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The role of nature-like technologies in implementing sustainable development of industrial systems

https://doi.org/10.17073/2072-1633-2025-4-1530

Abstract

Under the conditions of sanctions restrictions, Russia’s modern macroeconomic system is aimed at technological development, digitalization, sustainable development and greening of production and economic systems. The key problem of the functioning of economic systems is the limited resources, which determines the emergence of “bottlenecks” and queues. As a result, this affects the efficiency of the entire system and its competitiveness. One of the key conditions for achieving the goals of sustainable development is the development of nature-like technologies and their introduction into industry. The purpose of the study is to identify the theoretical aspects of nature-like technologies, to identify their potential for implementation to achieve sustainable industrial development. The scientific origins of the concept of developing the topic of nature-like technologies are analyzed, and the categories “nature-like technologies”, “nature-inspired algorithms”, and “nature-inspired systems” are defined. The principles of nature-like technologies that contribute to solving environmental problems, resource independence, economic efficiency, and technological development are summarized. The transformation of traditional production systems into mesosystems by analogy with natural ecosystems is considered. It is proved that this approach, which underlies the closed-loop economy, allows us to go beyond the introduction of individual “green” technologies and achieve a synergistic effect. To systematize the analysis, a classification of nature-like technologies is proposed and the main directions of their use in industry are identified. The following conclusions are drawn: the introduction of nature-like technologies gives a measurable and significant effect in three areas of sustainable development: 1) economics – reducing costs, creating new value; 2) ecology – reducing emissions, optimizing resources; 3) social sphere – creating safe working conditions and a favorable environment for human life and health. The economic effect of the introduction of nature-like technologies is one of the key drivers of their scaling. When making managerial decisions, it is necessary to consider that the introduction of nature–like technologies is a strategic investment in ensuring the competitiveness of the mesosystem and its resource independence. At the level of the country’s top leadership, it is necessary to develop and implement incentive measures for projects that implement the principles of nature-like technologies at the system level.

About the Authors

N. V. Barsegyan
https://www.kstu.ru/emp_detail.jsp?id=35239
Kazan National Research Technological University
Russian Federation

Naira V. Barsegyan – PhD (Econ.), Associate Professor, Associate Professor of the Department of Logistics and Management, Kazan National Research Technological University.

68 Karl Marx Str., Kazan 420015, Republic of Tatarstan



A. I. Shinkevich
https://www.kstu.ru/emp_detail.jsp?id=1029066
Kazan National Research Technological University
Russian Federation

Aleksei I. Shinkevich – Dr.Sci. (Econ.), Dr.Sci. (Eng.), Professor, Head of the Department of Logistics and Management, Kazan National Research Technological University.

68 Karl Marx Str., Kazan 420015, Republic of Tatarstan



F. F. Galimulina
https://www.kstu.ru/emp_detail.jsp?id=11030
Kazan National Research Technological University
Russian Federation

Farida F. Galimulina – Dr.Sci. (Econ.), Associate Professor, Professor of the Department of Logistics and Management, Kazan National Research Technological University.

68 Karl Marx Str., Kazan 420015, Republic of Tatarstan



References

1. Вернадский В.И. Научная мысль как планетное явление. Отв. ред. А.Л. Яншин. М.: Наука; 1991. 270 с.

2. Kaznacheev V. P.V. I.Vernadsky’s Theory of the Noosphere in Relation to Contemporary Human Ecology Issues. Noospheric Studies. 2023;4:6–16. (In Russ.). https://doi.org/10.46724/NOOS.2023.4.06-16

3. Benyus J.M. Biomimicry: Innovation Inspired by Nature. New York: William Morrow; 1997. 288 p.

4. McDonough W., Braungart M. Cradle to Cradle: Remaking the Way We Make Things. New York: North Point Press; 2002. 193 p.

5. Chin M.H.W., Linke J., Coppens M.-O. Nature-inspired sustainable medical materials. Current Opinion in Biomedical Engineering. 2023;28:100499. https://doi.org/10.1016/j.cobme.2023.100499

6. Dao V.-D., Nguyen H.T.K. Nature-inspired design for high-efficiency solar-driven water evaporation. Journal of Power Sources. 2024;609:234676. https://doi.org/10.1016/j.jpowsour.2024.234676

7. Gaitan N.C., Ungurean I., Corotinschi G., Roman C. An intelligent energy management system solution for multiple renewable energy sources. Sustainability. 2023;15(3):2531. https://doi.org/10.3390/su15032531

