Ecosystems Without Borders 2023: Opportunities and Challenges (Lecture Notes in Networks and Systems) 303134328X, 9783031343285

Citation preview

Lecture Notes in Networks and Systems 705

Ruslan Polyakov   Editor

Ecosystems Without Borders 2023 Opportunities and Challenges

Lecture Notes in Networks and Systems Volume 705

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

Ruslan Polyakov Editor

Ecosystems Without Borders 2023 Opportunities and Challenges

Editor Ruslan Polyakov Kaliningrad State Technical University Kaliningrad, Russia

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-34328-5 ISBN 978-3-031-34329-2 (eBook) https://doi.org/10.1007/978-3-031-34329-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Ecosystem development is a complex, multi-dimensional process that requires the interaction of different scientific disciplines as well as cooperation between different countries and cultures. Ecosystems Without Borders 2023—Opportunities and Challenges in your hands is a collection of papers delivered at the Second International Conference “Ecosystems Without Borders: Opportunities and Challenges” held at Kaliningrad State Technical University in February 2023. The materials include studies reflecting the transformation of ecosystems, both in innovative development and in science, technology and business, as well as the spatial aspects of ecosystems and features of the formation of a creative class in ecosystem conditions. In addition, the book includes theoretical articles that aim to implement the ideas of sustainable development and circular economy. The book is intended for a wide range of readers, including practicing economists, students, graduate students and researchers, as well as government officials and company managers. It can serve as an important source of information and knowledge that will lead to scientific and innovative advances in the sustainable development of society. We thank everyone who contributed to this book, including paper authors, session presenters, section chairs, Program Committee members, volunteers, as well as reviewers and sponsors. Special thanks go to the conference organizers, without whose help this book would not have been possible. We hope that this book will serve as an important tool for advancing the science and practice of ecosystems and sustainable development, and will benefit readers. Kaliningrad, Russia April 2023

Ruslan Polyakov

v

Contents

Theoretical Aspects of Ecosystems The Imperatives of the Green Economy and Their Relationship to the Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatyana Stepanova and Olga Schneider

3

Actual Problems of Circular Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey V. Bank, Viktor V. Shnaider, Tatiana B. Izzuka, Natalya S. Mayer, and Natalya O. Mikhalenok

9

Analysis of Ecosystem Business in Strategic Development . . . . . . . . . . . . . Natalia Nikiforova, Yuri Putikhin, Dmitry Shlychkov, and Veronika Frolova

16

The Political Economy of Digital Platforms: Postindustrial Paradigm Against Self-replicating Market Failures . . . . . . . . . . . . . . . . . . . Andrey Koshkin, Matiar Rakhman Khashimi, Andrei Fedorov, Sofia Protasova, and Kirill Kirilichev Ecosystem-Based Approach to Assessing the Impact of Climate Change on Fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Albert Mnatsakanyan and Alexander Kharin

25

33

Spatial Ecosystems Entrepreneurship Developmentin the Old Industrial Cities of Russia Based on the Ecosystem Approach . . . . . . . . . . . . . . . . . . . . . . . . . Olga Akimova, Margarita Kozhukhova, and Daniil Frolov Industrial Clusters and the Process of Their Self-organization . . . . . . . . . Ruslan Polyakov and Olga Brizhak

45 60

vii

viii

Contents

Business Ecosystems as Innovative Models for the Development of Modern Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina Kharlamova, Irina Ezangina, Irina Chekhovskaya, and Sergey Sazonov Modeling Method in Ecological and Economic System Development of a Transport Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Julia Tagilceva, Elena Kuzina, Marina Vasilenko, Leonid Limanchuk, and Evgeny Nazarov The Ecological and Economic Transport System Influence on the Non-transport Effect Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina Vasilenko, Elena Kuzina, Julia Tagilceva, Pavel Nadolinsky, and Marina Kuzina The Russian Federation is on Its Way to a Climate-Neutral Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergei Muzalev, Tatiana Muzaleva, Marina Gordova, and Irina Demina

73

83

92

99

Towards Scalable Consortium-Based Organic Agri-Food Systems . . . . . . 112 Denis Galkin Innovation Ecosystems Analytical Review of the Formation of Key Performance Indicators of Development Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Ruslan Polyakov and Elena Nikiforova Shaping Digital Ecosystem of the Eurasian Economic Union: Issues and Resolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Sergey Kamolov and Sofia Glazyeva A Compositional Approach to Labor Potential Evaluation and a Neural Network Model for Its Forecasting . . . . . . . . . . . . . . . . . . . . . 140 Oksana Ogiy and Vasiliy Osipov The Use of Neural Network Technology in Bank Digital Ecosystems . . . . 154 Alexey Snytnikov, Marina Solovey, and Larisa Zelenina The Concept of Digital Transformation in Educational Discourse . . . . . . 163 Irina Guseva and Elena Pliva Risk Management in the System of Economic Security of the Enterprise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Tatiana Tarasova and Artem Krivtsov The Specifics of Determining the Value of Segments of Digital Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Vladislav Podtopelny and Alina Babaeva

Contents

ix

Russian Banks’ Monetary-Targeting and Investment Strategies in the Context of Elevated Uncertainty of Financial Markets . . . . . . . . . . . 197 Elena Gordeeva The Global Practice of Implementation and Use of Digital Currencies of Central Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 Olga Kuzmina, Maria Konovalova, and Tatyana Stepanova Measures of Information Use Quality for Changing Activity Success in Agricultural Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Alexander Geyda Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Theoretical Aspects of Ecosystems

The Imperatives of the Green Economy and Their Relationship to the Ecosystem Tatyana Stepanova1(B)

and Olga Schneider2

1 Kaliningrad State Technical University, Kaliningrad, Russia

[email protected] 2 Financial University Under the Government of the Russian Federation, Moscow, Russia

Abstract. The importance of the issues of “green economy” is due to the development and requirement of the modern world community. The turbulent state of the economy determines the search for new management mechanisms for the use of natural resources of our planet and increasingly, leading experts in this field turn to scientific research processes of formation of a “green” economy at the level of global importance. The development vector of “green economy” is aimed not only at achieving the goals of sustainable development, it is focused on responsible consumption and production. “Green” economy is essentially a litmus test of sustainable development of the world community of the present time. The relevance and significance of green economy aspects is undeniable. Changes of socio-economic and ecological significance necessitate systematic overcoming of emerging problems. Acute issues of waste of the country’s natural resources more and more come to the surface of the problems that need to be solved immediately. The dominance of a commercialized approach to doing business by many economic entities has relegated issues of social and environmental importance to secondary tasks, which is currently unacceptable, since the financial and economic activities of such entities have a negative impact on the environment and the value of non-renewable natural resources. The policy of “green economy” improves the welfare of the population of our planet, is socially oriented and focused on the preservation of the environment. Keywords: Analysis · Green economy · Natural resources · Sustainable development · Performance efficiency

1 Introduction The world community of today is on the threshold of global socio-economic and environmental problems. System solution of these problems will ensure sustainable development of all countries of the world. Renewability of natural resources, recycling of waste and secondary raw materials, the use of alternative fuels are the objects of attention of the “green economy” policy, which aims for transparency and predictability of economic relations for all participants in this process. The development of society in this case will be rapid, but systemic, which predetermines the need for a new financial system focused on “green” investments and ensuring the balance, as well as the sustainability © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 3–8, 2023. https://doi.org/10.1007/978-3-031-34329-2_1

4

T. Stepanova and O. Schneider

of the global space. “Investment is an important tool supporting the transition to a green economy” [1]. The European Union’s development directions in the field of green economy are enshrined in the Action Plan to Support Recovery and Transformation. According to this plan, the future society must be guided by digitalization and sustainable development through green investments to achieve “climate neutrality” in 2050. “According to the European Commission, the EU’s sustainable growth strategy will lead to 2 percent additional gross domestic product by 2024 and create 2 million jobs, including by accelerating the green and digital transition.” [2].

2 Methods The concept of sustainable development, focused on the balance between the economy, social development of society and the environment contributes to the development of a “green” economy. The interaction of the world community through economic, political instruments contributes to strengthening the elements of sustainable development, allowing to reduce the negative impact on the environment. As we know, the principles of a closed economy strengthen the processes of functioning of a “green” economy through resource efficiency, which is determined by rational and efficient consumption and production, reduction of waste and emissions into the atmosphere and discharge of technical water. “The green economy includes the so-called low-carbon economy, aimed at reducing greenhouse gas emissions into the atmosphere to achieve carbon neutrality, as defined by the Paris Agreement” [3]. Current issues of “green” economy are considered by leading Russian and foreign experts in this field [4, 5]. Important aspects of sustainable development are in the field of heated discussion and are disclosed in the scientific articles of many scientists [6–14].

3 Results and Discussion At present, the world community in most of its part is in the plane of the linear economy and partially transformed into the directions of the closed economy, which is defined by the stages of formation (Fig. 1). The controversial aspects of the formation and implementation of a green policy have led to a comprehensive assessment of the global, national and regional development strategy in relation to this policy in terms of reflecting the possible positive and negative changes. Priority issues to be solved operationally should be issues related to adequate regulation, continuous monitoring and comprehensive assessment of green policy initiatives. Scientific research on the methodology of green policy formation should not be neglected. Control activities over unregulated waste disposal sites in this aspect should take the leading place. Timely notification of the population about threats of ecological character will allow achieving prevention of man-made disasters. In this regard, the benefits of the transition to the principles of green economy are undeniable (Fig. 2). However, it is worth paying attention to the fact that the outlined advantages cannot be realized without attracting financial resources, innovative technologies, system procedures of ESG-compensation and audit, improvement of risk management, as well as targeted “green” investments. At the current stage of society development an acute problem

The Imperatives of the Green Economy …

5

Fig. 1. Stages of formation of the closed-loop economy (compiled by the authors on the materials of [15])

is the reorientation of the platform of linear economy to the platform of cyclic economy, ensuring sustainable development in the present and future development. The development and implementation of “green” financial instruments through various development institutions is required. “Green” financing is provided by internalization of ecological aspects and minimization of risks of ecological significance. In this regard, it is necessary to note the acute shortage of public and private investment in the development and reorientation of production in the direction of the “green” economy. The green economy development strategy should be based on a number of production and financial directions (Fig. 3). The Russian Federation has sufficient reserves of natural resources and their careful and rational use is possible with an adequate response of society to accelerate entry into the “green” economy. Discussion and the urgent need for this is defined in the scientific works of leading experts in the field of circular economy, the problems of formation of a closed-loop economy in the Russian Federation [16]. However, the definition of goals, objectives and prospective strategies of “green” economy reveal problems of instrumental and informational and methodological nature, which opens the field of scientific research. Short-term tasks and identified problems of green economy strengthen the importance of addressing environmental, economic, socio-political aspects, which in turn will help to reduce energy intensity, anthropogenic and technogenic impact on the environment, strengthening environmental protection measures, regulatory and legal regulation

6

T. Stepanova and O. Schneider

Fig. 2. Predominant principles of the green economy (compiled by the authors on the basis of [15])

Fig. 3. Production and financial directions of the green economy development strategy (compiled by the authors)

The Imperatives of the Green Economy …

7

and attraction of “green” financial resources, as well as methodology for implementing this policy both in our country and in the world as a whole. A powerful incentive for the green economy is the high demand for environmentally friendly and safe products from any industry. Industries, in turn, are focused on reducing the negative impact on the environment. In addition, the policy of green economy defines the directions of continuous monitoring and analysis of processes, factors and conditions necessary to justify the vector of sustainable development, the essence, structure and functions of the mechanism to stimulate greening not only the Russian, but also the world economy as a whole. As part of the concept of sustainable development, the world community is increasingly paying attention to environmental culture and public awareness of the consequences of non-sustainable consumer behavior and the benefits of responsible choices. According to the FiBL study, the Russian Federation belongs to the countries which are in the process of formation of legislation in the field of green policy. In this regard, the products of the agricultural sector and other sectors of the Russian economy are inferior in terms of demand for organic products, which causes the growth of serious problems in marketing and sales of products and their low competitiveness against the world leaders of organic products. However, it should be noted that the Russian Federation is currently taking strict measures to ensure environmental security, strengthening the development directions in the field of “green economy”, stimulates the growth of environmental awareness.

References 1. Varavin, E.V., Kozlova, M.V., Makoveckij, M.Y.: Development of environmentally responsible investment: implementation of foreign experience for Kazakhstan. Cent. Asian Econ. Rev. 4(139), 52–63 (2021) 2. Action Plan to support recovery and transformation. https://www.eur-lex.europa.eu/legal-con tent/en/TXT/?qid=1600708827568&uri=CELEX%3A52020DC0575. Last accessed 10 Oct 2022 3. An agreement within the framework of the UN Framework Convention on Climate Change regulating measures to reduce carbon dioxide in the atmosphere from 2020. The agreement was prepared to replace the Kyoto Protocol during the Climate Conference in Paris and adopted by consensus on December 12, 2015, and signed on April 22, 2016 4. Braam G., Ewen D., Ossenblok H., Toxopeus H., Maas k.: A roadmap for a circular business model paperback, June 11, 172 p. https://www.amazon.com/Circular-Route-Roadmap-Bus iness-Model/dp/9463012052 (2018). Last accessed 10 Oct 2022 5. Davydova, T.E., Popova, A.I., Raspopova, A.E.: Green economy in the context of global sustainable development. Ekonominfo 1, 49–54 (2020) 6. Lipinsky, D.A., Berdnikova, L.F., Schnaider, O.V.: Specific features of training of law makers with the help of remote technologies. Lect. Notes Netw. Syst. 87, 606–611 (2020) 7. Petrov, A.M., Nikiforova, E.V., Kiseleva, N.P., Grishkina, S.N., Lihtarova, O.V.: Creation of the reporting on sustainable development of companies based on socioeconomic measurement statistics. Int. J. Recent. Technol. Eng. 8(2), 4005–4012 (2019)

8

T. Stepanova and O. Schneider

8. Efimova O.V., Nikiforova E.V., Basova M.M., Shnaider O.V., Ushanov I.G.: Practice of nonfinancial reporting disclosure by Russian companies: bridging the gap between company disclosures on sustainability and stakeholders’ needs. In the collection: ACM International Conference Proceeding Series. Proceedings of the 5th International Conference on Engineering and MIS, ICEMIS, p. 11 (2019) 9. Bulyga, R.P., Nikiforova, E.V., Safonova, I.V.: Indicators of the universities control activities. Int. J. Innov. Technol. Explor. Eng. 9, 1409–1415 (2019) 10. Muzalev, S.V., Reshetov, K.Y.: Food security of Russia: problems and perspectives of sustainable development studies in systems. Decis. Control. 282, 495–502 (2020) 11. Rakhmanova, M.S., Schneider, V.V.: Modern status of small enterprise development prospects and problems in Russia. Amaz. Investig. 14, 61–67 (2018) 12. Tolmachev M.N., Latkov A.V., Mitrofanov A.Y., Barashov N.G.: (2021) Economic dynamics of Russia: approach based on the solow-swan model. In the collection: Proceeding of the International Science and Technology Conference “FarEastSon 2020”. Singapore: 1063–1072 (2021) 13. Tolmachev, M., Tsypin, A., Barashov, N.: Statistical study of dynamics of the agricultural production of post-soviet countries in the context of food security. Smart Innov., Syst. Technol. 172, 699–711 (2020) 14. Varavin E.V., Makovetsky M.Yu., Komarova A.S.: Problems of ensuring transition to a closedcycle economy. Bull. S.Y. Witte Mosc. University. Ser. 1 Econ. Manag. 1(40), 42–51 (2022) 15. Shakirova A.V., Nikulina S.N.: Closed cycle economics in Russia. Collection of scientific papers based on the materials of the XVII International Scientific Conference. Publishing house: SIC “L-Magazine” 15–21 (2019)

Actual Problems of Circular Economy Sergey V. Bank1 , Viktor V. Shnaider2(B) , Tatiana B. Izzuka2 , Natalya S. Mayer2 , and Natalya O. Mikhalenok3 1 Russian Technological University, Moscow, Russia 2 Financial University Under the Government of the Russian Federation, Moscow, Russia

[email protected] 3 Samara State Transport University, Russian Federation, Samara, Russia

Abstract. The actuality of the problems of circular economy is increasing every day. The need for the transition of production and consumption of resources into a cyclical pattern is determined by the rapid development of society, the increase in CO2 emissions and resource limitations. The vector of development of the circular economy is meeting the needs of society through renewable assets or resources that have passed the stages of production and consumption, and have transformed into waste. The main goal of the circular economy—cyclic economy is increasing the terms of use and safety of resources, materials in production and the economy through a closed production cycle, namely through the processing of both the resource itself and waste after its consumption. This approach is achieved by the interconnection of various industries in industrial symbiosis, where waste from one industry is used for its own purposes or in the production cycles of another economic entity. In this case, the primary production technologies are aimed at the possibility of recovering waste into a resource, despite the fact that waste is involved in the process of downcycling. However, downcycling is often used as a starting point for upcycling, and on the basis of recycled material, which is of lower quality than the original material, it is possible to obtain a product with new functionality if it has been creatively transformed to meet quality requirements. Keywords: Circular economy · Production process · Resources · Technical development · Consumption · Waste · Recycling

1 Introduction The rapid technical development of society entails an increase in the consumption of resources and natural wealth of our planet. Nowadays the irrationality of production and consumption of the resources is being more and more determined. The presumably shortterm outlook for the development of the world community in ten years is conditioned by a jump in demand for resources and an increase in their production. In the long term, there is a horizon of threats to the ecosystem of our planet and the future human generation. All the above determines the demand for the transition from classical linear production to a cyclic production process, highlighting the technical and biological processes according to their specifics, but at the same time being in a unified production process for the use of resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 9–15, 2023. https://doi.org/10.1007/978-3-031-34329-2_2

10

S. V. Bank et al.

“The term “circular economy” (“closed-loop economy” or “cyclic economy” also often occur in parallel with “circular economy”)—in general means an economy based on renewal of resources and acting as an alternative to the traditional linear economy (creation, use, waste disposal)” [1]. The regulation of the circular economy in the transition from a linear economic model requires systemic changes, both in terms of business approaches and in terms of basic habits and thinking, which confirms the relationship between the population of the country, the business environment, and the state as a whole. The population puts forward its requirements and forms the demand for ecological products; business in order to meet the requirements of the population, reproduces the required products, using new technologies and innovative production. In its turn, being an important structural element of the circular economy chain, the state needs to create a legal framework that ensures the development of business and human life level, through the creation of institutional conditions.

