IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9721-d1173665.html
   My bibliography  Save this article

Does Population Aging Affect Carbon Emission Intensity by Regulating Labor Allocation?

Author

Listed:
  • Ran Yu

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

  • Zhangchi Wang

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

  • Yan Li

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

  • Zuhui Wen

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

  • Weijia Wang

    (School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China)

Abstract

Carbon emission is the focus of global climate change concerns. Population aging changes the level of labor structure, which directly affects the industry adjustment and will also have a long-term impact on carbon emissions. Uncovering the complex association among population aging, labor allocation, and CO 2 emission is crucial for developing effective policies for low-carbon and sustainable development in China. Therefore, this study aims to analyze whether population aging contributes to reducing carbon emission intensity by regulating labor allocation. Based on provincial panel data from 2000 to 2019, the Systematic Generalized Method of Moments (Systematic GMM) model and the Bias Corrected Least Squares Estimation with Nonsymmetric Dependence Structure (Bias Corrected LSDV) model are adopted in this study. The results show that nationwide as a whole, population aging objectively inhibits human capital accumulation and, to some extent, weakens its positive carbon emission reduction effect. Meanwhile, population aging helps to mitigate the increase in carbon emissions caused by the capital-labor endowment structure. Due to the dual impact of aging and population migration, the emission reduction effect of human capital accumulation is significant in the East. The brain drain in the central and western regions further inhibits the positive effect of regional human capital accumulation. Promoting the rationalization of population mobility nationwide, reducing the brain drain in less developed regions, and directing capital into technology-intensive industrial sectors are the core keys to achieving optimal labor allocation in an aging society. This will help China meet its carbon neutrality target on schedule.

