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Knowledge production and world population dynamics

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  • Dolgonosov, Boris M.

Abstract

Three laws of knowledge production explaining the empirically observed hyperbolic growth of world population are formulated. Knowledge and demographic dynamics are developed on the basis of the least action principle to describe deviations from the hyperbolic growth. The efficiency of knowledge production defined as the share of technological knowledge in total knowledge production is introduced. It is proven that a monotonic population growth with reaching a plateau is possible only when efficiency is 1. At a lower efficiency, population reaches a maximum and then declines. The estimated efficiency is currently not more than 0.1. The calculations show that if efficiency remains constant, population will peak at 9 billion in the mid-21st century and then decline to 5 billion or lower over several centuries. To keep population at a level of not less than 90% of maximum, it is necessary to raise efficiency of up to 0.31.

Suggested Citation

  • Dolgonosov, Boris M., 2016. "Knowledge production and world population dynamics," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 127-141.
    • Handle: RePEc:eee:tefoso:v:103:y:2016:i:c:p:127-141
    DOI: 10.1016/j.techfore.2015.10.023
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    References listed on IDEAS

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    Cited by:

    1. Boris M. Dolgonosov, 2020. "The energy representation of world GDP," Papers 2006.07938, arXiv.org.
    2. Court Victor & Florent Mc Isaac, 2019. "A Representation of the World Population Dynamics for Integrated Assessment Models," Working Papers hal-03192539, HAL.
    3. Chatterjee, Diti & Dinar, Ariel & González-Rivera, Gloria, 2018. "An empirical knowledge production function of agricultural research and extension: The case of the University of California Cooperative Extension," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 290-297.
    4. Court Victor & Florent Mc Isaac, 2019. "A Representation of the World Population Dynamics for Integrated Assessment Models," Working Papers hal-03192539, HAL.
    5. Okuducu, Mahmut Burak & Aral, Mustafa M., 2017. "Knowledge based dynamic human population models," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 1-11.
    6. Tao Chen & Muhammad Rizwan & Azhar Abbas, 2022. "Exploring the Role of Agricultural Services in Production Efficiency in Chinese Agriculture: A Case of the Socialized Agricultural Service System," Land, MDPI, vol. 11(3), pages 1-18, February.
    7. Grinin, Leonid & Grinin, Anton & Korotayev, Andrey, 2020. "A quantitative analysis of worldwide long-term technology growth: From 40,000 BCE to the early 22nd century," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    8. Boris M. Dolgonosov, 2020. "The Energy Representation of World GDP," Biophysical Economics and Resource Quality, Springer, vol. 5(3), pages 1-5, September.
    9. Luís Loures & Alejandro Chamizo & Paulo Ferreira & Ana Loures & Rui Castanho & Thomas Panagopoulos, 2020. "Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms," Sustainability, MDPI, vol. 12(9), pages 1-15, May.
    10. Aral, Mustafa M., 2020. "Knowledge based analysis of continental population and migration dynamics," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    11. Boris M. Dolgonosov, 2018. "A Conceptual Model of the Relationship Among World Economy and Climate Indicators," Biophysical Economics and Resource Quality, Springer, vol. 3(1), pages 1-15, March.

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