2 Methods and Materials Currently, the European Union is considered the leader in the direction of the closedloop economy. Adhering to the Circular Economy Concept, the European Union directs the development of the economy towards the long-term use of resources through their recovery and return to the production process, thereby ensuring the preservation of natural resources and their saving, while the volume of waste is reduced to a minimum. The priority of the closed-loop economy and approaches to its formation is determined by the need to restore natural resources and recycle them to increase their life cycle and rational use. The principles of the circular economy are maximizing the use of natural resources, minimizing production waste, and, if possible, recycling, which allows upcycling to appear. The main goals of the closed-loop economy should be considered: reducing the level of resource consumption through their competent and rational use; upcycling expansion. Using the methods of motivating the population to sort waste from the experience of foreign countries, it is possible to strengthen the directions for the rational use of resources and waste recycling. It is necessary to develop a “road” map to solve the problems of waste processing with phased implementation. It is rational to reduce the level of consumption of raw materials as the first stage. The second step can be reducing the level of waste generation. At the third stage—the stage of utilization, attention should be paid to the emergence of a secondary resource and its possibility of being involved in the production process of the cyclic economy. Undoubtedly, the circular economy in its development will contribute to the emergence of new and demanded industries in terms of processing secondary raw materials and waste, while creating new jobs, which will affect the employment and well-being of the country’s population.

Actual Problems of Circular Economy

11

3 Discussion The popularization of the closed-loop economy is determined by the need of society to slow down the process of changing the ecosystem of our planet. However, the trends in the development of the circular economy in the countries of the world space are ambiguous. Based on the Circular Economy Action Plan, adopted in 2015, a Strategy for plastics and many production resources is defined, initiating an increase in the use of secondary raw materials and materials in the production process [2]. The circular economy strategy has its own economic benefits, namely: reduction of production risks due to a decrease in the level of imports of raw materials, economical water and energy resources consumption, adaptation of the industrial sector to the realities and requirements of the present and future periods of development. The European Union, being a leader in promoting the circular economy, determined the directions of its policy aimed at constructively changing the production processes of a number of leading industries (Fig. 1).

Fig. 1. Priority sectoral directions of the circular economy (compiled by the authors based on materials [3])

However, it is worth paying attention to the fact that the growing exorbitant scale of the use of natural resources of the present and their growth are leading the humanity of our planet to disrupt the functioning of the living ecosystem, which makes it important to study the issues of the circular economy in conjunction with the present realities and the prospective development of not only a single country, but also the world space as a whole.

4 Research Results Controversy in the creation of effective models of the circular economy is present on scientific platforms of world importance. Thus, the Ellen MacArthur Foundation considers the circular economy through the division of the structure of the circular economy into two cycles: biological and technical (Figs. 2 and 3).

12

S. V. Bank et al.

Fig. 2. Biological cycles (compiled by the authors based on materials [4])

Fig. 3. Technical cycles (compiled by the authors based on materials [4])

As a catalyst in the overall Concept of Sustainable Development, the circular economy ensures the preservation of the environment. The need to develop a circular economy in the Russian Federation is undeniable. Possessing a huge supply of natural resources, it is necessary to treat them carefully and learn to control the rationality of their use. In

Actual Problems of Circular Economy

13

the scientific works of leading experts in the field of the circular economy, questions, tasks and problems of the formation of a circular economy in the Russian Federation are identified [5]. The main trends of a closed cycle in foreign countries are disclosed in the scientific works of P. Nosko [6]; E. Mikhalenko, D. Klimova, I. Mankovsky [7] and others [8–10]. From the point of view of sustainable development, issues of social importance are not identified in the circular economy. Focusing on human consumption of certain products that meet the requirements and quality, not enough attention is paid to “how the transition to a circular economy can affect public health, primarily in relation to harmful chemicals, water reuse, disposal of electrical and electronic waste and distribution effects between population groups” [11–13]. In the Russian Federation, issues related to the closed-loop economy are considered a high priority and their resolution should be prompt. The urgency of addressing these acute issues is due to the fact that the country’s economic development is not feasible without using resources that have the property of being finite or depletable. Therefore, the main task of the closed-loop economy is to ensure the livelihoods of society as a whole and individual economic entities by relying on efficient resource consumption, primarily through rational reduction of primary resources and their replacement with secondary recycled resources, while maintaining product quality. Solving important issues related to the closed-loop economy should not only take place at the federal level, but also be promptly addressed at the regional, municipal, and global levels. To address the important issue of closed-loop or cyclical economics, society’s efforts must be directed towards extending the lifespan of resources and materials used in production by means of a closed production cycle, achieved through the recycling of both the resource itself and the waste generated after its consumption. The sustainability of resources will be an accompanying issue in solving these problems, which will require controlling the use of each type of resource. Undoubtedly, all aspects of the closed-loop economy involve responsible and rational treatment of nature, as well as the potential for reusing waste in production. The positive effects of this direction are reinforced by the possibility of increasing the amount of recycled products, reducing the negative effects of landfills, and having a positive impact on the environment and surroundings. However, it should be noted that managing the closed-loop economy must be accompanied by accurate information on the amount of waste generated and their impact on the environment and human health. Certainly, there are still a lot of questions and problems to solve the implementation of the closed-loop economy today. The framework definition of the tasks of the circular economy reveals the problems of methodological, instrumental and informational definition of this direction, which opens the field for discussion and consensus of scientists and practitioners in this field.

5 Conclusion In this context, it should be noted that the acute problem in the relevant instrumentation of standards, principles and criteria must be solved in the short term, as environmental problems increase the risks associated with the environment, economy and socio-political orientation.

14

S. V. Bank et al.

Systematization and clarification of the theoretical and practical aspects of the closed cycle economy must correlate with the primary issues of sustainable development. The close connection between the closed cycle economy and sustainable development is traced in the rational use of primary natural resources and their conservation based on the application of innovative technologies and advanced scientific and technical developments. The closed cycle economy provides interaction between society and individuals through the rationality of production processes, taking into account the efficient use of primary natural resources and the consumption of secondary raw materials, thereby reducing the negative impact on the environment and increasing resource efficiency. The importance of the development directions of the closed cycle economy is also traced in the increased need for processing human and societal waste. Effective waste management is currently an important problem for the global community, requiring improvement in the field of international environmental policy and priority aspects of sustainable development as a separate economic entity and the state as a whole. The timely achievement of the goals of sustainable development is currently ensured through the development of information technologies, which are the foundation for building a closed cycle economy and a green economy aimed at increasing the environmental safety of the global space as a whole and making a significant contribution to the concept of sustainable development.

References 1. Ye.V. Varavin, M. Yu. Makovetsky, A.S. Komarova.: The Problems of Ensuring the Transition to Circular Economy. Bulletin of the Moscow Witte University Series 1. Economics and Management 1(40): 42–51 (2022) 2. A European Strategy for Plastics in a Circular Economy (brochure), http://ec.europa.eu/env ironment/circular-economy/pdf/plastics-strategy-brochure.pdf. Last accessed 08 Oct, 2022 3. P.A. Nosko.: Trends in the circular economy development in the European Union. Russian Journal of Resources, Conservation and Recycling 1(6). Available at: https://resources.today/ PDF/04ECOR119.pdf (in Russian). DOI: https://doi.org/10.15862/04ECOR119 4. The Ministry of Economic Development of Russia has prepared a Review of international approaches to the closed cycle economy, https://www.economy.gov.ru/material/departments/ d30/obzory_i_analitika/minekonomrazvitiya_rossii_podgotovil_obzor_mezhdunarodnyh_p odhodov_po_ekonomike_zamknutogo_cikla.html. Last accessed 03 Oct 2023 5. Shakirova, A.V., Nikulin, S.N.: Circular economy in Russia. Collection of scientific papers based on materials of the XVII international scientific conference. Publisher: Research Center “L-Journal”: 15–21 (2019) 6. Nosko P.A. (2019). Trends in the circular economy development in the European Union. Russian Journal of Resources, Conservation and Recycling. Available at: https://resources. today/PDF/04ECOR119.pdf (in Russian). DOI: https://doi.org/10.15862/04ECOR119 7. Mikhalenko, E., Klimova, D., Mankovsky, I.: Circular economy as a model of the economy of the future. Bank. Bull. 12(689), 42–51 (2020) 8. End-of-Life Vehicle Recycling: State of the Art of Resource Recovery from Shredder Residue, https://publications.anl.gov/anlpubs/2011/02/69114.pdf. Last accessed 09 Oct 2022 9. Recycling for Profit: The New Green Business Frontier, https://hbr.org/1993/11/recyclingfor-profit-the-new-green-business-frontier. Last accessed 09 Jan 2022

Actual Problems of Circular Economy

15

10. Waste Management in Germany 2018 Facts, data, diagrams, https://www.bmu.de/fileadmin/ Daten_BMU/Pools/Broschueren/abfallwirtschaft_2018_en_bf.pdf. Last accessed 19 Aug 2022 11. Tolmachev, M.N., Latkov, A.V., Mitrofanov, A.Y., Barashov, N.G.: Economic dynamics of Russia: approach based on the Solow-Swan model in the collection. In: Proceeding of the International Science and Technology Conference “FarEastSon 2020”. Singapore: 1063–1072 (2021) 12. Tolmachev, M., Tsypin, A., Barashov, N.: Statistical study of dynamics of the agricultural production of post-Soviet countries in the context of food security. Smart Innov. Syst. Technol. 172, 699–711 (2020) 13. Circular economy and health: opportunities and risks. Copenhagen: World Health Organization. Regional Office for Europe (2019). License: CC BY-NC-SA 3.0 IGO

Analysis of Ecosystem Business in Strategic Development Natalia Nikiforova1(B) , Yuri Putikhin1 , Dmitry Shlychkov1 , and Veronika Frolova2 1 Financial University Under the Government of the Russian Federation, Moscow, Russia

[email protected] 2 St. Petersburg State Marine Technical University, St. Petersburg, Russia

Abstract. The article reveals the significant aspects of the development of a new business model—an ecosystem. The authors consider the mechanisms of formation, types and forms of ecosystems. The article analyzes the advantages and problems of modern ecosystems in business. Digital, telecommunication technologies are developing incredibly fast, from 3D printing to the Internet of things, mobile everything (money, services, etc.), social networks and much more. The authors considered an interesting analogy between modern ecosystems and the principles of building the relationship of cooperation and specialization in the era of the USSR economy. The authors’ opinion is to consider the company not as a separate player, but as a representative of a business ecosystem that encompasses many participants from different industries. The stakeholder approach of business analysis is also shown in the article. The authors identified six groups of ecosystem stakeholders. The structure of digital innovations is defined in the form of seven basic elements of the innovation ecosystem. The article shows that modern changes are caused by digital innovations, and countries, cities and communities need to navigate them in order to achieve sustainable economic growth. A feature of ecosystems is noted—a model of seamless access to a variety of services for the consumer. The authors support the idea of further development and improvement of ecosystem models. Keywords: Entrepreneurial ecosystem · Business ecosystems in the economy · Ecosystem analysis · Strategic development · Seamless models and platforms

1 Introduction One of the central elements of the modern strategic development of many countries in the world is digital transformation. Without this development, it is impossible to imagine our life now, and even more so in the future. Knowledge in this area is developing exponentially in the 21st century. Consider the second half of the twentieth century. Documents on paper, in institutions—queues of customers, in a store—you need to get to it, choose a product, try it on, stand in line at the cashier—and only after that, we received the service (like 100 and 400 years ago). Over the past 20 years, we have moved into a completely different reality. Around almost everything is digitized. The time © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 16–24, 2023. https://doi.org/10.1007/978-3-031-34329-2_3

Analysis of Ecosystem Business in Strategic Development

17

spent on obtaining the result of more than 5 min is disappointing for a modern person in developed countries. It must be admitted that in Russia we have achieved greater success in some areas than our neighbors (digitalization of the social sphere, electronic document management, for example), but at some points there is still a backlog [1]. Russia has adopted a National Security Strategy, which includes the further development of modern digital platforms. This development cannot be imagined without the creation and involvement of new ecosystem participants in this process. Ecosystem business is already our present, and it will develop by leaps and bounds further.

2 Methods and Materials Business borrowed the definition of “ecosystem” from biology. For the first time this word was used in the 30 s of the twentieth century by the English botanist Arthur Tansley. Ecosystem he called “local communities of organisms that interact with each other and the environment.” To survive, these organisms compete and cooperate simultaneously, co-evolve and adapt to external risks. However, the use of ecosystems in economic theory was much earlier. For the first time, the use of the prefix “eco-” is found in the writings of Hesiod (‘Hσ´ιoδoς; VIII– VII centuries BC) [2]. He wrote about the household “oikos” (oκoς), which is the main economic unit of the state and includes activities, products and people. The terms “ecology” and “economy” originated from the concept of oikos [3]. Any analysis related to a particular branch of the economy requires an in-depth study of the specifics of the industry, for this purpose the works of various scientists have been studied [4]. Then the following theories appeared: the cluster theory of economic development [5, 6], and others; the concept of regional innovative systems [7, 8], etc.; theory of entrepreneurial networks [9, 10], the practice of disclosure by companies of information about sustainable development and the needs of stakeholders in business analytics [11] and others. It was they who became the prototype of the entrepreneurial ecosystem.

3 Discussion However, considering this topic carefully, the idea came that the system of cooperation and specialization in the Soviet economic model is nothing more than a prototype of business ecosystems. Yes, there was no business in the USSR, but there was production and economic activity. Various factories, subcontractors, service supply and maintenance organizations were involved in this activity. Even they were in different regions and countries (for example, the CMEA countries). There were no digital platforms, but there were economic ties. The State Planning Committee of the USSR controlled this system. And in it, by the way, there was a large computing center—a “digital” platform. Soviet cooperation and specialization was introduced to improve the efficiency of management. Cooperative ties between enterprises were largely of an administrativecommand character. Their effectiveness and expediency were also evaluated by specific methods adopted in the Soviet economy. And the task of increasing efficiency today is central to ecosystems.

18

N. Nikiforova et al.

The search for sources of efficiency went through the study of the interaction between the organization and its environment. It turned out that the enterprise can change the environment: the same transition to the production of cars on the assembly line turned them from a luxury item into a product of mass demand [12]. Then it turned out that companies can grow not only independently (by launching new production facilities), but also through mergers and acquisitions of other companies. Business began to create what are now called ecosystems long before the term itself appeared. For example, the insurance organization Lloyd’s of London dates back to the 17th century (insurance and reinsurance contracts). The company now includes more than a thousand corporate members and several hundred individual members, as well as agents, certified brokers and industry syndicates. In the 20th century, horizontally and vertically integrated companies began to actively develop. Horizontal integration involves the unification of organizations of the same type that produce the same type of product. In this case, it is possible to increase the market share and benefit from the cost of production. For example, an oil producing company can buy the business of an oil producer who is developing other fields. Vertical integration is the association of enterprises that create different components of one product within the framework of production processes that follow one after another (this is cooperation). But a new type of activity has appeared—outsourcing. This is another reason for the emergence and development of ecosystems. There was a situation of cooperation with a huge number of contractors. But such a qualitatively new collaboration has raised the question of where the boundary lies between the company and its external environment. This boundary began to blur, including between competitors. For example, Nestlé and Coca-Cola teamed up (and they are competitors) because they had to market a new product, Nestea. In the 20th century, large automakers such as Toyota, Volkswagen, BMW, and Daimler created their own ecosystems. They united suppliers and distributors in huge horizontal networks. In the seventies of the 20th century, the so-called population-ecological or evolutionary theory was developed. It is based on the analogy between changes in the biosphere and the development of the business environment. Organizations are supposed to change to better fit the environment. According to this model, each company goes through certain stages of the life cycle and must consistently solve emerging problems caused by the beginning, growth of the business, the extinction of the business and the conditions of the external environment. In the early 1990s, business strategist James Moore suggested that the company should not be seen as an individual player, but as part of a business ecosystem that included many participants from different industries. “Like its biological counterpart, the business ecosystem is gradually moving from a random set of elements to a more structured community,” J. Moore noted [13].

4 Research Results At the center of ecosystem development are ever-increasing consumer demands, evolving communities that create new value through collaboration and competition. Such cooperation has become the basis of ecosystem business.

Analysis of Ecosystem Business in Strategic Development

19

Analysts distinguish two types of ecosystems. The first is a centralized system that is built around solutions: participants create or provide a product to the user through the coordination of different companies. A centralized ecosystem occurs when the main player builds a network of businesses and acts as a key intermediary between them, without uniting them with each other. This format was chosen by Apple and Amazon to interact with vendors (companies that create and promote their own brands). And BMW and Daimler, for example, work together to create a product for the consumer. The second type is transaction ecosystems, an adaptive system. Here, participants and consumers are connected through a common (usually digital) platform. This type includes, among other things, the ecosystems of Sberbank and Yandex [14]. An adaptive ecosystem is formed when a central player (or players) is looking for new ways to create value by connecting different businesses together. In this case, the ecosystem can be dominated by three- or even four-way relationships, and the process of creating innovations and interaction between partners becomes more flexible. This is especially important for industries in which the rate of change is very high [12]. Figure 1 illustrates the difference between a traditional command and control system and a self-managed adaptive system.

Fig. 1. Mega-trend: from management and control to adaptability

The model we see in the figure, described in the book “Team of Teams” [15], shows how organizations can adapt in today’s increasingly complex and hyper-connected world. Creating competitive digital transformation opportunities requires such a paradigm shift on the part of stakeholders. A mixed (hybrid) ecosystem model is also possible. Typically, ecosystem services include four main blocks: finance, lifestyle, ecommerce, and information technology (Fig. 2). The “Finance” block includes savings, loans, mortgages, etc. The Lifestyle block includes cinemas, music, books, navigators, online education, taxi and carsharing, and more. The Information Technologies block contains a voice assistant, a search service, cloud services, search engines, etc. According to BCG experts, ecosystems have several important differences [16]. A modular principle is a collection of independent elements. Unlike vertically integrated or hierarchical schemes, in ecosystems, various offers (modules) for consumers

20

N. Nikiforova et al.

Fig. 2. Ecosystem services

can be developed independently, but function as a whole. The consumer, as a rule, chooses which options to use and how to combine them. Here, ecosystem services are like smartphone apps. Some of them are pre-installed, but most can be selected and downloaded by yourself. Customization is when a company operates on an open market, it focuses only on its capabilities and the needs of its consumers. But in an ecosystem, products must be mutually compatible. This means that each new element will have to be adapted to a common, usually digital platform. Multilateral relationships—all participants in the ecosystem are connected by relationships, and they cannot be decomposed into bilateral interactions. The online store included in the system simultaneously cooperates with suppliers, payment systems, application developers and other participants in the ecosystem. Coordination is the implementation of common rules, standards, processes. All actions in such a system should be coordinated, and not controlled from a single center. Due to the complexity of structural construction, an ecosystem cannot be managed and controlled in the sense that we used to understand as management. Instead, coordination mechanisms are used. Ecosystem analysis shows that six groups of ecosystem stakeholders can be distinguished (Fig. 3): entrepreneurs, the public sector, financiers, scientists, the private sector, and entrepreneurship support networks. The structure of digital innovation defines seven basic elements of an innovation ecosystem: vision and strategy, capital, market, infrastructure, talent, culture and politics (Fig. 4). These are the ones that are analyzed to get a comprehensive view of system performance. Understanding the issues surrounding each component through a stakeholder lens helps to identify strategic opportunities and threats for digital transformation.