Suggested Citation

  • Ran Yu & Zhangchi Wang & Yan Li & Zuhui Wen & Weijia Wang, 2023. "Does Population Aging Affect Carbon Emission Intensity by Regulating Labor Allocation?," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9721-:d:1173665
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9721/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/12/9721/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ha, Wei & Yi, Junjian & Zhang, Junsen, 2016. "Brain drain, brain gain, and economic growth in China," China Economic Review, Elsevier, vol. 38(C), pages 322-337.
    2. Pedro Cavalcanti Ferreira & Samuel de Abreu Pessoa, 2007. "The Effects of Longevity and Distortions on Education and Retirement," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(3), pages 472-493, July.
    3. Fougère, Maxime & Harvey, Simon & Mercenier, Jean & Mérette, Marcel, 2009. "Population ageing, time allocation and human capital: A general equilibrium analysis for Canada," Economic Modelling, Elsevier, vol. 26(1), pages 30-39, January.
    4. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    5. Anzelika Zaiceva, 2014. "The impact of aging on the scale of migration," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-99, November.
    6. Cao, Jing & Ho, Mun S. & Hu, Wenhao & Jorgenson, Dale, 2020. "Effective labor supply and growth outlook in China," China Economic Review, Elsevier, vol. 61(C).
    7. Griffin, James M & Gregory, Paul R, 1976. "An Intercountry Translog Model of Energy Substitution Responses," American Economic Review, American Economic Association, vol. 66(5), pages 845-857, December.
    8. Daron Acemoglu & Pascual Restrepo, 2017. "Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation," American Economic Review, American Economic Association, vol. 107(5), pages 174-179, May.
    9. Gradstein, Mark & Kaganovich, Michael, 2004. "Aging population and education finance," Journal of Public Economics, Elsevier, vol. 88(12), pages 2469-2485, December.
    10. Khan, Zaid Ashiq & Koondhar, Mansoor Ahmed & Tiantong, Ma & Khan, Aftab & Nurgazina, Zhanar & Tianjun, Liu & Fengwang, Ma, 2022. "Do chemical fertilizers, area under greenhouses, and renewable energies drive agricultural economic growth owing the targets of carbon neutrality in China?," Energy Economics, Elsevier, vol. 115(C).
    11. Wang, Lili & Szirmai, Adam, 2012. "Capital inputs in the Chinese economy: Estimates for the total economy, industry and manufacturing," China Economic Review, Elsevier, vol. 23(1), pages 81-104.
    12. 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.
    13. Gao, Kang & Yuan, Yijun, 2022. "Does market-oriented reform make the industrial sector “Greener” in China? Fresh evidence from the perspective of capital-labor-energy market distortions," Energy, Elsevier, vol. 254(PA).
    14. Gao, Cuixia & Tao, Simin & He, Yuyang & Su, Bin & Sun, Mei & Mensah, Isaac Adjei, 2021. "Effect of population migration on spatial carbon emission transfers in China," Energy Policy, Elsevier, vol. 156(C).
    15. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    16. Sufyanullah, Khan & Ahmad, Khan Arshad & Sufyan Ali, Muhammad Abu, 2022. "Does emission of carbon dioxide is impacted by urbanization? An empirical study of urbanization, energy consumption, economic growth and carbon emissions - Using ARDL bound testing approach," Energy Policy, Elsevier, vol. 164(C).
    17. Dogan, Eyup & Chishti, Muhammad Zubair & Karimi Alavijeh, Nooshin & Tzeremes, Panayiotis, 2022. "The roles of technology and Kyoto Protocol in energy transition towards COP26 targets: Evidence from the novel GMM-PVAR approach for G-7 countries," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    18. Knesl, Jiří, 2023. "Automation and the displacement of labor by capital: Asset pricing theory and empirical evidence," Journal of Financial Economics, Elsevier, vol. 147(2), pages 271-296.
    19. Abdulla, Kanat, 2020. "Human capital accumulation: Evidence from immigrants in low-income countries," Journal of Comparative Economics, Elsevier, vol. 48(4), pages 951-973.
    20. Leonid Azarnert, 2010. "Free education, fertility and human capital accumulation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(2), pages 449-468, March.
    21. Mingyi, Wang & Zhongyi, Zhang & Xiaoyu, Liu & Siwei, Xu, 2023. "Labor price distortion and export product markups: Evidence from China labor market," China Economic Review, Elsevier, vol. 77(C).
    22. Kwan, Fung & Zhang, Yang & Zhuo, Shuaihe, 2018. "Labour reallocation, productivity growth and dualism: The case of China," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 198-210.
    23. Baltagi, Badi H. & Wu, Ping X., 1999. "Unequally Spaced Panel Data Regressions With Ar(1) Disturbances," Econometric Theory, Cambridge University Press, vol. 15(6), pages 814-823, December.