8. Bertaglia T., Costa C.M., Lanceros-Méndez S., Crespilho F.N. Eco-friendly, sustainable, and safe energy storage: a nature-inspired materials paradigm shift. Materials Advances. 2024;5(19):7534–7547. https://doi.org/10.1039/d4ma00363b

9. Meshalkin V., Akhmetov A., Lenchenkova L., Nzioka A., Politov A., Strizhnev V., Telin A., Fakhreeva A. Application of renewable natural materials for gas and water shutoff processes in oil wells. Energies. 2022;15(23):9216. https://doi.org/10.3390/en15239216

10. Selvam D.C., Devarajan Y. Bio-inspired hybrid materials for sustainable energy: Advancing bioresource technology and efficiency. Materials Today Communications.2025;46:112647. https://doi.org/10.1016/j.mtcomm.2025.112647

11. Oguntona O. Developing a nature-inspired sustainability assessment tool: The role of materials efficiency. Materials Proceedings. 2025;22(1):3. https://doi.org/10.3390/materproc2025022003

12. Lebdioui A. Nature-inspired innovation policy: Biomimicry as a pathway to leverage biodiversity for economic development. Ecological Economics. 2022;202:107585. https://doi.org/10.1016/j.ecolecon.2022.107585

13. Prathumrat P., Likitaporn C., Rimdusit S., Nikzad M., Wongsalam T., Tanalue N., Okhawilai M. Deep Insights into the design of next-generation water-based stimuli-responsive shape memory polymers: From fundamentals to nature-inspired innovations. Applied Materials Today. 2025;44:102754. https://doi.org/10.1016/j.apmt.2025.102754

14. Huang Z., Hwang Y., Radermacher R. Review of nature-inspired heat exchanger technology. International Journal of Refrigeration. 2017;78:1–17. https://doi.org/10.1016/j.ijrefrig.2017.03.006

15. Xu Y., Zhang Q., Liang Y., Huang L. A review of solar interfacial distillation water purification technology inspired by nature. Journal of Water Process Engineering. 2023;55:104156. https://doi.org/10.1016/j.jwpe.2023.104156

16. Gertsen M.M., Perelomov L.V., Arlyapov V.A., Atroshchenko Y.M., Meshalkin V.P., Chistyakova T.B., Reverberi A.P. Degradation of oil and petroleum products in water by bioorganic compositions based on humic acids. Energies. 2023;16(14):5320. https://doi.org/10.3390/en16145320

17. Trubetskoi K.N., Galchenko Y.P. Naturelike mining technologies: Prospect of resolving global contradictions when developing mineral resources of the lithosphere. Herald of the Russian Academy of Sciences. 2017;87:378–384. https://doi.org/10.1134/S1019331617040050

18. Quintero A., Zarzavilla M., Tejedor-Flores N., Mora D., Chen Austin M. Sustainability assessment of the anthropogenic system in panama city: Application of biomimetic strategies towards regenerative cities. Biomimetics. 2021;6(4):64. https://doi.org/10.3390/biomimetics6040064

19. Musango J.K., Currie P., Robinson B. Urban metabolism for resource efficient cities: From theory to implementation. Paris: UN Environment; 2017.

20. Tan X., Jiao J., Jiang M., Chen M., Wang W., Sun Y. Digital policy, green innovation, and digital-intelligent transformation of companies. Sustainability. 2024;16(16):6760. https://doi.org/10.3390/su16166760

21. Luo S., Yimamu N., Li Y., Wu H., Irfan M., Hao Y. Digitalization and sustainable development: How could digital economy development improve green innovation in China? Business Strategy and the Environment. 2023;32(4):1847–1871. https://doi.org/10.1002/bse.3223

22. Zhu Y., Zhang H., Siddik A.B., Zheng Y., Sobhani F.A. Understanding corporate green competitive advantage through green technology adoption and green dynamic capabilities: Does green product innovation matter? Systems. 2023;11(9):461. https://doi.org/10.3390/systems11090461

23. Yang J.Y., Roh T. Open for green innovation: From the perspective of green process and green consumer innovation. Sustainability. 2019;11(12):3234. https://doi.org/10.3390/su11123234

24. Sun Y., Sun H. Green innovation strategy and ambidextrous green innovation: The mediating effects of green supply chain integration. Sustainability. 2021;13(9):4876. https://doi.org/10.3390/su13094876

25. Ozgul B. Does green transformational leadership develop green absorptive capacity? The role of internal and external environmental orientation. Systems. 2022;10(6):224. https://doi.org/10.3390/systems10060224

26. Shinkevich A.I., Barsegyan N.V., Galimulina F.F. Measuring and forecasting the development concept of the “Green” macrosystem using data analysis technologies. Sustainability. 2024;16(24):11152. https://doi.org/10.3390/su162411152