Analysis of Ecosystem Business in Strategic Development

21

Fig. 3. Ecosystem stakeholder groups [17]

Fig. 4. Digital transformation: opportunities and barriers [17]

Innovation potential is created thanks to best practices, cooperation between different business systems, and a coordinated strategy both at the regional and state levels. Combining the incongruous is possible only by uniting the innovation ecosystem, the business ecosystem and the knowledge ecosystem on one platform. Interregional and intersectoral cooperation is essential, as is support for the creation of innovation centers and knowledge networks. Innovation capacity building experiments require intersectoral collaboration with higher education. Sharing university experiences and best practices in technology transfer can accelerate the delivery of innovations to markets. Universities play a key role in providing entrepreneurial skills and fostering a culture of innovation. One of the best ways to build innovative capacity is through governmentinitiated acceleration programs and cross-border knowledge sharing involving talent. Higher education needs to improve its ability to provide skills that promote innovation and technical training. Thus, the state needs an ecosystem of universities, laboratories, companies, investors and regulators that are ready to cooperate and innovate. Another feature of ecosystems is the creation of seamless access to a variety of services for the consumer (Table 1). This is a model when a user registers in the ecosystem

22

N. Nikiforova et al.

once and all platform services are open to him. There are no endless inputs and outputs, confirmation of passwords and logins. Table 1. Characteristics of the composition of business ecosystems Ecosystem

Composition

Apple Company (seamless space) iPhone, iPad, MacBook, Mac Pro, iMac, Apple Watch, iPod, AirPods, Apple TV, HomePod, subscription to all services, smart home Home Kit, Apple Pay Ecosystem Xiaomi

Smartphones, laptops, watches, speakers, televisions, set–top boxes, electric scooters, smart home Mi Home are household appliances, lighting. Smart locks and even cat toilets

Ecosystem Huawei

Smartphone is the main device of the ecosystem, gadgets, smart home elements, office and household appliances, intelligent ecosystem Huawei Seamless AI Life

Ecosystem Honor

Smartphone—this is the main hub of the ecosystem, laptops, watches, headphones

Ecosystem Alibaba

The Chinese e-commerce platform relies on digital tools and platforms. Retail, payments and credit scoring

Ecosystem BMW i Daimler

A joint project called You Now with the participation of several startups is car–sharing services, parking, taxi calling, charging for electric vehicles and an application for multimodal transportation

Housing construction ecosystem

In Russia, in the field of housing construction: from the choice of an apartment and mortgage registration to the organization of repairs and relocation

Ecosystem «Yandex»

Smart speakers and smart sockets, a single ecosystem of services—taxi, delivery, mail, music, virtual disk, search, voice assistant

Russian project “Digital Transport and Logistics”

For the transport sector in Russia, the project “Digital Transport and Logistics” has been developed and is being actively implemented, the purpose of which is to form a single digital transport space that ensures the safety of both passenger and cargo transportation, reducing transport costs, supporting the growth of export and transit opportunities, “… Automation and information and analytical support of transport complex management processes [18]

According to experts, digital ecosystems will generate about 30% of corporate income by 2025. Russia has adopted a Digital transformation Strategy in various fields [19]. The largest ecosystems tend to be formed around the daily needs of the customer and include

Analysis of Ecosystem Business in Strategic Development

23

services for shopping, travel, payments and entertainment. Such projects in Russia are being developed, in particular, by Sberbank, Yandex, Tinkoff, Mail.ru Group and MTS. According to experts, the place of messengers and social networks in the next three to five years will be taken by personal digital assistants. “They will perform a wide variety of tasks—from ordering tickets to choosing clothes—and will become the main link between the user and the ecosystem” [14]. BCG highlights several differences between digital ecosystems and traditional business alliances: • focus on “smart”, integrated solutions instead of focusing on the product; • priority is given to innovativeness and speed of bringing products and services to the market; • highly adaptive networks instead of sustainable value chains; • cooperation beyond geographical and cultural threats; • intersectoral interaction based on intellectual property; • development of new forms of cooperation, including flexible and short-term ones; • joint and continuous creation of value for all participants in the ecosystem [17].

5 Conclusion The study and analysis of business ecosystems allows you to understand in which direction the landscape of modern business is developing. Does the ecosystem seek to surround the user with more and more products and simplify the purchase process? This and many other questions remain to be answered. It is obvious that the results of ecosystem activity, with their clear advantages and no less obvious difficulties, are a complex problem for modern society. The process of forming business ecosystems will undoubtedly continue. At the same time, a large number of complex problems of a technical, legal and organizational nature will have to be solved. But ecosystems are definitely the future.

References 1. Tolmachev, M.N., Latkov, A.V., Mitrofanov, A.Y., Barashov, N.G.: Economic dynamics of Russia: approach based on the solow-swan model. In the collection: Proceeding of the International Science and Technology Conference “FarEastSon 2020”. Singapore: 1063–1072 (2021) 2. Ramenskaya, L.A.: Application of the ecosystem concept in economic and managerial research. Manager 4, 16–28 (2020). https://doi.org/10.29141/2218-5003-2020-11-4-2 3. Ovchinnikova, A.V., Zimin, S.D.: The birth of the concept of the entrepreneurial ecosystem and its evolution. Econ., Entrep. Laws 11(6), 1497–1514 (2021). doi: https://doi.org/10.18334/ epp.11.6.112307 4. Filimonov, S.V., Nikiforova, N.A.: Risk analysis of investment choice based on the blackscholes option pricing model (Black-Scholes option pricing model) on the example of the coal company ARCH COAL INC. Ugol 3, 49–53 (2020) 5. Porter M. Competition. Tr. from Engl. Moscow, Publishing house “Williams”, 495 p. (2001) 6. Bergman, E.M., Feser, E.J.: Industrial and Regional Clusters: Concepts and Comparative Applications. West Virginia University, Regional Research Institute (1999)

24

N. Nikiforova et al.

7. Freeman C.: Japan: A new national innovation system. In: Technology and economy theory.— London: Pinter: 331–348 (1988) 8. Patel, P., Pavitt, K.: The nature and economic importance of national innovation systems. STI Rev. 14, 9–32 (1994) 9. Burt R.: The Social Capital of structural holes. In book: New directions in economic sociology. Guillien M.F., Collins R., England P., Meyer M.—N.Y.: Russel Sage Foundation: 201–246 (2001) 10. Elfring, T., Hulsink, W.: Networks in entrepreneurship: the case of high-technology firms. Small Bus. Econ. Springer 21(4), 409–422 (2003) 11. Efimova, O.V., Nikiforova, E.V., Basova, M.M., Shnaider, O.V., Ushanov, I.G.: Practice of non-financial reporting disclosure by Russian companies: bridging the gap between company disclosures on sustainability and stakeholders’ needs. In the collection: ACM International Conference Proceeding Series. Proceedings of the 5th International Conference on Engineering and MIS, ICEMIS (2019) 12. Statsenko Vladimir Vladimirovich, Bychkova Irina Igorevna: Ecosystem approach in building modern business models. Industrial economy 1, 45–61 (2021) 13. Moore, J.: “Business ecosystems and the view from the firm”. The Antitrust Bulletin. 51(1) (Spring): 31 (2006). doi:https://doi.org/10.1177/0003603X0605100103 14. https://trends.rbc.ru/trends/innovation/6087e5899a7947ed35fdbbf3#card_6087e5899a794 7ed35fdbbf3_1 15. McChrystal, S.A. et al. (2015) Team of teams: new rules of engagement for a complex world, Portfolio/Penguin, New York, NY, 2015 16. BCG: Breaking the Culture Barrier in Postmerger Integrations. https:// www.bcg.com. Last accessed 10 Jan 2022 17. Accelerating Digital Transformation—Good practices for developing, driving and accelerating ICT centric innovation ecosystems in Europe /https://www.itu.int/en/ITU-D/Innovation/ Documents/Publications/18-00204_E_Goodpractices.pdf. Last accessed 01 Jan 2022 18. Chupin Alexander, L., Makar Svetlana, V., Fomenko Natalia, M., Nikiforova Natalia, A., Orusova, O.V.: Analysis of the modern scientific and methodological apparatus for the development of the information infrastructure of a common transport space in the territory of the EAEU. Voprosy Istorii 3(1), 233–240 (2022) 19. Nikiforova Natalia A., Barilenko V.I.: Strategic direction of digital transformation within the framework of development institutions. RISK: Resources, information, supply, competition 3, 110–115 (2022)

The Political Economy of Digital Platforms: Postindustrial Paradigm Against Self-replicating Market Failures Andrey Koshkin(B) , Matiar Rakhman Khashimi , Andrei Fedorov , Sofia Protasova , and Kirill Kirilichev St. Petersburg State University (Department of Economics), St. Petersburg, Russia [email protected]

Abstract. The article draws attention to the efforts of comprehension of the labour market transformation in the area of taxi and food delivery services in the Russian Federation. The key feature of these areas is the leading role of maternal companies of digital platforms, which provides them with a competitive advantage in the market. The research hypothesis states that regardless of non-zero-sum games adherents’ declarations, the main rules of such games within the framework of the digital economy remained unchanged. The main conclusion of this work is the thesis that the economy of digital platforms, which had been placed with high expectations by ideologists of the post-industrial transition, did not solve the contradictions and flaws inherent in capitalist relations. The article reflects on the idea of institutional political economy’s hindrance in the context of the issue along with the national factor of exploitation in the labour market in the taxi and food delivery industry. Keywords: Political economy · Postindustrial revolution · Digital platforms · Digitalisation · Labour market · Marxism

1 Introduction The conceptualization of the transition to Industry 4.0, the platform economy, digital systems, and the knowledge-based economy actualizes questions within the current political economy paradigm. The search for a “new working class” as a cognitariat or precariat is the vector of those directions of political economy which refuse to see the dreadful gap between rich and poor as an ordinary imperfection of political institutions of lagging countries following the example of Acemoglu and Robinson. The precariat refers to a social class formed by people who lack job security and have precarious existence due to infrequent employment or being underemployed. Many economists and sociologists say that the precariat emerged as a result of the entrenchment of neoliberal capitalism. Precarity is inherent to the very nature of employment contracts under capitalism. In principle, a worker is free to negotiate the price of their own labor power, on an equal footing with their putative employer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 25–32, 2023. https://doi.org/10.1007/978-3-031-34329-2_4

26

A. Koshkin et al.

The purpose of this article is to examine the failures of the market for digital platforms (using the example of the corresponding market in Russia) from the perspective of political economy within the Marxist tradition. The research hypothesis, as well as the thesis to prove it, is that despite all the beliefs of the adherents of the idea of economics as a game with non-zero-sum, the fundamental rules of this game in the digital economy remained unchanged. Capital still tends to self-expansion, seizing new markets, and especially leans towards monopolising them. As the capital grows, exploitation will inevitably arise in the labour market, which will become an important site for the workplace conflict of interests of employees and employers. Russian digital platform market was surely selected not by mere chance. It had been actively developing long before the pandemic and is still dynamic today. Special segments of the market for digital platforms include the marketplace area, the food delivery services, and the taxi sphere. All of them have gained particular popularity in recent years and are showing impressive growth in economic indicators. Within the marketplaces, “Wildberries” and “Ozon” have shown incredibly substantial results. The food delivery market is divided between “Yandex.Eats” and “Delivery Club”. The taxi industry has recently been dominated by “Yandex.Taxi”, but “Gett” and “Uber” were present on the market before the war with Ukraine as well. The agreement signed between “Yandex” and “VK” concerning the sale of two of its media properties “Yandex.News” and “Yandex.Zen” in return for “Delivery Club”. Also, in the context of any discussion regarding Industry 4.0, platform economics or the knowledge-based economy, it is necessary to point out the limitations of the conceptual framework underlying these theoretical constructs. The fact that no knowledge-based economy can exist outside the context of classical commodity production is widely ignored by much of the research. Regardless of how well a particular post-industrial economy is constructed, the possibility of its existence is ensured by the existing configuration of the world division of labour and the uneven distribution of the results of this division. Certainly, the success of developed countries cannot be reduced to the world’s established exploitative economic system, but the lack of acknowledgment in the example of neoliberal economists is impermissible too.

2 Methods Political economy, within the framework of such a view of the causes of economic inequality, provides the researchers with specific and powerful tools for analyzing the economic situation. So, the authors identify the research objective of the study as the analysis of the Russian market of digital platforms in order to consider the failures observed in this market from the perspective of the Marxist tradition of political economy. The hypothesis from which the authors predict the outcome is the thesis that those markets that set leading role as part of the transition to Industry 4.0 comply with the same rules as the so-called “zero-sum game” markets that are traditional for political economy. At the same time, the premise of self-growth of capital should be preserved and acquire the core of a conflict of interests between an employee and an employer regarding the labor market. Attempts to identify Industry 4.0 as a fundamentally new stage of development with new rules of the game are inconsistent, at least as long as in concerns arguments

The Political Economy of Digital Platforms …

27

about overcoming contradictions between the owners of means of production and wageearners. The cognitive studies mentioned above can be quite a relevant argument here. Clearly, as manufacturing was transferred to the periphery (in terms of the world-systems theory), the main focus of leading Western economists has shifted from hired manual labour to paid intellectual labour, and directly to the processes involved in this restructuring of the industrial sector. Nevertheless, appropriation problems of surplus value by the owners of the means of production are also relevant in the context of platform economics. The fundamental importance of this point becomes particularly clear while comparing the applicability of the methodology to the analysis of the phenomena on the issue. Why the Marxist tradition of political economy? Why not the institutionalist one? Obviously, in the example of monopolistic aggregators, the failure of a country’s antitrust institutions will be pointed out. Many reasons will be found as to why the pure prescription of the capitalist system has been broken again, as well as why the democratisation of public institutions will lead to economic growth. It seems that there are two main problems with this approach. The first can be confidently described as a fundamental internal contradiction of the entire institutional school of political economy in general. One can see the substitution of a premise with a predicate in modus operandi of this school which can be briefly formulated as “democratisation of public institutions leads to economic growth”. Turned inside out the system of reasoning allows us to overlook the fact that only an economically developed system can afford the maintenance of modern democratic (inclusive, if you prefer) institutions. There is certainly a stable correlation between democracy and economic development, but the nature of this relationship certainly does not allow us to affirm that democratisation is a factor of economic growth, at least with the unambiguity that proponents of institutional political economy demonstrate. The second problem has much more connected with the limitation of the institutional approach to the boundaries of the state in question (with the rare exception of the institutionalists’ study of loose international institutions). While Marxist tradition, especially the modern one, pays very much attention to the inequality of economic relations at the international level, the institutional school prefers to ignore this issue. Additionally, the methodological basis of the research was the development of colleagues regarding the conceptualization of the industrial revolution [1], the study of structural transformations of labor mobility [2], as well as the formulation of long-term vulnerabilities of capitalism on the example of Spain [3]. One of the important aspects of the criticism of neo-institutional political economy by Marxists is the confusion that arises between the premise and the consequence in the relationship between economic development and democratization. What if it is not inclusive institutions that determine the success of economic development, but the fact of economic development itself does it allow countries to build democratic regimes within themselves? In other words, we do not observe economically undeveloped democracies, not because democratic institutions would automatically lead to such It is because a poor country is not capable of maintaining inclusive economic and political institutions

28

A. Koshkin et al.

in the long term. A more detailed analysis of this most interesting with from a macrotheoretical point of view, the dispute is not included in the objectives of this article. Now it seems reasonable to focus on the advantages of neo-institutional political economy.

3 Results Within the Russian market of digital platforms, the national factor of exploitation of the unskilled labour force of migrant workers from Central Asia is also a subject of particular importance. For example, according to the Moscow mayor’s office, more than a quarter (officially employed only) of cab drivers in the Russian capital are foreign nationals [4] (primarily from Kyrgyzstan, Tajikistan, and Uzbekistan). According to a study of a public movement defending the rights of car drivers, this proportion is even more than half in Moscow. It is also safe to say that during the COVID-19 pandemic, the downfall in the Russian construction industry in conjunction with the explosive growth in demand for food delivery services resulted in the mass employment of migrant workers from Central Asia as couriers. Taxi companies have been known to exploit drivers by not providing them with basic workers’ rights. Uber tend to avoid conversations about basic workers’ rights by advertising drivers’ hourly pay. In addition, nearly two dozen cab drivers were arrested for offenses from assault to arson during protests against Uber. The absence of Russian citizenship and willingness to work for lower wages compared to Russian citizens explains the commitment of digital giants (by Russian market standards) to work with migrant workers. However, the full-fledged inclusion of foreign nationals in the staff entails higher costs associated with the implementation of Russian labour laws. The lack of social security for taxi drivers is another issue that has been raised by many. Taxi drivers are often classified as independent contractors, which means they are not entitled to benefits such as health insurance, paid time off, maternal leave or retirement plans. This lack of social security can lead to financial instability and hardship for drivers. This motivates the above-mentioned aggregate companies to search for alternative systems of employment, including the use of intermediary companies that are hired for outsourcing. Another form of tax relief (including social payments) by such companies can be described as forcing them to register their employees as selfemployed or individual entrepreneurs. According to numerous testimonies from trade union activists and workers themselves, this practice is particularly widespread in the cab industry. Configuration of the market relationship that develops between digital platforms, consumers, and couriers/taxi drivers is another particular research interest. To make it clearer, let’s focus on taxi services. The average consumer today does not think about who exactly will provide the transportation service. One can reach out to the aggregator, which will obligingly provide him with an individual entrepreneur or self-employed person, who is ready to give the consumer a ride for an amount unknown to the consumer. The complete absence of guarantees on the part of the aggregator (including ones for cab drivers that they will not get an asocial dangerous behaviour coming from a client) is masked by a system of social ratings, which is rather another form of control of the aggregator over cab drivers. In this case, the consumer, for obvious reasons, is more interested in the price of the trip for themselves, rather than the amount of money the

The Political Economy of Digital Platforms …

29

driver will receive. The non-transparent system of price formation and assignment of payments to the cab drivers themselves in the hands of the IT giant creates enormous opportunities for unhealthy competition with traditional car fleets, and subsequently for monopolizing the market. Another phenomenon is a gig economy. The gig economy is a labor market that relies heavily on temporary and part-time job positions filled by independent contractors and freelancers rather than full-time permanent employees. It uses digital platforms to connect freelancers with customers to provide short-term services. Examples include ride-hailing apps, food delivery apps, and holiday rental apps. In a traditional work arrangement, an employee is hired by the company and paid by the company, earning an agreed-upon wage or salary. For gig workers, they are hired to complete a certain task or project. The gig economy refers to an economy where flexible and temporary jobs are available hence companies hire freelancers and independent contractors instead of full-time employees. Unlike traditional employees, a single company or organization does not employ gig workers. It would seem that the obvious response to attempts to extract rents by a hypothetical monopolist after a dumping takeover of the market may and should lead to a growing share of real self-employed taxi drivers. Nevertheless, this does not and cannot happen. As market transformations take place, digital platforms turn into the analogue of means of production for large IT aggregator companies. Just as it was possible to sew a shirt using artisanal methods in the 19th century to sell it on the market, so today it is possible to register as a self-employed taxi driver and make attempts at private haulage. However, the artisanal method of production at the weaving factories is simply not competitive enough, just as a private taxi driver or even a small regional fleet. Calling a taxi without a user-friendly interface or over the phone with significantly longer waiting times cannot compete with the convenience of Yandex.Taxi for the consumer.