    24. Weidong Li & Xin Qi & Xiaojun Zhao, 2018. "Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis," Sustainability, MDPI, vol. 10(7), pages 1-13, July.
    25. Addessi, William, 2018. "Population age structure and consumption expenditure composition: Evidence from European countries," Economics Letters, Elsevier, vol. 168(C), pages 18-20.
    26. Jäger, Philipp & Schmidt, Torsten, 2016. "The political economy of public investment when population is aging: A panel cointegration analysis," European Journal of Political Economy, Elsevier, vol. 43(C), pages 145-158.
    27. Judith Banister & David E. Bloom & Larry Rosenberg, 2012. "Population Aging and Economic Growth in China," International Economic Association Series, in: Masahiko Aoki & Jinglian Wu (ed.), The Chinese Economy, chapter 6, pages 114-149, Palgrave Macmillan.
    28. Tan, Youchao & Liu, Xiumei & Sun, Hanwen & Zeng, Cheng(Colin), 2022. "Population ageing, labour market rigidity and corporate innovation: Evidence from China," Research Policy, Elsevier, vol. 51(2).
    29. Lin, Boqiang & Chen, Xing, 2020. "How technological progress affects input substitution and energy efficiency in China: A case of the non-ferrous metals industry," Energy, Elsevier, vol. 206(C).
    30. Lin, Boqiang & Wang, Chonghao, 2023. "Does industrial relocation affect regional carbon intensity? Evidence from China's secondary industry," Energy Policy, Elsevier, vol. 173(C).
    31. Agrawal, Ajay & Kapur, Devesh & McHale, John & Oettl, Alexander, 2011. "Brain drain or brain bank? The impact of skilled emigration on poor-country innovation," Journal of Urban Economics, Elsevier, vol. 69(1), pages 43-55, January.
    32. Lee, Jong-Wha & Kwak, Do Won & Song, Eunbi, 2022. "Can older workers stay productive? The role of ICT skills and training," Journal of Asian Economics, Elsevier, vol. 79(C).
    33. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    34. Menz, Tobias & Welsch, Heinz, 2012. "Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects," Energy Economics, Elsevier, vol. 34(3), pages 842-849.
    35. Huang, Kaixing & Zhao, Hong & Huang, Jikun & Wang, Jinxia & Findlay, Christopher, 2020. "The impact of climate change on the labor allocation: Empirical evidence from China," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    36. Giovanni S. F. Bruno, 2005. "Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals," Stata Journal, StataCorp LP, vol. 5(4), pages 473-500, December.
    37. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    38. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    39. Daron Acemoglu, 2007. "Equilibrium Bias of Technology," Econometrica, Econometric Society, vol. 75(5), pages 1371-1409, September.
    40. Xiang, Yitian & Cui, Haotian & Bi, Yunxiao, 2023. "The impact and channel effects of banking competition and government intervention on carbon emissions: Evidence from China," Energy Policy, Elsevier, vol. 175(C).
    41. Wu, Ning & Liu, ZuanKuo, 2021. "Higher education development, technological innovation and industrial structure upgrade," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    42. Walheer, Barnabé, 2021. "Labor productivity and technology heterogeneity," Journal of Macroeconomics, Elsevier, vol. 68(C).
    43. Yao, Yourong & Shen, Yue & Liu, Kexin, 2023. "Investigation of resource utilization in urbanization development: An analysis based on the current situation of carbon emissions in China," Resources Policy, Elsevier, vol. 82(C).
    44. Long, Houyin & Li, Jianglong & Liu, Hongxun, 2022. "Internal migration and associated carbon emission changes: Evidence from cities in China," Energy Economics, Elsevier, vol. 110(C).
    45. Khan, Zeeshan & Hussain, Muzzammil & Shahbaz, Muhammad & Yang, Siqun & Jiao, Zhilun, 2020. "Natural resource abundance, technological innovation, and human capital nexus with financial development: A case study of China," Resources Policy, Elsevier, vol. 65(C).
    46. Zhan, Peng & Ma, Xinxin & Li, Shi, 2021. "Migration, population aging, and income inequality in China," Journal of Asian Economics, Elsevier, vol. 76(C).
    47. Supachet Chansarn, 2010. "Labor Productivity Growth, Education, Health and Technological Progress: A Cross-Country Analysis," Economic Analysis and Policy, Elsevier, vol. 40(2), pages 249-261, September.
    48. Solveig Erlandsen & Ragnar Nymoen, 2008. "Consumption and population age structure," Journal of Population Economics, Springer;European Society for Population Economics, vol. 21(3), pages 505-520, July.
    49. repec:iza:izawol:journl:y:2014:p:99 is not listed on IDEAS
    50. Yao, Wenyun & Zhang, Yi & Ma, Jingwen & Cui, Guanghui, 2023. "Does environmental regulation affect capital-labor ratio of manufacturing enterprises: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 86(C).
