Wang, Guimei & Zha, Donglan, 2022. "Paris climate agreement and global environmental efficiency: New evidence from fuzzy regression discontinuity design," Energy Policy, Elsevier, vol. 168(C).
    18. Weixiang Zhao & Yankun Xu, 2022. "Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    19. 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).
    20. Meiling Wang & Silu Pang & Ikram Hmani & Ilham Hmani & Cunfang Li & Zhengxia He, 2021. "Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 217-227, January.

    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:gam:jsusta:v:15:y:2023:i:8:p:6709-:d:1124408. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.