4 Discussion However, the cost of developing and maintaining a complete and high-quality aggregator application creates a significant barrier to market entry. Similarly to the means of production studied by Marx, digital platforms have become a factor in the division and polarisation of labour market participants into owners of the means of production and workers who sell their labour (constrained by the fact that, without adding up to costly means of production, their labour results are not competitive in the market). Interestingly, it is couriers and taxi drivers who have become the drivers of the trade union movement in Russia [5] and in the world [6]. These include the Russian unions “Courier” and “Taxi Driver”, the association of taxi drivers “Plataforma Riders X Derechos BCN” in Spain, the Belgian guild of food delivery couriers “Collectif des coursier-e-s/Koeriers Kollectief”, the Parisian association of taxi drivers “Collectif livreurs autonomes de Paris”. Most of these guilds are regarded as “union-affiliated” because they are supported by massive and long-established trade union organisations which provide them with advice and assistance in logistical matters. In Belgium, Sweden and Switzerland, traditional trade unions have attempted to negotiate directly with workers on digital platforms on working conditions, collective agreements, and collective representation. For example, IG Metall, a German corporation, signed a Code of Conduct in 2015 with eight digital platforms, according to which

30

A. Koshkin et al.

they commit to local wage standards. At the same time, an association of food delivery couriers was formed in Vienna with the support of the trade union “Vida”. A guild of food delivery couriers in German cities is currently in the process of being established in cooperation with the trade union “Gewerkschaft Nahrung-GenussGenussGaststätten” operating in the beverage and catering industry [6]. Of particular interest are works exploring the nature of the brand in the “new economy” [7]. If the question of treating the digital platform as a new form of means of production can be seen from the objective costs of large IT aggregator companies to develop and maintain appropriate applications, then the value of brands can be described as a truly new form of capital (at least the novelty of this factor having such a strong impact on the economy) [8]. Digitalisation has affected taxi drivers in many ways. For example, ridehailing apps such as Uber and Lyft have disrupted traditional taxi services by offering a more convenient and affordable alternative. Taxi drivers have lowered their rates and improved significantly their services (to the extent of implanting new app services) in an effort to try to overcome Uber’s competition. Governments have different approaches to regulating ride-hailing apps. For example, some governments regulate companies that offer ride-hailing apps the same as they are now. In general, governments aim to ensure that ride-hailing services are safe and reliable for passengers. For example, they can require ride-hailing companies to obtain licenses and permits. They can also require ride-hailing companies to comply with safety and insurance regulations. In addition, governments can set minimum fares for ride-hailing services. Finally, governments can also require ride-hailing companies to provide data on their operations. Works exploring the nature of the brand in the “new economy” are of particular interest [7]. If the issue of treating the digital platform as a new form of means of production can be considered from the perspective of objective costs of large IT aggregator companies for development and maintaining corresponding applications, then the value of brands can be described as a truly new form of capital (at least the novelty of this factor consists of having such a strong impact on the economy) [8]. By any means, we are witnessing a situation when the mutually conditioned shifts in the economy do not bring anything fundamentally new to classical labour relations, the nature of which has been described since the nineteenth century by the classics of economic theory. One can even say more, the widespread digitalisation of the market and the development of digital platforms as a means of production in the service market are surprisingly conducive to the precarisation of labour. For Russia, the involvement of Central Asian migrants in unequal and near-legal labour relations is especially noteworthy. This is consistent with the world-system paradigm of global economic development and its perception of the comprador nature of semi-peripheral capitalism in Russia. The unequal relationship between semi-peripheral capital and the labour resources of the peripheral country could not be better illustrated. Such conclusions are quite consistent with the studies that can be observed today in the Marxist spectrum of the European tradition of critical sociology [9]. In our case, however, the class component of the relationship between hired taxi or food delivery workers and the companies owning the digital platforms plays a much more significant role. Yes, there is a lack of institutions whose purpose is to prevent migrant workers from openly exploiting migrant workers illegally in order to extract

The Political Economy of Digital Platforms …

31

monopoly rents from a dumping market. However, this seems to be secondary. We have to see the institutionalisation of already established relationships in the technologically advanced and previously poorly regulated field of e-commerce, which occurs ex post facto. It would be strange to expect something not brutally extractive, for example as the institutionalisation of the type of precarious labour relations represented the employment of migrant workers in Russia before the implementation of self-employment option and bogus individual entrepreneurs. Conversely, it is not the mythical “virtuous circle” or the supposedly social state that ensures the inclusion of such institutions in an overwhelming number of cases, but the well-organised struggle of workers united into trade unions on precisely a class basis. Unions play a significant role both in securing labor protections and preserving their rights such as safety and health, overtime, and family leave along with health services and in enforcing those rights on the job. Unions can help to advocate for workers’ rights, negotiate for better working conditions and pay, and provide a protection for workers. They also help to ensure that their needs are heard by management. However, in terms of contemporary Russian civil society it is difficult to speak about specialised trade unions for taxi drivers. Currently there are no associations with the ability to stand against corporations for workers’ rights. The Marxist thesis is particularly interesting in the context of state persecution of trade union activists fighting for their rights against IT corporations.

5 Conclusion To summarise, the digital platform economy, on which the ideologues of the postindustrial transition pinned such high anticipations, has not resolved the inherent contradictions and flaws inherent in capitalist relations. Market failures are reproduced with frightening accuracy concerning the seemingly far-gone wild exploitation of the nineteenth century. Digital technologies only optimize the unequal labour relations between the owners of digital platforms, as the means of producing certain services (like taxis or deliveries), and the employees, who are forced into deliberately false legal schemes like self-employment or self-employment registration. Moreover, the innovative approach of IT corporations allows them to escape into the grey zones of the economy, where state regulation of labour rights has simply not yet taken control. The conclusions made in this paper are further confirmed by the latest data on mass strikes of workers and the lowest managerial staff of the Russian aggregator company “Wildberries”. The aggravated antagonistic labor relations of the owners of this company and its employees are manifested in severe penalties, unfriendly attitude towards customers and attempts by the aggregator company to shift part of the fixed costs in the form of marriage to the issuing punt (existing within the franchise) and their employees. It is fundamentally important to further investigate the possible existence of class consciousness of the actively fighting for their rights unions of taxi drivers and couriers. It is their unions that are the most visible uncontrolled unions in the post-Soviet space today. Also, in any case, we still have to think more deeply about the national factor of exploitation of Central Asian migrant workers. All these issues seem important not only in terms of economic efficiency or the heuristic potential of contemporary economic theory but also are simply ethically necessary.

32

A. Koshkin et al.

References 1. Kelly, M., Gráda, C.Ó.: Connecting the scientific and industrial revolutions: The role of practical mathematics. J. Econ. Hist.33, 841–873 (2022) 2. Makarski K, Tyrowicz J.: Preference for redistribution during structural change with labor mobility frictions. Eur. J. Polit. Econ. 77, (2023) 3. Rey-Araújo PM, Buendia L.: The Long-term vulnerabilities of spanish capitalism in light of the Covid-19 pandemic: A political economy approach. Int. J. Polit. Econ. 51, 33-48 (2022) 4. Deptrans Moskvy nazval dolyu migrantov sredi taksi (in Russian). https://www.rbc.ru/society/ 04/08/2022/62eb77d79a7947993951ee34. Last Accessed 18 Dec 2022 5. Lyutov, N., Voitkovska, I.: Remote work and platform work: The prospects for legal regulation in Russia. Russ. Law J. 9, 81–113 (2021) 6. Vandaele, K.: Will trade unions survive in the platform economy? Emerging patterns of platform workers’ collective representation and voice. Working paper (2018) 7. Pashkus, V.Y., Pashkus, N.A., Krasnikova, T.S., Asadulaev, A.B.: Estimating a university brand in the new economic conditions: Concept, principles, technique. Int. J. Econ. Financ. Issues 5(3), 100–104 (2015) 8. Pashkus, M., Pashkus, V., Koltsova, A.: Impact of strong global brands of cultural institutions on the effective development of regions in the context of the Covid-19 Pandemic. In: SHS Web of Conferences, vol. 92. (2021) 9. Gartman, D.: Marx and the Labor Process: an Interpretation. Insurgent Sociologist 8(2–3), 97–108 (1978)

Ecosystem-Based Approach to Assessing the Impact of Climate Change on Fisheries Albert Mnatsakanyan(B)

and Alexander Kharin

Kaliningrad State Technical University, Kaliningrad, Russian Federation [email protected]

Abstract. Admittedly, one of the greatest challenges facing humanity is a climate change. Rising level of global redistribution of natural wealth takes its place among many other problems caused by climate change. This process leads to the transformation of economic relations and affects the well-being of people; the consequences at the same time are not yet fully understood and evaluated. The purpose of the article is an economic assessment of climate change consequences. Our study focuses on the way climate-driven change in fish resources, as one of the components of natural capital, affects fisheries, value creation and welfare. The basic idea of the work is the principle of the interconnectedness of well-being with the stability of complex socio-ecological and economic systems. Fishery is a part of a system and authors study interconnections in this field. The methodological basis of the study is the provisions of the theory of inclusive growth. Using the fisheries in the Baltic Sea as an example, we conclude that if current trends continue, the loss in welfare in this region should be expected to exceed the physical change in fish stocks caused by climate change. Keywords: Climate change · Natural capital · Ecosystems · Fish stocks · Wealth

1 Introduction Like everything in nature, ecosystems change constantly. These changes are caused by the influence of factors of both anthropogenic and natural character. Fishing is one of the anthropogenic factors that has a significant impact on the ecosystems of the World Ocean. At the same time, the processes taking place in ecosystems are characterized by changes in parameters over time and delayed consequences of these changes. Lastly, the long-term consequences of ecosystem transformation are of particular importance, since humans tend to prefer the immediate and underestimate future benefits and costs. Climate change is one of the process that change ecosystems and cause significant socio-economic consequences. Transformation of marine ecosystems under the influence of climate change, among other things, leads to a significant change in the conditions of fishery activities and resulting a change in the lives of people who depend on this activity. To adapt to climate change, purposeful activities are required to mitigate the negative consequences; the positive socio-economic results of this natural process should © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 33–41, 2023. https://doi.org/10.1007/978-3-031-34329-2_5

34

A. Mnatsakanyan and A. Kharin

be analyzed as well. Such activities are usually implemented through the principles and methods of management. Currently, the ecosystem approach is often used as a conceptual framework for fisheries management. The essence of this approach lies in such an organization of interaction between environmental, economic and social goals and interests that would maintain the integrity of the structure and functions of aquatic ecosystems and ensure the sustainable use of their bioresources. At the end of the last century, the FAO developed the Code of Conduct for Responsible Fisheries, a document that defined the basis of fisheries management in the framework of the ecosystem approach [1]. The basis of this approach is a holistic perception of ecosystems, a clear understanding of the nature of the changes occurring in them and taking into account the relationship between ecosystems and their environment, including society. The latter means that people involved in the process of making economic and political decisions should be able to assess the consequences of certain changes in the parameters of the ecosystem for the economy and society; also people need to realize how to neutralize the negative effects of these changes. A climate change noticeable increase in recent years, the consequences of which are fundamentally changing ecosystems, has intensified the public and political discussion about the role of natural capital in the fate of mankind [2, 3]; its transformation and redistribution will affect future well-being [4]. Understanding the links between climate, the state of ecosystems and human economic activity is a prerequisite for sustainable development. This is important because many political decisions are still largely based on traditional indicators (such as GDP, production and investment volumes, etc.), which often do not provide reliable information about the development sustainability. The consequence of this is the non-optimality of the decisions made in terms of a positive change in well-being. While there is now a broad consensus on what is and is not sustainable in the context of climate change and how businesses adapt to these changes, the matter of quantifying the economic impacts of climate change remains unsolved. Therefore, in order to improve the quality and efficiency of the models used to manage the economy, it is important to adequately assess climate change affecting natural capital stocks and, ultimately, well-being. The solution to this problem is possible within the framework of the concept of inclusive growth, aimed at a more complete, comprehensive measurement of the economic benefits and the achievement of sustainable economic development distribution [5]. Using this concept, one can not only fully assess changes affect the current sustainability of eco-logo-socio-economic systems, but also predict their future state. The latter is especially important, since the public discourse when discussing political decisions is often shifted towards assessing their consequences for the current generation, while the results of climate change, as a rule, are delayed in time. In the context of inclusive growth theory, wealth is interpreted as the total value of capital assets that embody natural, human and produced capital and serve as the basis for the formation of value streams—current and future benefits. These assets, within the framework of a particular ecological-socio-economic system, can be evaluated with varying degrees of localization (for example, at the national or local level) and from the standpoint of different stakeholders’ interests (for example, the entire population of the

Ecosystem-Based Approach to Assessing …

35

country, residents of one locality, industry entities economy). However, regardless of the type of assessment, the key criterion for the sustainability of any system like that is the level of its inclusive wealth that does not decrease [6]. Climate change is changing the quantity and value of natural capital, and is also redistributing it. Studies of natural capital various components dynamics show that in response to changes in the abiotic environment caused by the influence of global warming, there is a shift of many of its components in the high-latitude direction, on land elevations or in deeper water zones of the World Ocean [4]. These biological shifts often entail institutional transformations—the mechanisms structuring economic relationships change. Human response to climate change is diverse and spans all types of capital employed—human, manufactured and natural [7]. Although some forms of capital, primarily natural, are little subject to the will of people, there are ways to adapt human behavior that can affect the dynamics of the state of natural resources. These modes of adaptation are largely based on economic changes. In order to assess the possible consequences of climate change from the standpoint of welfare, we use the well-known provisions of the theory of inclusive growth, adapting them to the specifics of fisheries. Then, using the example of fisheries in the Baltic Sea, we assess how well-being will change in response to climate change in this region and suggest ways to compensate for the deterioration in well-being.

2 Materials and Methods In the framework of the concept of inclusive growth, wealth is considered as a set of resources available for current and future production and consumption. This wealth includes the free goods assemblage, as well as what is usually called human, industrial and financial capital in economics [6, 8, 9]. So the welfare of society W (t) over time t can be estimated as  Vi (t) · Si (t) (1) W (t) = i

where i is a resource used for production and consumption, Si(t) is the stock (size) of resource i, and Vi(t) is the socially recognized value of a unit of stock of resource i. When measuring well-being, the greatest difficulty is identifying the value of a resource. In a market economy, the price of a natural resource is determined primarily by its scarcity [10]. The price also reflects current institutional conditions, which in some cases can lead to a discrepancy between publicly recognized and market prices (for example, due to the distorting effect of subsidies and other exogenous factors, which is typical for many forms of natural and human capital), or to the absence of a market price as such (if there is no market for a given resource) [6]. Based on these considerations, the value (price) of a resource can be interpreted as a function of the capital stock and institutional conditions: V i (t) = Pi (S i (t), S–i (t), F), where S–i is the stock vector of other components capital, except for resource i, F is a set of parameters describing the institutional conditions that affect decisions on the use of certain resources. Equation (1) evaluates the level of achieved well-being. However, as noted above, the impact of climate change on well-being is one of a prolonged nature. Therefore,

36

A. Mnatsakanyan and A. Kharin

stability presents particular interest as dynamics of this indicator. Sustainability means that the level of well-being remains stable or increases over time which means that in the future, society will be able to maintain at least the achieved level of production and consumption [11]. Based on this assumption, the change in the contribution of natural capital to wealth can be estimated using the cost of compensating for the loss of the industrial base arising from climate change. In the case of small changes in the natural resource, this change in welfare can be described by a linear approximation equation [6].  P i · Si (2) W = i

where P i —weighted average price of stock i before and after its change. Equation (2) makes it possible to measure the net costs of adapting the economy to changing operating conditions, if Pi(Si, S–i, F) is linear in Si and the other arguments of the function do not change. However, climate change, as a rule, leads to a significant change in natural capital stocks, which is why the price functions for these stocks are non-linear and, therefore, Eq. (2) does not give the right answer. Considering a situation where climate change significantly changes the j-th element of natural capital, its other components, denoted as −j change insignificantly (S–j ≈ 0). In this case, when measuring changes in welfare, resource j cannot be included in the sum, as in Eq. (2), and is accounted for separately:

W =

 i=j

sj (t+τ )

P i · Si +

Pj (α, S−j (t), F)d α

(3)

sj (t)

where sj– some resource Sj, Pj = Pj(Sj, S– j F)–stock price function j, α–infinitesimal increment of margin j. If we eliminate the dependence of price on S–j(t) and on F, fixing these variables, then the contribution of the stock j in welfare change is the area below the price curve (see Fig. 1). On the figure sj(t) and sj(t + τ) correspond s and s ± δ (resource stock is changed to δ). If sj(t + τ) = s –δ then the integral in Eq. (3) can be interpreted as the area (A + B + C) on the figure. Moreover, if the price function is linear, then as δ → 0, the weighted average price of the stock can be defined as P j = (Pj (s) + Pj (s − δ))/2. Prices change reliable forecasting for various components of capital as their values change requires finding the corresponding functional dependencies. In our case, we can use well-known and well-developed approaches to measuring produced and human capital, adaptation of which can solve a wide range of problems, including the economic assessment of natural capital stocks. There are various ways to determine the type of natural resource price functions. At the same time, such an assessment, among other things, should take into account the existence of a transfer of benefits between different generations of people since it is required to evaluate not only the current, but also the future state. That is, the extent to which the current economic policy reflects the interests of future generations regarding the use of natural capital. In our opinion from this standpoint, the approach proposed by E. Fenichel looks promising. For each period of time, provided that there is no direct replacement of natural capital by other types of

Ecosystem-Based Approach to Assessing …

37

Fig. 1. Change in the price of a natural resource under conditions of its scarcity

capital, the price Pj of natural capital stock Sj can be defined as [12]. Pj (Sj ) =

MD(Sj , x(Sj )) + Pj (Sj ) ρ − MG(Sj ) − MHI (Sj , x(Sj ))

(4)

where MD—the marginal benefit from increasing the stock of natural capital j, due to the inherent ecosystem properties of this stock. In the case of fishing, this parameter reflects the change in the net fishermen income due to changes in the size of fish stocks. MD depends on the size of the stock, both directly and indirectly, if special compensation policy x(S j ) is implemented. MG is the marginal change in the growth rate of the price of stock j as a result of an increase in its value. MHI is the marginal human impact that characterizes the reaction of stakeholders to changes in the resource, increasing or decreasing the exploitation of the resource as it changes. Parameter ρ is a discount rate that reflects people’s time preferences, how they compare the values of today’s and future benefits. The parameter characterizes the change in the price of a natural resource and can be found, for example, using models that are usually used to evaluate other types of capital. The above equations provide a tool to measure the impact of climate change on natural capital and welfare. While Eq. (3) allows assessing the consequences of climate change on the scale of the entire ecological-socio-economic system, Eq. (4) evaluates its economic (cost) component in conjunction with other effects arising from changes in natural capital.