    51. Kim, Hoolda & Song Lee, Bun, 2023. "Aging workforce, wages, and productivity: Do older workers drag productivity down in Korea?," The Journal of the Economics of Ageing, Elsevier, vol. 24(C).
    52. Okada, Akira, 2012. "Is an increased elderly population related to decreased CO2 emissions from road transportation?," Energy Policy, Elsevier, vol. 45(C), pages 286-292.
    53. Wu, Linfei & Sun, Liwen & Qi, Peixiao & Ren, Xiangwei & Sun, Xiaoting, 2021. "Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions," Resources Policy, Elsevier, vol. 74(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. Rainer Kotschy & Uwe Sunde & Tommaso MonacelliManaging Editor, 2018. "Can education compensate the effect of population ageing on macroeconomic performance?," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 33(96), pages 587-634.
    2. Abeliansky, Ana Lucia & Prettner, Klaus, 2017. "Automation and demographic change," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168215, Verein für Socialpolitik / German Economic Association.
    3. Abeliansky, Ana Lucia & Prettner, Klaus, 2023. "Automation and population growth: Theory and cross-country evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 208(C), pages 345-358.
    4. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    5. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    6. Zhang, Weike & Fan, Hongxia & Zhao, Qiwei, 2023. "Seeing green: How does digital infrastructure affect carbon emission intensity?," Energy Economics, Elsevier, vol. 127(PB).
    7. Hean, Oudom, 2018. "The Effect of Metropolitan Technological Progress on the Non-metropolitan Labor Market: Evidence from U.S. Patent Counts," 2018 Annual Meeting, August 5-7, Washington, D.C. 274176, Agricultural and Applied Economics Association.
    8. Che, Shuai & Wang, Jun & Chen, Honghang, 2023. "Can China's decentralized energy governance reduce carbon emissions? Evidence from new energy demonstration cities," Energy, Elsevier, vol. 284(C).
    9. Che, Shuai & Wang, Jun, 2022. "Can environmental regulation solve the carbon curse of natural resource dependence: Evidence from China," Resources Policy, Elsevier, vol. 79(C).
    10. Dosi, G. & Piva, M. & Virgillito, M.E. & Vivarelli, M., 2021. "Embodied and disembodied technological change: The sectoral patterns of job-creation and job-destruction," Research Policy, Elsevier, vol. 50(4).
    11. Issaka Dialga & Youmanli Ouoba, 2022. "How do extractive resources affect human development ? Evidence from a panel data analysis," Post-Print hal-04467781, HAL.
    12. Harald Oberhofer & Christian Glocker & Werner Hölzl & Peter Huber & Serguei Kaniovski & Klaus Nowotny & Michael Pfaffermayr & Monique Ebell & Nikolaos Kontogiannis, 2016. "Single Market Transmission Mechanisms Before, During and After the 2008-09 Crisis. A Quantitative Assessment," WIFO Studies, WIFO, number 59156, April.
    13. Mariacristina Piva & Marco Vivarelli, 2018. "Is Innovation Destroying Jobs? Firm-Level Evidence from the EU," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    14. Mariacristina Piva & Marco Vivarelli, 2018. "Technological change and employment: is Europe ready for the challenge?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 8(1), pages 13-32, March.
    15. Ana Lucia Abeliansky & Klaus Prettner, 2021. "Population Growth and Automation Density: Theory and CrossCountry Evidence," VID Working Papers 2102, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
    16. Giovanni Dosi & Mariacristina Piva & Maria Enrica Virgillito & Marco Vivarelli, 2019. "Technology and employment in a vertically connected economy: a model and an empirical test," DISCE - Quaderni del Dipartimento di Politica Economica dipe0005, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    17. Florian Dorn & Christoph Schinke, 2018. "Top income shares in OECD countries: The role of government ideology and globalisation," The World Economy, Wiley Blackwell, vol. 41(9), pages 2491-2527, September.
    18. Piva, Mariacristina & Vivarelli, Marco, 2017. "Technological Change and Employment: Were Ricardo and Marx Right?," IZA Discussion Papers 10471, Institute of Labor Economics (IZA).
    19. Mougnol A Ekoula, Hervé William & Kamguia, Brice & Ndoya, Hermann, 2023. "Do women hold the key to financial sector development in Africa?," International Economics, Elsevier, vol. 173(C), pages 233-248.
    20. Łukasz Goczek, 2012. "Metody ekonometryczne w modelach wzrostu gospodarczego," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 10, pages 49-71.

    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:12:p:9721-:d:1173665. 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.