27. Momenikorbekandi A., Kalganova T. Intelligent scheduling methods for optimisation of job shop scheduling problems in the manufacturing sector: A systematic review. Electronics. 2025;14(8):1663. https://doi.org/10.3390/electronics14081663

28. Rao R.V., Davim J.P. Single, multi-, and many-objective optimization of manufacturing processes using two novel and efficient algorithms with integrated decision-making. Journal of Manufacturing and Materials Processing. 2025;9(8):249. https://doi.org/10.3390/jmmp9080249

29. Galimulina F.F., Barsegyan N.V. Application of mass service theory to economic systems optimization problems – A review. Mathematics. 2024;12:403. https://doi.org/10.3390/math12030403

30. Zhang L., Hu Y., Tang Q., Li J., Li Z. Data-driven dispatching rules mining and real-time decision-making methodology in intelligent manufacturing shop floor with uncertainty. Sensors. 2021;21(14):4836. https://doi.org/10.3390/s21144836

31. Bányai T. Optimization of material supply in smart manufacturing environment: A metaheuristic approach for matrix production. Machines. 2021;9(10):220. https://doi.org/10.3390/machines9100220

32. Massim Y., Yalaoui F., Chatelet E., Yalaoui A., Zeblah A. Efficient immune algorithm for optimal allocations in series-parallel continuous manufacturing systems. Journal of Intelligent Manufacturing. 2012;23:1603–1619. https://doi.org/10.1007/s10845-010-0463-7

33. Para J., Del Ser J., Nebro A.J. Energy-aware multi-objective job shop scheduling optimization with metaheuristics in manufacturing industries: A critical survey, results, and perspectives. Applied Sciences. 2022;12(3):1491. https://doi.org/10.3390/app12031491

34. Gao K., Huang Y., Sadollah A., Wang L. A review of energy-efficient scheduling in intelligent production systems. Complex & Intelligent Systems. 2020;6:237–249. https://doi.org/10.1007/s40747-019-00122-6

35. Islam J., Mamo Negash B., Vasant P.M., Ishtiaque Hossain N., Watada J. Quantum-based analytical techniques on the tackling of well placement optimization. Applied Sciences. 2020;10(19):7000. https://doi.org/10.3390/app10197000

36. Kumar A., Jaiswal A. A deep swarm-optimized model for leveraging industrial data analytics in cognitive manufacturing. IEEE Transactions on Industrial Informatics. 2021;17(4):2938–2946. https://doi.org/10.1109/TII.2020.3005532

37. Guneshwor L., Eldho T.I., Vinod Kumar A. Identification of groundwater contamination sources using meshfree rpcm simulation and particle swarm optimization. Water Resources Management. 2018;32:1517–1538. https://doi.org/10.1007/s11269-017-1885-1

38. Goulart D.A., Pereira R.D. Autonomous pH control by reinforcement learning for electroplating industry wastewater. Computers & Chemical Engineering. 2020;140:106909. https://doi.org/10.1016/j.compchemeng.2020.106909

39. Sakharov M., Koledina K., Gubaydullin I., Karpenko A. Studying the efficiency of parallelization in optimal control of multistage chemical reactions. Mathematics. 2022;10(19):3589. https://doi.org/10.3390/math10193589

40. Chertow M.R. Industrial symbiosis: Literature and taxonomy. Annual Review of Energy and the Environment. 2000;25(1):313–337. https://doi.org/10.1146/annurev.energy.25.1.313

41. Jacobsen N.B. Industrial symbiosis in Kalundborg, Denmark: A quantitative assessment of economic and environmental aspects. Journal of Industrial Ecology. 2006;10(1-2):239–255. https://doi.org/10.1162/108819806775545411

42. Gamage A., Dayaratne R. Learning from nature: towards a research-based biomimicry approach to ecologically sustainable design (ESD). Conference: Sustainability through biomimicry: Discovering a world of solutions inspired by nature: College of Design, Dammam University. 2012;17.

43. Benachio G.L.F., Freitas M.C.D., Tavares S.F. Circular economy in the construction industry: A systematic literature review. Journal of Cleaner Production. 2020;260:121046. https://doi.org/10.1016/j.jclepro.2020.121046


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For citations:


Barsegyan N.V., Shinkevich A.I., Galimulina F.F. The role of nature-like technologies in implementing sustainable development of industrial systems. Russian Journal of Industrial Economics. 2025;18(4):486-498. (In Russ.) https://doi.org/10.17073/2072-1633-2025-4-1530

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ISSN 2072-1633 (Print)
ISSN 2413-662X (Online)