3 Results and Discussion Let’s consider a stylized special case to illustrate the approach outlined above makes it possible to assess the consequences of climate change from an economic standpoint. Fishery is a typical ecological-socio-economic system (subsystem), including elements of natural, human and manufactured capital. The end result of fishery is to ensure the well-being of people. Although it is not yet completely clear the way climate change changes and redistributes the wealth of the World Ocean and, therefore, it is impossible

38

A. Mnatsakanyan and A. Kharin

to quantify all aspects of this change in terms of well-being [13], however, it is possible to estimate the impact of climatic changes to the contribution that fisheries make to wellbeing approximately. In order to do this, we will focus our attention only on one material component of natural capital, which is the foundation of fishing activity—fish stocks. Let us consider a possible scenario of the impact of climate change on fish stocks and fisheries in the Baltic Sea, using the welfare indicator as the main indicator characterizing the socio-economic consequences of this impact. Studies show that over the past 140 years the water temperature in the Baltic Sea has increased by 1 °C. At the same time, for the period 1982–2006 sea surface temperature rise was 0.06 °C per year, which is 5–6 times faster than the rise in the average surface temperature of the World Ocean [14] and this rise was more intense than the rise in air temperature [15]. As model simulations show, climate warming also leads to a decrease in water salinity and an increase in the total eutrophication of the Baltic Sea [16]. Climatedriven changes affect fish stocks, which form the basis of fisheries in the Baltic. The deterioration in the general habitat conditions of cod fish species was the most notable, in particular Atlantic cod, which populations in the Baltic Sea are overfished and, as a result, are highly susceptible to environmental stresses, including those resulting from climate change [16]. An increase in water temperature has also become one of the reasons for the spatial redistribution of herring and sprat stocks, which has led to a change in their annual catch in some parts of the sea [17]. Thus, climate change entails changes in fisheries and, consequently, in welfare. To analyze and assess the consequences of climate change, it is convenient to divide the Baltic Sea into two conditional zones that differ in the ecological gradient caused by these changes—the North and the South. While fish stocks in the Southern Baltic are declining with climate change, they are increasing in the Northern. Although simplified, this model correctly reflects the trend in the transformation of fish stocks in the Baltic in response to warming—the replacement of some types of commercial fish species by others and a change in the value of gross fishery production as a result [18]. Climate change affects more than just the amount of available fish resources. Its consequence is also a change in the price function. Fish, like many other aquatic organisms, is known to have a narrow range of habitat conditions favorable for growth and reproduction [19]. If the temperature of the medium differs from the optimum, the growth rate of the stock decreases, it leads to a decrease in its marginal growth rates. According to formula (4), lower marginal growth (MG) increases the value of the denominator (effective discount rate) and reduces the stock price; it means a lower cost for the fish stock, even before there is a change in size. Thus, from an economic standpoint, a reduction in the growth rate of the stock leads to its depreciation. Obviously, this mechanism also works in the opposite direction, in the case of optimal habitat parameters for individuals that make up the fish stock. A warming climate generally reduces the productivity of existing ecosystems, leads to the opportunities reduction for fish to live and reproduce [20], and lowers longterm growth rates of stocks, thereby increasing the effective discount rate [21]. In our example, such a change in the long term will lead to a decrease in the value of fish stocks in both zones. In addition, rising water temperatures pose a threat of rapid degradation and collapse of the most valuable fish stocks, especially relevant for the southern zone.

Ecosystem-Based Approach to Assessing …

39

This exogenous risk must be taken into account in the value of the denominator in Eq. (3), which further shifts the price curve down. Given the non-linearity of the growth function of fish stocks, even small changes in growth rates can lead to a significant and unpredictable price shift. Another factor to be taken into account is the actions of public institutions when assessing the impact of climate change on fisheries and on welfare. Fishing in the Baltic is regulated at the international and national levels. Now one of the main regulators of fishing activity is the establishment of the total fishing effort at a level that, among other things, would provide equilibrium prices for fish Pj(s, Si, F) > 0. By regulating the use of a resource this way, society attaches great value to each additional unit of this resource as it is depleted. If we assume that both of the fishing zones we are considering before climate change had the same ecosystem parameters, then climate change should lead to a decrease in total well-being, even if the decrease in the latter in one zone is compensated by growth in the other. This result occurs due to the decreasing type of the price function, which determines that management decisions are made primarily on the basis of the degree of scarcity of the resource. If regulators actions in both zones are based on the same price curve Pj(Sj, S–j, F), the initial value of the fish stock is s, and climate change leads to the movement of the stock δ from the South to the North zone, then the losses of the South zone will exceed the payoff of the North. In graphical form, the value of the increase (loss) can be represented as a change in the area under the price curves for both zones, caused by a physical change in stock (see the figure). Assessed at prices established after climate change, the loss in welfare in the South zone will be equivalent to the area C, while in the North the welfare will increase by an amount equivalent to the area D. At the same time, the gain in the North zone (domain E) will be less than the rectangle with area δ · (Pj(s + δ)—Pj(s)), since Pj(S) is lower than Pj(s). The welfare loss in the South zone, on the other hand, is made up of areas (A + B), where area B is a rectangle of area δ · (Pj(s + δ)—Pj(s)), since Pj(S) increases when S decreases. Thus, if we assume that before climate change the conditions for life and reproduction of fish stocks in the considered fishing zones were identical, the result of warming due to the scarcity effect in the Southern zone will be greater than its growth in the northern zone.

4 Conclusion Climate change affects ecological, social and economic systems by changing their structure and interconnections. The situation deteriorates by overfishing, creating conditions making fish stocks more sensitive to environmental changes. The results of environmental modeling show that the productivity of the fishing sector in the Baltic will decrease with climate warming. This will occur both due to a general decline in catches, and due to the replacement of valuable commercial fish species with less valuable ones; accordingly—a decrease in the total cost of fishing products. At the same time, due to the characteristics of biophysical and socio-economic processes, the scale of changes in well-being can differ significantly from the transformation of natural capital caused by climate change. Although the links between climate change, physical changes in ecosystems, and economic resilience are difficult to identify and quantify, nevertheless, it should be

40

A. Mnatsakanyan and A. Kharin

expected that climate change will strongly affect the components of natural capital used in economic activity [12]. At the same time, the absence or strong distortion of many markets for natural capital reduces the effectiveness of existing market mechanisms for adapting the activities of economic entities to climate change. Under these conditions, in addition to general measures to decarbonize the economy, local actions that can partly compensate for the negative effects of climate change on the communities of the Baltic Sea are the continuation of the policy aimed at reducing the fishing pressure on the fish stocks of the Baltic. For example, the World Bank’s World Development Report 2010: Development and Climate Change states that reducing the overcapacity of fishing fleets and restoring fish stocks can increase the resilience of the latter to climate change and increase the economic returns from fisheries. In addition, this measure will reduce emissions of gases from fuel combustion by fishing vessels, which will further contribute to the reduction of the greenhouse effect [22]. An optimal solution to the most difficult task of harmonizing the scale of fishing with the possibilities of the changing ecosystem of the Baltic Sea is impossible without a clear understanding of both the individual elements of this ecosystem and its entire structure, which requires comprehensive monitoring. This is important, since fisheries management in Russia (as in most other countries) is still mainly based only on solving the problem of optimizing the commercial stock through various fishery regulation measures [23], while ignoring many important components, in the aggregate constituting the ecological-socio-economic system, of which fishing is a part, and its main purpose is to ensure sustainable well-being. The fact no less important is that without unambiguously interpreted and practical indicators of welfare change, it is impossible to assess the amount of losses and benefits to society or individual market participants from climate change. It is possible that, taking into account the diversity of relationships that exist in ecological-socio-economic systems, the redistribution of wealth in this case will be more significant than momentary losses or profits, which are usually taken into account when making political and managerial decisions. Taking better account of how climate change is redistributing natural wealth across borders in addition to better tracking cumulative changes in well-being, will make regional and international cooperation more rational. Assessing the impact of climate change on fisheries and well-being is a complex empirical task that also requires a clear understanding of the characteristics of social and market environments in addition to knowing the parameters of fish stocks. Our example shows that a good basis for forecasting and subsequent effective management of fisheries in this case can be the use of an inclusive wealth methodology that takes into account many key aspects of social and economic well-being.

References 1. FAO.: Code of conduct for responsible fisheries. Retrieved from https://www.fao.org/3/v98 78e/v9878e00.htm 2. Agarwala, M., Atkinson, G., Baldock, C., Gardiner, B.: Natural capital accounting and climate change. Nat. Clim. Chang. 4, 520–522 (2014) 3. IPCC (2021). Climate Change 2021. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press

Ecosystem-Based Approach to Assessing …

41

4. Nelson, E.J., Kareiva, P., Ruckelshaus, M., Arkema, K., Geller, G., Girvetz, E., et al.: Climate change’s impact on key ecosystem services and the human well-being they support in the US. Front Ecol Environ 11(9), 483–893 (2013) 5. Hanley, N., Dupuy, L., McLaughlin, E.: Genuine savings and sustainability. Journal of Economic Surveys 29(4), 779–806 (2015) 6. Dasgupta, P.: Measuring the wealth of nations. Annu Rev of Res Econ 6, 17–31 (2014) 7. Mendelsohn, R., Dinar, A.: Climate change and agriculture: an economic analysis of global impacts, adaptation, and distributional effects. Edward Elgar Publishing, (2009) 8. Fisher, I.: The nature of capital and income. Norwood Press, Norwood, MA (1906) 9. Barbier, E.B.: Account for depreciation of natural capital. Nature 515, 32–33 (2014) 10. Hotelling, H.: The economics of exhaustible resources. J Polit Econ 39(2), 137–175 (1931) 11. Arrow, K.J., Dasgupta, P., Goulder, L.H., Mumford, K.J., Oleson, K.: Sustainability and the measurement wealth. Environ Devel Econ 17, 317–353 (2012) 12. Fenichel, E.P., Gopalakrishnan, S., Bayasgalan, O.: Bioeconomics: nature as capital. In: Halvorsen, R., Layton, D. (eds.) Handbook on the Economics of Natural Resources, pp. 165–205. Edward Elgar (2015) 13. Pinsky, M.L., Fogarty, M.J.: Lagged social-ecological responses to climate and range shifts in fisheries. Clim. Change 115, 883–891 (2012) 14. Dailidien˙e, I., Baudler, H., Chubarenko, B., Navrotskaya, S.: Long term water level and surface temperature changes in the lagoons of the South and East Baltic. Oceanologia 53, 293–308 (2011) 15. Bukanova, T.V., Staunt, J.I., Gushchin, O.A.: Sea surface temperature variability in the SouthEast Baltic according to MODIS data. Mod. Probl. Remote. Sens. Earth Space 12(4), 86–96 (2015) 16. Gushchin, A.V., Fedorov, V.E.: The current state of the commercial ichthyofauna of the southern part of the Baltic Sea as a result of anthropogenic impact. Sci. Notes Russ. State Hydrometeorol. Univ. 49, 134–144 (2017) 17. Pedchenko, A.P., Boitsov, V.D.: Peculiarities of long-term climate dynamics and its influence on the distribution and harvesting of herring fish species in the Baltic Sea. VNIRO proceedings 180, 44–59 (2020) 18. Eremina, T.R. et al.: Impact of climate change on marine natural systems. Baltic Sea/The second assessment report of Roshydromet on climate change and its consequences on the territory of the Russian Federation. Under total ed. A.V. Frolova, (c. 615–643). Roshydromet, Moscow (2014). Retrieved from http://downloads.igce.ru/publications/OD_2_2014/v2014/ v1/Razdel_5.pdf 19. Portner, H., Knust, R.: Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007) 20. Behrenfeld, M.J., Malley, R.T., Siegel, D.A., Mcclain, C.R., Sarmiento, J.L., Feldman, G.S., et al.: Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006) 21. Caswell, H.: Matrix population models: construction, analysis, and interpretation, 2nd edn. Sinauer, Sunderland, MA (2001) 22. World Bank.: World development report 2010: Development and climate change. World Bank, Washington, DC (2010). Retrieved from https://openknowledge.worldbank.org/han dle/10986/4387 23. Ivanov, O.A.: Change of paradigms in management of fishery: from conception to realization? Izvestiya TINRO 190(3), 3–17 (2017). (In Russ.)

Spatial Ecosystems

Entrepreneurship Developmentin the Old Industrial Cities of Russia Based on the Ecosystem Approach Olga Akimova , Margarita Kozhukhova , and Daniil Frolov(B) Volgograd State Technical University, Volgograd, Russia [email protected]

Abstract. Old industrial cities in Russia, as well as throughout the world, are experiencing a whole range of extremely complex and diverse problems. The launch of new trajectories of economic growth implies, first of all, the development of new zones of entrepreneurial activity in the economic space of the city. Especially important for the development of small creative entrepreneurship is an established dialogue between the state, business and society. The article proposes an ecosystem concept for the development of an entrepreneurial environment and entrepreneurship in old industrial cities. The ecosystem approach involves considering the business environment of the city as a complex adaptive system, covering a variety of stakeholders that interact interactively with each other in a continuously changing space. As a result, the existing programs to support entrepreneurship from the point of view of the ecosystem approach are extremely one-sided: they are aimed at “top-down” interactions, are mainly associated with financial support, poorly take into account the social and environmental responsibility of business, pay insufficient attention to the entrepreneurial orientation of the broad social masses and raising the social status of entrepreneurs in the eyes of society. A special role in the implementation of the ecosystem approach to the development of the business environment and entrepreneurship in old industrial cities is played by ecosystem innovations, which we propose to integrate into targeted programs in the field of entrepreneurship: these include digital platforms, digital services and auxiliary digital infrastructure, which together form a digital ecosystem of state interactions, businesses and local communities. Keywords: Old industrial cities · Ecosystems · Digital technologies · Entrepreneurship · The economic growth

1 Introduction At present, every second region of Russia is old-industrial, having the legacy of the Soviet industrial sphere and facing the problems of developing modern industries within the framework of the sixth technological order, high unemployment and inflation, low wages, low business activity rating, increased population migration, trying to overcome the contraction of territories and degradation of the social sphere. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 45–59, 2023. https://doi.org/10.1007/978-3-031-34329-2_6

46

O. Akimova et al.

The phenomenon of the old industrial region arose within the framework of the model of the Swedish economist G. Myrdal, who described the stages of development and decline of the region. In his opinion, the development of the region is mediated by the emergence of new industrial enterprises, the formation of a new city-forming sphere, the attraction of investments, the development of technologies and increasing prosperity. However, when the basic city-forming sphere begins to experience crisis phenomena, the incomes of the population fall and, following the industrial enterprises of the region, the degradation of the service sector begins, which ultimately leads the region into decline, and it becomes an old industrial one [1]. Bakanov S.A., analyzing the causes of the economic decline in the old industrial regions, systematizes various studies in several areas [2]. In his opinion, according to one of the concepts, “globalization processes are to blame for the economic decline in the old industrial areas, which opened up the possibility of transferring industry to regions of the world with cheaper labor” [3]. Another point of view focuses on “structural shifts that accompany the transition from an industrial society to a post-industrial one and from mass production to flexible production schemes” [4]. The third—“as the root cause of disasters in the old industrial regions highlights the completion of the life cycle of industries concentrated in them” [5]. Another view draws attention to “the weakness of the innovative potential of the old industrial regions, caused by the fact that innovative activity in them is highly specialized and focused on improving existing technology, and not on developing a fundamentally new one” [6]. To overcome the shrinkage of the territory of the old industrial regions, it is necessary to include a new service sector in the “sectoral core” of the economy through the development of creative small business. In this regard, the main purpose of this article is to develop recommendations for the development of entrepreneurship in old industrial regions through the introduction of an innovative system of interaction between the state, entrepreneurs and society, based on the integration of elements of the ecosystem approach [7, 8].

2 Results 2.1 Analysis of the Development of Entrepreneurship in the Old Industrial Regions of Russia Entrepreneurship is the basis for the development of the economy of any territory, including the old industrial one. However, according to statistical data, entrepreneurship in the old industrial regions of the Central Federal District and the Southern Federal District has a spasmodic development (Table 1). C 2017 g. by 2019 g. There is an increase in entrepreneurial structures in general, with 2020 g. by 2021 g. Their sharp decline (apparently caused by the coronavirus pandemic) and again a slight increase in 2022 g. The number of legal entities (micro and small enterprises) has been steadily declining over 5 years. Legal entities—medium-sized enterprises and individual entrepreneurs—small and medium-sized enterprises show a decrease in the period from 2017 g. by 2019 g. And slight growth in 2021 g. And 2022 g. However, in general, it can be noted that the number of legal entities and individual entrepreneurs has not yet reached the indicators 2017 g., which means that their number is progressively decreasing.

Entrepreneurship Developmentin the Old …

47

Table 1The number of legal entities and individual entrepreneurs, information about which is contained in the unified register of small and medium-sized businesses of the Central Federal District and the Southern Federal District from 2017 to 2022 g The subject of the Russian Federation

Total Total

of them Legal entities Individual entrepreneurs Micro Small Average Micro Small Average Total enterprise enterprise prev. enterprise enterprise prev.

as of May 10, 2022 Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region Volgograd region Rostov region Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region

6 007 029 1 917 398

2 357 123 2 155 049 184 423 826 475

753 651

65 950

17 651

3 649 906

3 623 190

26 399

317

6 874

1 090 923

1 084 576

6 255

92

404 255

134 231

122 263

10 704

1 264

270 024

269 121

890

13

812 782

444 265

407 765

32 878

3 622

368 517

367 218

1 281

18

58 978

18570

16 760

1646

164

40408

40 152

252

4

85 824

30792

27 628

2885

279

55032

54 495

523

14

25 057

7439

6 628

748

63

17618

17 458

157

3

41 476

15396

13 736

1 531

129

26080

25 800

276

4

38 611

17457

16 024

1 293

140

21154

20 932

221

1

46 546

18280

16 508

1644

128

28266

28 009

257

0

53 571

18038

16 358

1 554

126

35533

35 319

211

3

689 799

181 596

165 660

14 529

1407

508 203

504 588

3 580

35

279 579

71 777

65 708

5481

588

207 802

206 313

1474

15

73 660

20 968

18 939

1 847

182

52 692

52 340

348

4

173 086

47 163

42 510 4 234 as of May 10, 2021

419

125 923

124 944

968

11

5 830 343

2 419 564 2 213 218 188 994

1 829 785

835 452

761 542

370 126

132 491

120 497

764 291

443 484

406 803

59 363

19736

17 860

86 166

33555

30 291

67 180

17 352

3 410 779 3384 495 25 978

6 730

994 333

988 187

10 757

1237

237 635

33 146

3 535

320 807

1715

161

2977

306

6057

89

236 791

832

12

319 747

1046

14

39627

39 367

257

3

287

52611

52 077

521

13

24 978

7858

7001

790

67

17120

16 962

155

3

40 846

15845

14 132

1 593

120

25001

24 704

292

5

37 435

16984

15 523

1 329

132

20451

20 218

231

2

46 029

19119

17 291

1 702

126

26910

26 648

262

0

52 910

19091

17 346

1620

125

33816

33 598

218

3

676 812

188 363

172 037

14 950

1 376

488 449

484 985

3434

30

270 291

73 534

67 369

5 595

570

196 757

195 374

1 373

10

(continued)

48

O. Akimova et al.

(continued) The subject of the Russian Federation

Volgograd region Rostov region Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region Volgograd region Rostov region

Total Total

of them Legal entities Individual entrepreneurs Micro Small Average Micro Small Average Total enterprise enterprise prev. enterprise enterprise prev.

73 504

22 394

20 278

169 831

49 632

44 822 4 399 as of May 10, 2020

6 035 035

1939

2 574 657 2 361 636 196 276

177

51 110

50 748

358

4

411

120 199

119 194

994

11

16 745

3 460 378 3434 270 25 811

297

1 870 480

884 238

808 101

69 687

6 450

986 242

980 205

5 959

78

367 444

137 876

125 619

11 060

1 197

229 568

228 739

821

8

775 047

470 782

433 296

34 127

3 359

304 265

303 397

859

9

63 289

21850

19 926

1 777

147

41439

41 163

274

2

88 760

35270

31 857

3 139

274

53490

52 961

516

13

25 889

8082

7 168

845

69

17807

17 641

164

2

43 381

17473

15 674

1 691

108

25908

25 609

296

3

39 424

17758

16 239

1 391

128

21666

21 435

229

2

48 013

20140

18 221

1 792

127

27873

27 599

273

1

56 253

20689

18 833

1 723

133

35564

35 339

222

3

700 974

199 303

182 436

15 557

1310

501 671

498 261

3 382

28

280 677

78 316

71 938

5 830

548

202 361

201 017

1 334

10

77 643

23 968

21 736

2056

176

53 675

53 319

353

3

175 341

52 672

47 713

4 582

377

122 669

121 647

1011

11

18 302

3 410 043

3 382 840

26 888

315

as of May 10, 2019 Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region Volgograd region Rostov region

6 184 204

2 774 161 2 535 614 220 245

1 927 918

984 678

897 735

79 705

7 238

943 240

936 998

6 162

80

352 082

138 325

125 388

11 668

1 269

213 757

212 886

864

7

826 975

554 328

509 697

40 660

3 971

272 647

271 886

754

7

65 288

23186

21 039

1 988

159

42102

41 804

293

5

91 239

37949

33 957

3 693

299

53290

52 743

535

12

27 112

9062

8040

950

72

18050

17 865

184

1

43 969

18223

16 265

1845

113

25746

25 434

309

3

39 823

17890

16 223

1534

133

21933

21 677

253

3

48 552

20721

18 646

1950

125

27831

27 532

298

1

57 693

22384

20 355

1 882

147

35309

35 073

231

5

719 289

209 845

191 411

17 039

1 395

509 444

505 962

3457

25

286 688

82 666

75 577

6 501

588

204 022

202 594

1416

12

81 181

26 491

23 998

2309

184

54 690

54 312

376

2

179 156

54 294

48 917

4 960

417

124 862

123 820

1033

9

(continued)

Entrepreneurship Developmentin the Old …

49

(continued) The subject of the Russian Federation

Total Total

of them Legal entities Individual entrepreneurs Micro Small Average Micro Small Average Total enterprise enterprise prev. enterprise enterprise prev.

as of May 10, 2018 Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region Volgograd region Rostov region

3 253 592

3225 786

6 170 963

2 917 371 2 661 202 236 495

19 674

27 460

346

1 928 175

1 053 243

958 864

86 411

7 968

874 932 868 626

6 219

87

329 159

135 418

122 056

12 046

849 112

613 534

563 845

45 169

1 316

193 741 192 855

875

11

4 520

235 578 234 886

689

66 194

24436

22 205

2064

167

41758

41 443

310

3 5

90 026

39112

34 700

4073

339

50914

50 383

518

13

26 752

9328

8 276

977

75

17424

17 226

197

1

43 756

19490

17 349

2009

132

24266

23 947

316

3

39 710

18064

16 330

1600

134

21646

21 384

259

3

48 215

21489

19 332

2030

127

26726

26 425

300

1

57 168

22981

20 845

1974

162

34187

33 941

241

5

717 402

217 921

198 485

17 930

1 506

499 481 495 962

3487

32

284 859

84 947

77 426

6 855

666

199 912 198 514

1 385

13

82 627

29 308

26 614

2496

198

53 319

52 914

401

4

178 746

56 359

50 698

5 232

429

122 387 121 335

1042

10

as of May 10, 2017 Russian Federation Central Federal District Moscow region Moscow Belgorod region Voronezh region Oryol Region Ryazan Oblast Smolensk region Tver region Tula region Southern Federal District Krasnodar region Volgograd region Rostov region

6 080 091

2 937 415 2 679 171 238 231

20013

3142676

3 114 136

28 185

355

1 861 973

1041630

945 983

87 229

8 418

820343

813 880

6 375

88

311 821

135089

121 967

11 859

1 263

176732

175 827

896

9

808 708

600739

549 066

46 583

5090

207969

207 311

656

2

64 319

23133

20 910

2035

188

41186

40 858

323

5

88 927

40115

35 598

4 158

359

48812

48 269

529

14 2

26 358

9386

8 394

916

76

16972

16 763

207

43 121

19884

17 822

1945

117

23237

22 908

326

3

38 900

17715

16 036

1 552

127

21185

20 919

263

3

48 071

21996

19 827

2046

123

26075

25 776

298

1

56 226

22888

20 784

1959

145

33338

33 088

244

6

710 400

220202

200 994

17 739

1469

490198

486 645

3 518

35

280 886

85393

77 900

6 868

625

195493

194 078

1 399

16

83 477

31179

28 391

2575

213

52298

51 882

412

4

176 023

56380

50 886

5078

416

119643

118 581

1051

11

Source compiled by the authors based on the materials of the unified register of small and mediumsized businesses

50

O. Akimova et al.

Among the old industrial cities of the Central Federal District, Moscow and the Moscow Region lead in terms of the number of business structures, in the Southern Federal District—Krasnodar Territory. If we consider each old industrial region separately, then the same trends are observed in the development of entrepreneurship as in Russia as a whole. At the same time, it is interesting that the growth of entrepreneurship both in Russia as a whole and in individual old industrial regions is provided mainly by individual micro-sized entrepreneurs, which may mean the forced nature of entrepreneurship, which is not the basis of economic growth. In connection with the crisis in the economy, the population is forced to open individual entrepreneurs to maintain income levels and support for the family. This data is also confirmed by the Global Entrepreneurship Monitoring (GEM). According to GEM, “In 2021, the most common entrepreneurs reported that they are motivated to start a business by the need to provide a source of income. 68.9% of early-stage and 70.4% of established entrepreneurs agreed with this to a greater or lesser extent. The value of this indicator has decreased compared to the previous year (71.4 and 78.8% respectively)…, but the main motive is still associated with forced involvement in business, while established entrepreneurs more often say this over the years of observation” [7]. Also according to the Global Entrepreneurship Monitor in 2021 g. The number of those wishing to open a business and taking concrete steps to do so is more than 1.5 times higher than the number of those who left the business (either closed the enterprise or transferred it to another owner). However, at the same time, the retirement index amounted to 3.9%, exceeding the readings of the previous year—3.3%. The main reason for leaving the business was its unprofitability, this reason was indicated by 28% of respondents… 17.6% of respondents named the pandemic as the factor that forced them to leave the business, 14.8% of those who left the business explained their decision by low efficiency regulation of entrepreneurial activity, including the imperfection of the taxation system and bureaucracy” [9]. It should be noted that in comparison with countries with a high per capita income, where positive reasons for exiting a business predominate, negative factors lead in this aspect in Russia. Let us analyze the innovative activity of the old industrial regions of the Central Federal District and the Southern Federal District. The leader in terms of innovative activity of entrepreneurship is the Rostov region (27.6%), which is ahead of Moscow and the Moscow region in this indicator. In second place is the representative of the Central Federal District—the Belgorod region (17%). Moscow ranks only fifth among the old industrial regions of the Central Federal District and the Southern Federal District in terms of the level of innovative activity of enterprises (Table 2). In general, it is worth noting that the level of innovative activity in all regions of the Central Federal District and the Southern Federal District is low, in some regions this indicator has slightly increased over the past 6 years (Voronezh, Ryazan, Moscow, Volgograd regions, in some regions it has decreased (Krasnodar Territory, Smolensk Region). In the Volgograd region in recent years, the dynamics of indicators has not changed much. But, unfortunately, the region lags far behind in development compared even with Moscow. Based on the data in Table 2, one can notice an abrupt change in the level of innovative activity. If between 2016 and 2017 there is a decrease in this indicator, then in 2018 g. There is a sharp increase, then a fall again, and in 2020–2021—again

Entrepreneurship Developmentin the Old …

51

Table 2 The level of innovative activity of organizations in the old industrial regions of the Central Federal District and the Southern Federal District (%) The subject of the 2016 2017 2018 2019 2020 2021 Russian On crite-Riyam On crite-Riyam Federation 3rd edition of the 4th edition of the Manual Oslo Manual Oslo Russian Federation

8.4

8.5

14.6

12.8

9.1

10.8

11.9

Central Federal District

10.3

9.9

18.5

16.2

10.8

12.5

12.6

Moscow region

8.5

8.9

18.8

14.1

8.6

10.8

11.7

16.1

14.3

32.4

33.8

12.1

13.0

13.3

Belgorod region

14.1

14.8

19.8

18.2

15.1

18.0

17.0

Voronezh region

11.6

11.7

18.6

17.1

13.4

15.9

12.6

7.4

6.8

11.0

8.6

10.4

13.7

15.3

Moscow

Oryol region Ryazan Oblast

12.3

12.1

17.5

16.4

11.8

10.9

12.6

Smolensk region

6.9

6.5

11.7

10.8

8.4

7.1

6.5

Tver region

7.9

8.7

16.3

15.6

12.1

12.0

11.7

Tula region

10.9

9.2

16.9

15.4

11.7

20.2

15.4

Southern Federal District

7.1

8.4

11.9

9.5

7.5

8.0

11.9

Krasnodar region

9.1

12.2

12.6

8.9

4.3

5.3

6.3

Volgograd region

4.9

4.6

10.1

8.0

4.9

7.7

8.8

Rostov region

8.4

8.2

14.6

13.2

17.6

13.8

27.6

Source Compiled by the authors based on materials [10]

a sharp jump up. Such a change in innovation activity can be explained by stimulating state support or crisis phenomena in the Russian economy. The Volgograd region can be safely described as a territory with a relatively low level of technological development of the industrial complex. It is noteworthy that, for example, the Krasnodar Territory leads in terms of the number of entrepreneurial structures among the old industrial regions of the Southern Federal District, but at the same time it has the lowest level of their innovative activity, which, apparently, once again confirms the forced nature of entrepreneurship. The leader in terms of the share of organizations implementing technological innovations among the old industrial regions of the Central Federal District and the Southern Federal District is again the Rostov Region (43%), in second place is Moscow (32.1%). At the same time, both Moscow and the Rostov region are leaders in their federal district. If we consider the share of organizations that carried out technological innovations in the Volgograd region, then it is in clear outsiders: c 2018 g. There is a sharp increase in

52

O. Akimova et al.

this indicator, which at the same time in the period from 2018 g. by 2021 g. Remains virtually unchanged, which may be due to international sanctions and the coronavirus pandemic. However, according to the 2021 g., the Volgograd region is more than 2 times behind the leaders: Moscow and the Rostov region, overtaking the Smolensk region and the Krasnodar Territory (Table 3). At the same time, it is worth noting a rather sharp breakthrough in this direction by the regions of the Central Federal District, which increased the indicator in question by 2 and even 3 times. Perhaps this is due to state support for enterprises and a large amount of investment. Table 3 The share of organizations that carried out technological innovations in the total number of organizations surveyed, in the Central Federal District and the Southern Federal District (%) The subject of the 2016 2017 Russian According to the Federation criteria of the 3rd edition of the Oslo Manual

2018 2019 2020 2021 According to the criteria of the 4th edition of the Guide Oslo Manual

Russian Federation

7.3

7.5

20.8

19.8

21.6

23.0

23.0

Central Federal District

9.0

8.6

24.6

23.9

28.1

26.5

25.5

Moscow region

7.1

7.6

24.7

22.9

27.5

24.5

23.9

Moscow

14.9

13.6

40.5

41.3

45.1

32.6

32.1

Belgorod region

13.0

13.3

21.5

21.3

26.7

30.6

27.8

Voronezh region

8.8

8.5

21.3

25.4

23.5

27.3

21.1

Oryol region

5.8

4.8

14.0

14.7

22.5

22.3

18.9

Ryazan Oblast

8.7

8.1

20.3

19.4

31.5

21.0

20.4

Smolensk region

6.2

5.8

17.7

15.6

17.9

17.2

14.4

Tver region

7.5

8.0

22.0

22.2

21.9

24.8

22.1

Tula region

10.3

8.1

22.6

24.5

22.5

36.0

26.7

Southern Federal District

6.2

7.5

18.5

14.8

17.8

19.1

21.5

Krasnodar region

7.3

10.7

18.3

12.6

10.3

12.8

11.0

Volgograd region

4.6

4.2

18.8

15.8

15.5

15.5

16.0

Rostov region

7.8

7.7

19.8

16.9

32.0

33.7

43.0

Source Compiled by the authors based on materials [11]

Despite the progressive growth of organizations that carry out technological innovations and the level of innovative activity, all the analyzed regions have a very modest

Entrepreneurship Developmentin the Old …

53

Table 4 The share of innovative goods, works, services in the total volume of shipped goods, performed works, services in the old industrial cities of the Central Federal District and the Southern Federal District (%) The subject of the Russian Federation Russian Federation

Specific gravity 2016

2017

2018

2019

2020

2021

8.5

7.2

6.5

5.3

5.7

5.0

Central Federal District

11.6

6.9

6.2

5.0

5.2

4.6

Moscow region

15.8

14.7

13.2

5.8

8.8

7.6

Moscow

13.6

3.3

3.0

3.9

3.6

3.2

Belgorod region

7.3

11.6

14.9

13.9

14.1

11.6

Voronezh region

5.9

6.1

5.9

7.3

6.2

4.0

Oryol region

0.5

1.1

1.0

0.5

4.8

2.1

Ryazan Oblast

6.2

6.8

5.8

9.7

5.2

5.5

Smolensk region

1.8

4.4

2.2

5.2

3.4

2.2

Tver region

5.5

3.1

4.5

5.6

7.1

9.4

Tula region

11.2

12.7

12.2

8.2

13.5

10.3

Southern Federal District

8.4

9.0

5.6

2.7

3.3

3.6

Krasnodar region

7.7

14.1

11.5

2.3

1.8

1.6

Volgograd region

3.0

3.6

2.2

2.7

2.2

2.4

14.5

10.6

5.8

4.9

8.5

8.9

Rostov region

Source Compiled by the authors based on Rosstat materials

share of innovative goods, works, services in the total volume of shipped goods, works, services in recent years, which is progressively declining every year (Table. 4). The leaders in this indicator among the old industrial regions of the Central Federal District and the Southern Federal District are the Belgorod (11.6%) and Tula (10.3%) regions. Within the SFD, the leader is the Rostov Region (8.9%). The Belgorod region is ahead of the Volgograd region by 9.2% points. In general, it can be noted that the growth in the share of organizations engaged in technological innovation does not lead to an increase in the share of innovative goods!. “The next annual rating of the investment attractiveness of regions, prepared by the RAEX agency, shows that the subjects of the Russian Federation entered the global pandemic crisis with increasing investment risks” [12]. According to the data for 2020, the Volgograd Region has a rating of 3B1, which is characterized by reduced investment potential and moderate investment risk. Neither in 2021, nor in 2022 g. The Volgograd region was not among the leaders of the national rating of the state of the investment climate in the constituent entities of the Russian Federation. The business activity of small and medium-sized businesses in the region is also progressively declining, amounting to 2020 g. 48.8 p.

54

O. Akimova et al.

According to GEM, ranks first and is most often mentioned by experts as having a negative impact on the development of entrepreneurship in the country. Among the constraining factors last year, experts noted primarily “lockdown”, “inconsistency in the actions of the federal and subfederal levels of government in the introduction of supportive measures…”, “closure of B2C channels” and, in the new conditions, “lack of synchronism between the actions of the state and business” due to “lack of dialogue”. Experts considered government support programs to be “overbureaucratic and inaccessible” or “insufficient” [9]. The lack of dialogue and dissynchrony in the actions of the state and business is the main reason for the inefficient development of entrepreneurship, which could lead many old industrial regions to a new path of development. In this regard, an innovative system of interaction based on elements of the ecosystem approach is proposed. 2.2 Innovative System of Regulation and Development of Entrepreneurship in Old Industrial Regions with Elements of the Ecosystem Approach A comparative analysis of the current system of regulation and development of entrepreneurship and the innovation system is presented in Fig. 1.

Fig. 1. Models of the current and innovative system of regulation and development of entrepreneurship [comp. Auth.]

As can be seen from Fig. 1 the “State. Entrepreneurs. Society”, this system involves the pursuit of various selfish interests by these economic entities. Thus, the state strives for economic growth, entrepreneurs for profit, society for quality goods and services.

Entrepreneurship Developmentin the Old …

55

As a result of the disunity of interests, the overall economic effect of the interaction of these market entities turns out to be weak. The innovative system “state-entrepreneurs-society” assumes the unity of interests of these economic entities and the desire to ensure intensive economic growth. Within the framework of this system, the state provides entrepreneurs with high-quality public services, business demonstrates social and environmental responsibility and provides consumers with quality goods and services, and society, in turn, provides support and approval to the state and entrepreneurs. As a result of the synergy of the “state-entrepreneurs-society” system, the maximum efficiency of interaction between the designated economic agents is achieved, common goals are achieved and intensive economic growth is ensured. It is proposed to increase the efficiency of interaction between economic entities by integrating elements of the ecosystem approach into the target program for the development of entrepreneurship. In the context of the digitalization of the modern economy, ecosystem innovations that should be integrated into targeted programs in the field of entrepreneurship are understood as digital platforms, digital services, digital infrastructure, digital technologies, which together form the digital ecosystem of the economy and its system elements—ecosystems of individual business entities. The inclusion of ecosystem elements in targeted programs from a practical point of view is proposed in order to ensure the compatibility of the ecosystems of individual entities in order to build a common digital ecosystem on the scale of the national economy. Close, “seamless” integration of the external and internal environments of the state and business entities, the use of an ecosystem approach, innovative methods and techniques provide new opportunities for the development of old industrial regions and ensuring intensive economic growth. An innovative system of regulation and development of entrepreneurship with elements of an ecosystem approach in old industrial regions is shown in Fig. 2. The proposed innovative system of regulation and development of entrepreneurship includes the following elements of the ecosystem approach: (1) application of mechanisms for the provision by the state (state bodies and state corporations) of technological (IT, industry, etc.) and infrastructural (financial, logistics, marketing, etc.) platforms to business entities; (2) “seamless” interaction and inclusion of private corporate ecosystems in a common (digital) ecosystem across the state; (3) organization of cross-border interaction between entrepreneurial and state ecosystems for the purpose of international integration, taking into account the protection of national economic interests. The purpose of the innovative system of regulation and development of entrepreneurship is the development of ecosystem elements of the business environment. The goal is achieved through the implementation of program tasks and functions by the state and entrepreneurs, incl. on the creation of ecosystems: the development of technical, technological, infrastructural, organizational and other elements of digital ecosystems; creation of conditions for the provision by the state (state bodies and state corporations) of technological (IT, industry, etc.) and infrastructural (financial, logistics,

56

O. Akimova et al.

marketing, etc.) platforms to business entities; creation of conditions for “seamless” interaction and inclusion of private corporate ecosystems in the general (digital) ecosystem of the state; organization of cross-border interaction between business and state ecosystems for the purpose of international integration, taking into account the protection of national economic interests, etc. For the interaction of the state, entrepreneurs and society, it is proposed to include in the target programs elements that together form the ecosystem of the modern innovative economy: (a) ecosystem elements of interaction between the state and society—services of state and municipal services, digital infrastructure, ecosystem social platforms; (b) ecosystem elements of interaction between the state and entrepreneurs—digital infrastructure, ecosystem platforms, public and municipal services; (c) ecosystem elements of interaction between entrepreneurs and society—ecosystem social and commercial services, ecosystem social and commercial platforms. To achieve this goal, within the framework of the developed mechanism, it is assumed that the state and entrepreneurs perform certain functions. For the state, these are the following functions: inflation targeting—this is necessary to stabilize the economic situation in the country; antimonopoly policy—this will create favorable conditions for the development of competition and increase the efficiency of entrepreneurial activity; provision of high-quality public services—this will create favorable conditions for interaction between the state and entrepreneurs; minimization of external threats—this is necessary to maintain the stability of foreign economic activity; support for the creation of innovative ecosystems. The functions of entrepreneurs in this mechanism are as follows: social and environmental responsibility—this will allow establishing a dialogue between entrepreneurs and society and will help improve the environmental situation; manifestation of investment activity—this will serve as an impetus for the launch and implementation of joint infrastructure projects with the state; the manifestation of innovative activity and the introduction of innovations—this will make it possible to implement the concept of innovative orientation of the Russian economy; design and development of ecosystems. The methods for developing the business environment within the framework of this system are as follows: cooperation between the state and entrepreneurs; protecting the interests of society;

Entrepreneurship Developmentin the Old …

57

Fig. 2. Innovative system of regulation and development of entrepreneurship with elements of the ecosystem approach [comp. Auth.]

58

O. Akimova et al.

joint opposition to external threats. The following are proposed as program activities for the development of the business environment within the framework of the innovative system of regulation and development of entrepreneurship: implementation of joint projects in the form of public-private partnership; implementation of joint projects to develop infrastructure for doing business; implementation of common innovative projects for the design and development of innovative ecosystems. The following stages of development of the business environment are distinguished: identification of common threats to the development of entrepreneurship and intensive economic growth; search for common ground of interests and goals of the state and entrepreneurs; identification of promising areas of cooperation between the state and entrepreneurs to overcome common threats; practical implementation of areas of cooperation.

3 Conclusion The main problem of the development of old industrial regions in modern conditions is the impossibility of replacing obsolete depressive industries with modern, fast-growing ones due to the development of creative small business. The lack of dialogue between the state and business further exacerbates the situation. In order to slow down the shrinkage of old industrial regions, it is necessary to develop entrepreneurship through the integration of elements of the ecosystem approach. As a result of the application of an innovative system of regulation and development of entrepreneurship with elements of an ecosystem approach, it is possible to achieve the development of an entrepreneurial environment and entrepreneurship in old industrial cities. The developed mechanisms allow streamlining and systematizing the functions and relations of the state, entrepreneurs and society within this system, as well as establishing successful interaction between economic entities and coordinating efforts towards achieving the goals of intensive economic growth and development of the business environment and entrepreneurship in old industrial regions. The innovative system of regulation and development of entrepreneurship proposed by the authors with elements of the ecosystem approach should be used in the development and implementation of: joint projects in the form of public-private partnerships, joint projects to develop infrastructure for doing business, and common innovative projects. Acknowledgments. The study was supported by a grant from the Russian Science Foundation No. 21-18-00271, https://rscf.ru/project/21-18-00271/.

Entrepreneurship Developmentin the Old …

59

References 1. The share of organizations that carried out technological innovations in the reporting year in the total number of surveyed organizations (p 2010 g.). Science, innovation and technology. Federal State Statistics Service, (2020). https://rosstat.gov.ru/statistics/science. Last accessed 23 Mar 2022 2. Myrdal, G.: Asian drama: An inquiry into the poverty of nations, vol. I-III, p. 850. Twentieth Century Fund, N. Y. (1968) 3. Frolov, D.P., Volkov, S.K., Akimova, O.E.: Ecosystems of old industrial cities: complexity and multifacetedness of urban shrinkage. In: Polyakov, R. (eds). Ecosystems Without Borders. EcoSystConfKlgtu 2021. Lect. Notes Netw. Syst. 474, 73–88. Springer, Cham (2021) 4. Rating of investment attractiveness of regions RAEX for 2020. RAEX-Analytics, https://raexa.ru/ratings/regions/2020. Last accessed 23 Mar 2022 5. Frolov D.P., Volkov S.K., Akimova O.E.: Shrinking old industrial cities: a research agenda for heterodox economics. Montenegrin J Econ 18(2), 103–112 6. Boschma, R., Neffke, F., Van Oort, F.: Externalities and the Industry Life Cycle: A long term perspective on regional growth in Great Britain. Draft for CEPR 5th Spring School in Economic Geography, p. 119. Utrecht (2005) 7. Verkhovskaya, O.R., Bogatyreva, K.A., Dorohina, M.V., Laskovaya A.K., Shmeleva E. V.: Global entrepreneurship monitoring: National report, p. 100. Graduate School of Management. St. Petersburg State University, St. Petersburg (2021/2022). https://gsom.spbu.ru/ima ges/1/1/report_2022_final_1.pdf. Last accessed 06 May 2022 8. The level of innovative activity of organizations (p 2010 g.). Science, innovation and technology. Federal State Statistics Service, (2020) https:// rosstat.gov.ru/statistics/science. Last accessed 23 Mar 2022 9. Bakanov S.A.: The concept of old industrial regions in historical and economic research: problems of theory and historiography. Bulletin of the Chelyabinsk State University. History 24(379), in issue66, 160–167 (2015) 10. Todtling, F., Trippl, M.: Like phoenix from the ashes? The renewal of clusters in old industrial areas. Urban Studies 41(5/6), 1175–1195 (2004) 11. Boschma R., Lambooy J.: Why do old industrial regions decline? An exploration of potential adjustment strategies. European Regional Science Association conference papers. Dublin, pp 1–26. (1999) 12. Steiner, M.: Old industrial areas: A theoretical approach. Urban Studies 22, 387–398 (1985)

Industrial Clusters and the Process of Their Self-organization Ruslan Polyakov1(B)

and Olga Brizhak2

1 Kaliningrad State Technical University, Kaliningrad, Russia

[email protected] 2 Financial University Under the Government of the Russian Federation, Moscow, Russia

Abstract. Discussions about the importance of industrial clusters and their impact on spatially localized economic systems have acquired a significant place in academia over the past quarter of a century. At the same time, previous studies have identified only a small part of externalities that have a significant impact on the behavior of firms, but only a few of them paid attention to the process of self-organization of clusters. This document is a supplement to the literature on understanding the process of self-organization of a cluster from the point of view of its fundamental causes, characteristics of the emergence, formation and development. Based on the data of the Kaliningrad region, this study empirically shows that, in comparison with the Russian Federation, the region can act as a “living laboratory”, which will make it possible to better study the self-organization model using the example of the “Shipbuilding cluster of the Kaliningrad region”. The purpose of this article is to study industrial clusters and the process of their self-organization in the region from the point of view of a circular economy. In this regard, the paper considers the evolution of the theory and methods for assessing the nature of the concentration of local business, and also studies the main parameters of the Kaliningrad region development, indicates its problems and prospects. This study will be useful for building a circular economy and planning the development of industrial clusters in it, as well as optimizing existing models for other industries and regions. Keywords: Intraindustrial · Agglomeration · Self-organization · Industry clusters

1 Introduction Discussions about the importance of industrial clusters and their impact on spatially localized economic systems have gained a significant place in academia over the past quarter century. Since Alfred Marshall in 1890 [1] pointed out that industrial districts have a significant impact on the dynamics of economic development of regions, and their intra-sectoral agglomeration (localized industries) is of particular importance for research, the world has been divided into before and after. In 1920, Arthur Pigou developed the ideas of Alfred Marshall in the form of the concept of externalities (external effects) [2]. The very phrase “external effects” in 1958 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 60–72, 2023. https://doi.org/10.1007/978-3-031-34329-2_7

Industrial Clusters and the Process of Their Self-organization

61

was introduced by Paul Samuelson [3, 4]. However, in the first half of the twentieth century, externalities were considered unimportant. This changed radically in the 1960s with a market redefinition based largely on Ronald Coase’s views on transaction costs. Later they became ubiquitous in the study of the economic nature of clusters. Today, externalities are understood as external effects that have a significant impact on the firm, which are not realized through a market transaction and do not have a certain price expression, but have an indirect positive (external economy) or negative (external dyseconomy) impact on the firm as a whole. According to Marshall [1], externalities are crucial in the formation of economic agglomerations. The science of industry clusters began with Marshall’s observation of the existence of “localized industries” that “can often be sustainable by concentrating many similar small businesses in a particular location” [5]. Thus the main reasons for agglomeration today can be considered as the following (this list is not complete and comprehensive): • • • • •

effects of proximity and co-location of firms; effects of increasing returns to scale; external effects; centripetal and centrifugal forces; imperfect competition of markets.

The above reasons form certain benefits that a firm belonging to a localized industry enjoys, and as noted by Schumpeter [6, 7] “… as soon as innovations become successful and profitable, other entrepreneurs follow them, which in turn leads to their accumulation—clustering”. At the same time, innovation from one area can stimulate innovation in related areas due to which there is: • rapid dissemination of new ideas and improvements in industries; • development of specialized suppliers of goods and services; • expansion of the pool of specialized workforce. We can consider the presence of all these three advantages as a defining feature of an industrial cluster within the framework of the Marshallian concept. At the same time, growing mobility and specialization are largely due to imperfect competition in markets, increased returns to scale and circular causality that shape economic and spatial landscapes in cities and stimulate competition between territories and regions [8]. One of the most important growth stimulators, as we can see, are economic clusters, which have spread widely in recent years, from agriculture to high-tech industries and from consumer services to business financing [9]. Since the influence exerted by cluster structures at the regional or city level is significant, the main priority in this work will be to assess their impact on the regional economy as a whole. In the following sections, we will look at the evolution of theory and methods for assessing the nature of local business concentration. Next, we will study the main parameters of the Kaliningrad region development and point out its problems and prospects. We will analyze priorities of the industrial policy of the Kaliningrad region and explore characteristic features of the emerging regional clusters, the study will result in conclusions and relevant recommendations for the regional economy.

62

R. Polyakov and O. Brizhak

2 Materials and Methods Exploring the economic causes and consequences of clustering firms and households in regions, cities and commercial areas, many economists such as Yorgos Papageorgiou, Masahisa Fujita, Paul R. Krugman, Anthony Venables [10–12] came to the conclusion that it would be useless to look for a universal model that explains the different types of economic agglomerations at different “geographic scales” and “degrees of sectoral detail” that would describe the spatial form and nature of local business concentration. After all, as noted by J.-F. Thisse and Masahisa Fujita in their work “Agglomeration and Economic. Theory Economics of Agglomeration: Cities, Industrial Location, and Globalization”—“Economic activities are not pinpointed or evenly distributed across the faceless plain. On the contrary, they are very unevenly distributed across places, regions and countries, forming contour lines that vary with time and place. Just as matter in the solar system is concentrated in a small number of bodies (planets and their satellites), economic life is concentrated in a rather limited number of human settlements. Moreover, parallel to the major and minor planets, there are large and small settlements with very different combinations of firms and households. Although these phenomena are universal, they are still in search of a general theory” [13]. Trying to uncover the main economic reasons for the existence of peaks and troughs in the spatial distribution of population and wealth, many economists have created comprehensive models for various cluster sizes in alternative market structures. In 1959 Isard et al. [14, 15] made a pioneering contribution to empirical analysis by applying an input-output (I-O) matrix in placement methods. Using multivariate statistics, in 1970, Czamanski [16] studied groups of industries for their clustering and found that industries are interconnected by formal flows of goods and services, regardless of spatial proximity, and he showed that firms can also interact over a long distance. Later in 1979, Czamanski and Ablas [17] in their work indicated that when these clusters are geographically concentrated, they should be called industrial complexes. Since 1990, there has been a surge in research in industrial areas. The research carried out by Porter [18, 19], Saxenian [20–22], Krugman [23, 24], Storper [25, 26] and others who presented how to build an effective strategy of economic and regional development. Since this period, the concept of “cluster” has become the most common to describe the phenomenon manifested in the agglomeration of firms, in the sense of industry specialization or regional concentration. The following two main types of methods are used to identify spatial clusters in economics (Fig. 1): • quantitative identification methods; • qualitative methods using intuitive identification (Table 1). The cluster identification process is time and resource intensive and is most appropriate when the results are critical to decision making. In practice, data availability and the use of cartography often affect the effectiveness of the associated assessment method. In this regard, regional authorities need to provide for the ability to effectively collect data for the analysis of spatial clusters (for example, as The U.S. Cluster Mapping Project; The

Industrial Clusters and the Process of Their Self-organization

63

Table 1. Methods for identifying industrial clusters. Methods

Industrial cluster

Geographic concentration/density

Industrial district

Local production systems

Cluster

Quantitative Gini

X

LQE

X

X

LQP

X X

I/O

X

X

X/FDI

X

X

Corr. A

X

X

Graphs

X

Absolute value X

X

X

Qualitative Practical (cases)

X

X

Expert commentary

X

X

Political borders

X

X X

X

X

Source Done by the author

Japan Industrial Location Center; The European Cluster Observatory; Clusterplattform Deutschland Im Überblick). At the moment, it has been proven that a continuous spatial approach is critical, since it allows one to determine the spatial extent of the emerging agglomerations, which is one of the main important characteristics of the spatial economy. In their work “The spatial economy: Cities, regions, and international trade” [12], M. Fujita, P. Krugman, A. Venables characterized the shape of the emerging clusters by performing numerical calculations of the preferred wavelength, that is, the wavelength of the dominant unstable spatial indignation. Since continuous spatial models can be used to derive endogenous spatial scales, they are undoubtedly a step towards understanding the functioning of both regional and global economies.

3 Results Rapid and steady decline in transportation costs since the mid-19th century, exacerbated by the decline of protectionism and, more recently, almost complete disappearance of communication costs, has allegedly freed economic agents from the need to be close to each other, which suggests that our economies are entering in an era culminating in the “death of distance”. If so, then the difference in location will gradually disappear

64

R. Polyakov and O. Brizhak

since the forces of agglomeration will disappear. In other words, the combined impact of technology and globalization would make the traditional geography of economic activity obsolete, and yesterday’s high and low world would miraculously become “flat”. However, the authors adhere to a different point of view and insist that the forces of agglomeration will continue to have a significant impact on the economic activity of countries and, by their influence, cause the process of clustering even in such highly turbulent conditions. Clustering is one of the key drivers of regional economic growth [27]. The development of clusters is a dynamic process determined by many internal and external factors caused by a whole range of effects due to which the cluster growth models can differ significantly from each other and in some ways will be unique. However, the clustering process, its evolutionary model, can be clearly represented (Fig. 1).

Fig. 1. Industry clustering dynamics. Source Done by the author

Usually, we consider four stages of cluster development: • • • •

Embryonic clusters—those in the early stages of growth; Established clusters—with prospects for further growth; Mature clusters—stable clusters, further growth of which is difficult; Declining clusters—those that have passed through the peak of their development and are experiencing a recession. At this stage, the clusters sometimes show the ability to revive through renewal (transformation)—and a new cycle begins.

In this paper, the authors will consider regional clusters, which, according to experts [28, 29], include a geographically limited cluster of interdependent firms, and can be used as a keyword for old concepts such as industrial areas, specialized industrial agglomerations, systems and local production systems. In addition, according to Martin and Sunley, today economists and geographers working in these fields have proposed a wide range of neologisms “to capture and represent the spatial form and nature of local business concentration” [30]. Understanding the fundamental reasons, features of the emergence and development of clusters allows the authors of the article to study regional territories and evaluate them from the standpoint of the formation of industrial clusters.

Industrial Clusters and the Process of Their Self-organization

65

The current research is carried out on the example of the Kaliningrad region. The authors have chosen this region for a reason, since it is an exclave territory of the Russian Federation and its special geopolitical position leaves a significant imprint on the possibilities for its economic, scientific, technical and technological development. The Kaliningrad region is located in Central Europe. In the south it borders with Poland, in the north and east with Lithuania, and in the west it is washed by the Baltic Sea and its bays—the Curonian and Kaliningrad (Vistula). The region is characterized by a small population size, as of 01.01.2020—1012.5 thousand people. Its logistical connectivity with the territory of Europe and at the same time developed infrastructure expands the possibilities for the development of scientific and industrial cooperation with European partners, as well as the export of products (goods, services). The established cooperative ties with a number of Russian and foreign research centers currently create additional opportunities for controlled approbation of products (services) of science-intensive and high-tech companies with the subsequent standardization of new technological solutions. The “living laboratory” model allows technology companies operating in the region to create stable starting market segments on a regional scale, and in the case of successful approbation and standardization of new solutions, market segments on a Russian scale with the subsequent export of products (works, services) to European markets. In 2021, main types of economic activity in the structure of the gross regional product were: manufacturing—22.8%; wholesale and retail trade; repair of motor vehicles and motorcycles—12.4; activity on operations with real estate—10.8; transportation and storage—9.2; construction—7.0%. Figure 2 shows that the largest contribution to the development of the manufacturing industry is made by the production of motor vehicles, trailers and semi-trailers as well as production of food products. Manufacturing of motor vehicles, trailers and… Food production Other industries Manufacturing of chemicals and chemical… Radioelectronic industry Manufacturing of furniture Manufacturing of finished metal products,… Manufacturing of other non-metallic mineral… Manufacturing of paper and paper products Manufactuting of other vehicles and equipment Manufacturing of electrical equipment

0

46,3 30,2 5,7 3,7 3,6 2,7 2,4 1,8 1,4 1,3 0,9 5

10 15 20 25 30 35 40 45 50

Source: compiled by the authors according to Rosstat «Regions of Russia. Socio-economic indicators – 2020» Fig. 2. Structure of manufacturing. Source Compiled by the authors according to Rosstat «Regions of Russia. Socio-economic indicators—2020»

66

R. Polyakov and O. Brizhak

At the moment, the region is one of the most developing in the Russian Federation. The drivers of development were not only industrial breakthroughs, but also advances in modern innovative technologies, industries, which are based on scientific and human potential. This data is confirmed by the index of industrial production for all types of activities, which in January-August 2021 amounted to 108.7% (Table 2). Table 2. Methods for identifying industrial clusters. Index

Value

Mining

97.2

Manufacturing

109.5

Electricity, gas and steam supply; air conditioning

112.9

Water supply; sewerage, waste collection and disposal, pollution elimination activities 101.9 Industrial production index

108.7

Source Done by the author

As can be seen from Fig. 3, the manufacturing index has been showing steady growth over the last 2 years.

2021 year

Russian Federation

January

February

December

October

November

August

2022 year

September

July

May

June

April

March

January

February

December

October

November

September

July

August

May

June

April

March

January

February

140,0 120,0 100,0 80,0 60,0 40,0 20,0 0,0

2023 year

Kaliningrad region

Fig. 3. Industrial production index for the period 2021–2023, in % to the corresponding month of the previous year.

The strategy of socio-economic development of the Kaliningrad region for the long term, approved by the Resolution of the Government of the Kaliningrad region No. 583 dated August 02, 2012, provides for the creation in the Kaliningrad region of: (1) a motor-car construction cluster; (2) a shipbuilding cluster; (3) a fishing cluster; (4) an amber jewelry cluster; (5) a construction cluster; (6) a transport and logistics cluster; (7) a tourist-recreational cluster; (8) a cluster in the agro-industrial complex.

Industrial Clusters and the Process of Their Self-organization

67

Stimulating the development of industrial clusters is one of the tasks of the Industry Development subprogram of the Kaliningrad region’s State Program for the Development of Industry and Entrepreneurship, approved by the Resolution of the Government of the Kaliningrad region No. 144 dated March 25, 2014. In accordance with the specified strategic planning document, a state policy is being carried out to form clusters in the following industries: (1) automotive; (2) shipbuilding (including a set of enterprises that repair ships and ships); (3) amber. The main cluster initiatives of the Kaliningrad region are presented in Table 3. At the same time, the main priorities in the field of industrial policy of the Kaliningrad region are: • sustainable balanced development of the Kaliningrad region industry; • improving life quality of the Kaliningrad region residents employed in industrial organizations; • using the opportunities of public-private partnerships for the development of the Kaliningrad region industry; • gradual decrease in the dependence of business entities in the field of the Kaliningrad region industry on the preferential regime of economic activity and other measures of state support; • consistency and complementarity of the industrial policy of the Kaliningrad region and the Russian Federation.

Table 3. Cluster initiatives of the Kaliningrad region. Name of the cluster Development prerequisites

Suggested results

Shipbuilding cluster • Presence of centers of • Accelerated development of the competence in the field of shipbuilding and engineering shipbuilding: shipbuilding industries, as well as the applied enterprises, as well as research and development educational institutions of higher industry • Implementation in the education, carrying out applied Kaliningrad region of projects research and experimental for the production of components development on shipbuilding for ships and vessels topics • Creation of engineering centers • Geographical location of the in the Kaliningrad region with Kaliningrad region (access to the specialization in the field of Baltic Sea) shipbuilding and (or) production of components for ships and vessels • Increase in the share of civil products in the shipment of the shipbuilding industry of the Kaliningrad region

68

R. Polyakov and O. Brizhak

The main goals of the industrial policy of the Kaliningrad region are: • formation of a high-tech, competitive industry, ensuring the transition of the Kaliningrad region economy to the innovative type of development; • ensuring employment of the population and increasing the real incomes of the Kaliningrad region residents employed in industrial organizations. Also among the main priorities of the Kaliningrad region are such areas as sustainable development of each direction of industry, improving life quality of the residents who work in the industrial sector, development of public-private partnerships for the growth of industrial indicators, a gradual decrease in the dependence of business entities on preferential treatment of economic activities and other measures of state support, full compliance of the measures proposed by the industrial policy of the Kaliningrad region with the measures proposed by the Russian Federation. The main goals of the industrial policy of the Kaliningrad region are creation of a high-tech industry, competitive in the world market, which will ensure sustainable movement of the Kaliningrad region towards an innovative type of economic development, increase in the share of the Kaliningrad region residents employed in industry, and increase in their living standards. To achieve these goals, the Kaliningrad region has already created amber and shipbuilding clusters.

4 Discussion Due to historical and geographical reasons, shipbuilding has for a while become one of the priority industries in the Kaliningrad region. On 07.11.2018 Federal State Educational Institution of Higher Education “Kaliningrad State Technical University” together with the cluster development center of the Foundation “Center for entrepreneurship support of the Kaliningrad region” (microcredit company) officially registered a specialized organization of the cluster “Cluster of ship-building and ship repair of the Kaliningrad region”. The cluster includes 33 organizations; more than 5000 people are employed at the enterprises of the cluster. Cluster residents have an opportunity to build vessels for a wide variety of purposes. Baltic Shipyard Yantar is one of the members of the cluster, due to which not only the civil direction of shipbuilding closes, but the military-industrial one as well. Main specialization of the cluster members: construction of ships, vessels and boats, floating structures, pleasure and sport boats. The cluster organizations have an extensive partner network: they have agreements with the largest world-renowned manufacturers of ship and technological equipment in Russia, China, Germany, the USA and other countries. Thanks to the Cluster Development Center, cluster members can establish contacts with enterprises in South Korea through the Korean-Russian Business Council and the Busan Shipbuilders Association. Currently, the Government of the Kaliningrad region continues to expand the industrial shipbuilding cluster. It is planned that in the near future the cluster will be joined by other shipbuilding and ship repair organizations, as well as manufacturers of equipment for ships.

Industrial Clusters and the Process of Their Self-organization

69

As of 2017, the residents of the cluster manufactured products and performed services worth more than 10 billion rubles. The volume of investments in the industry amounted to more than 0.5 billion rubles in 2019, and the total turnover—1.378 million rubles. Main directions of the cluster development: • growth of competitiveness and economic opportunities of the organizationsparticipants of the cluster; • cooperation of the cluster members should lead to equal broad access to innovations, as well as provide technology transfer; • the industry should become attractive for highly qualified personnel, as well as contribute to the development of a system of training and advanced training of scientific, engineering and technical, managerial and production personnel; • development of all components of the cluster infrastructure; • creation of a base of possible joint investment projects for the cluster members to launch, as well as simplified access of the cluster members to financial (credit) resources and government support measures; • promoting the development of small and medium-sized businesses in the cluster; • representing and increasing the competitiveness of the cluster members in the domestic and international markets. It is already the case when the region has a network of strong industrial shipbuilding and ship repair enterprises, with the joint work of which a synergistic effect will appear, a system of state support measures has been built, a historically strong system of training highly qualified personnel for the industry and related sectors, as well as the presence of a developed infrastructure (Kaliningrad sea trade port, a cruise terminal in Pionersky, a railway ferry complex in Baltiysk) with convenient access to key domestic and foreign markets. Key directions of scientific, technical and industrial development of the cluster: • Increasing the competitiveness and economic potential of the cluster members; • Creation of conditions for wide access to innovations, new technologies and technical solutions, provision of technology transfer through cooperation of the cluster members; • Creation of conditions for attracting highly qualified personnel to the shipbuilding industry and related sectors, as well as the development of a system for training and advanced training of scientific, engineering, technical, managerial and production personnel; • Modernization and comprehensive development of the cluster infrastructure; • Creation of opportunities for launching joint investment projects, including simplifying the access of the cluster members to financial (credit) resources and government support measures; • Promoting the development of small and medium-sized businesses in the cluster; • Foreign economic integration and growth of the competitiveness of the cluster members, based, among other things, on the support of the cluster projects in domestic and foreign markets. Transition from a linear economy to a circular economy requires a system change. Parts of such a change for this cluster will be: industrial design, digitalization of processes

70

R. Polyakov and O. Brizhak

(for example, 3D printing), modern products, a distinctive feature of which will be complete maintainability, processing and interchangeability (for example, parts). The most rational in this aspect is to consider the future structure of this cluster from the point of view of the product chain, where the main priority should be on the economy of the region’s circular cycle. In this regard, a more appropriate organizational structure would be a cluster scheme that uses modern sustainable approaches at its core. By implementing these strategic approaches, the Association “Cluster of shipbuilding and ship repair of the Kaliningrad region” will be able to achieve the following competitive advantages: • To develop strong industrial shipbuilding and ship repair facilities and/or create potential partners in the industry; • To provide more sustainable government support measures from both the region and the federal government; • To build an effective system for training highly professional personnel for the shipbuilding industry and related sectors of the regional economy; • To develop a transport infrastructure with a high supply of capacity (Kaliningrad sea trade port, a cruise terminal in Pionersky, a railway ferry complex in Baltiysk) with convenient access to key markets; • To establish a model for sustainable potential growth of the regional economy. Meanwhile, transition to sustainable development also presupposes a radical change in organizational behavior and this often affects such processes as energy supply, transport, production, distribution of products and services. A typical element of the transition to sustainability, as indicated by the author above, is socio-institutional changes, that is, changes in structures, rules, customs, standards, production methods and consumer behavior.

5 Conclusion The results of the study show that today’s transition to sustainable development was primarily triggered by radical technological changes, which in turn launched a new cycle in the field of industrial innovations, and also dramatically affected the energy transition of all sectors of the economy. At the same time, the social and institutional framework was rethought, including cluster formations, which immediately affected the changes in the self-organization of such structures. Today it is clear that discussions about the importance of industrial clusters and their impact on spatially localized economic systems have gained a significant place in academic circles. Therefore, in this work, the author also turned his attention to previous studies, which revealed only a small part of externalities and at the moment can only partially reveal the factors of behavior of firms that can describe the cluster process, while the issues of self-organization of such structures in a new circular economy are very little ad-dressed. This document is an addition to the literature on understanding the process of selforganization of a cluster from the point of view of its fundamental causes, features of the emergence, formation and development. Based on the data of the Kaliningrad region,

Industrial Clusters and the Process of Their Self-organization

71

this study empirically shows that, in comparison with the Russian Federation, the region can act as a “living laboratory”, which will make it possible to better study the selforganization model using the example of the Associations “Cluster of the Kaliningrad region amber industry” and “Cluster of shipbuilding and ship repair of the Kaliningrad region”. Today, the new paradigm of the circular economy brings to the fore the creation of better structures for cluster development, within which businesses, government authorities, citizens and technology companies initiate and actively develop bold projects, generate creative ideas that help innovative creativity and improve the tools of self-organization of high-tech industries based on new organizational principles [31, 32]. At the same time, development of such an environment requires from any regional economy, including the Kaliningrad region, a new, more innovative basis [33], which could act as a basic platform and be able to flexibly evolve in a rapidly changing world, as well as contribute to an efficient development and prosperity. By analyzing the regional aspects of the Kaliningrad region development, the author of this work has identified the priority directions of the sectoral development of the region under study, and also proposed a conceptual model of its growth driver. Thus, the work contributes to the existing discussion about the development directions of industrial clusters and shows specific growth points within the process of their self-organization by conducting such an analysis through the prism of a circular economy.

References 1. Marshall, A.: Principles of Economics. An Introductory Volume. The Online Library of Liberty (1890) 2. Pigou, C.: The Economic of Welfare (1920). Library (1920) 3. Samuelson, P.A.: Aspects of public expenditure theories. Rev. Econ. Stat. (1958). https://doi. org/10.2307/1926336 4. Samuelson, P.A., de V. Graaff, J.: Theoretical welfare economics. Econ. J. (1958). https://doi. org/10.2307/2227559 5. Coase, R.H.: The problem of social cost. J. Law Econ. (1960). https://doi.org/10.1086/466560 6. Schumpeter, J.A.: Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. Martino Pub (1939) 7. Schumpeter, J.: The Theory of Economic Development. Harvard University Press (1934) 8. Scott, A.J.: World Development Report 2009: Reshaping Economic Geography. Oxford University Press (2009) 9. Yang, Z., Hao, P., Cai, J.: Economic clusters: A bridge between economic and spatial policies in the case of Beijing. Cities 42, 171–185 (2015). https://doi.org/10.1016/j.cities.2014.06.005 10. Papageorgiou, Y.Y.: Models of agglomeration. Sist. Urbani 3, 391–410 (1983) 11. Fujita, M., Krugman, P.R., Venables, A.: The Spatial Economy: Cities, Regions, and International Trade. MIT Press (1999) 12. Fujita, M., Thisse, J.-F.: Economics of Agglomeration: Cities, Industrial Location, and Regional Growth. Cambridge University Press (2002). https://doi.org/10.1017/CBO978051 1805660 13. Fujita, M., Thisse, J.-F.: Economics of Agglomeration: Cities, Industrial Location, and Globalization, 2nd edn. Cambridge University Press (2013). https://doi.org/10.1017/CBO978113 9051552

72

R. Polyakov and O. Brizhak

14. Isard, W.: Industrial Complex Analysis and Regional Development. Technology Press of the Massachusetts Institute of Technology (1959) 15. Isard, W., Vietorisz, T.: Industrial complex analysis and regional development. Pap. Reg. Sci. 1(1), 227–247 (1955) 16. Czamanski, S.: Study of Clustering of Industries/Institute of Public Affairs. Dalhousie University, Halifax, Canada (1974) 17. Czamanski, S., Ablas, L.A.D.Q.: Identification of industrial clusters and complexes: a comparison of methods and findings. Urban Stud. 16(1), 61–80 (1979) 18. Porter, M.E.: The Competitive Advantage of Nations: With a New Introduction. Free Press (1990) 19. Porter, M.E.: Clusters and the new economics of competition. Harv. Bus. Rev. 76(6), 77–90 (1998) 20. Saxenian, A.L.: Regional networks and the resurgence of Silicon Valley. Calif. Manag. Rev. (1990). https://doi.org/10.2307/41166640 21. Saxenian, A.L.: The origins and dynamics of production networks in Silicon Valley. Res. Policy (1991). https://doi.org/10.1016/0048-7333(91)90067-Z 22. Saxenian, A.: Lessons from Silicon Valley. Technol. Rev. (1994) 23. MacPherson, A., Krugman, P.: Geography and trade. Econ. Geogr. (1992). https://doi.org/10. 2307/144207 24. Krugman, P., Venables, A.J.: Integration, specialization, and adjustment. Eur. Econ. Rev. 40(3–5), 959–967 (1996). https://doi.org/10.1016/0014-2921(95)00104-2 25. Storper, M.: The Regional World: Territorial Development in a Global Economy (1997). https://doi.org/10.2307/144543 26. Storper, M.: The resurgence of regional economies, ten years later: the region as a nexus of untraded interdependencies. Eur. Urban Reg. Stud. (1995). https://doi.org/10.1177/096977 649500200301 27. He, J., Fallah, M.H.: The typology of technology clusters and its evolution—evidence from the hi-tech industries. Technol. Forecast. Soc. Chang. 78(6), 945–952 (2011). https://doi.org/ 10.1016/j.techfore.2011.01.005 28. Rosenfeld, S.A.: Bringing business clusters into the mainstream of economic development. Eur. Plan. Stud. 5(1), 3–23 (1997) 29. OECD: World Congress on Local Clusters: Local Networks of Enterprises in the World Economy. Issues Paper, Paris, 23–24 Jan 2001 30. Martin, R., Sunley, P.: Deconstructing clusters: chaotic concept or policy panacea? J. Econ. Geogr. 3(1), 5–35 (2003). https://doi.org/10.1093/jeg/3.1.5 31. Polyakov, R.K.: Lessons on technopreneurship in Kaliningrad region: regional hubs in global networks. In: IOP Conference Series: Earth and Environmental Science, vol. 689, no. 1, p. 012006. IOP Publishing (2021) 32. Brizhak, O., Polyakov, R.: Creative impetus for the development of innovation clusters of the Russian economy. In: Ecosystems Without Borders: Opportunities and Challenges, pp. 99– 104. Springer International Publishing, Cham (2022) 33. Polyakov, R., Stepanova, T.: Innovation clusters in the digital economy. In: Digital Transformation of the Economy: Challenges, Trends and New Opportunities, pp. 200–215. Springer (2020)

Business Ecosystems as Innovative Models for the Development of Modern Companies Ekaterina Kharlamova(B) , Irina Ezangina , Irina Chekhovskaya , and Sergey Sazonov Volgograd State Technical University, Volgograd, Russia [email protected]

Abstract. In the present time, in Russia, the growth in the pace of development and introduction of innovative goods and services to the market and the rapid development of digital technologies put forward new and increased requirements for models and ways of doing business. In recent years, interest has grown significantly in the latest, modern business models based on deep collaboration of companies with each other in order to obtain synergetic effects from joint work. The ecosystem is a new business format based on the cooperation of companies in the collection and exchange of information, which provides the emergence of new opportunities to enter the market at minimal cost and create effective combinations of resources and innovative proposals. This format provides a number of advantages to its participants since it unites many companies of different profiles, satisfies a wide range of needs, creates a complete environment for consumers and increases the income of companies. The paper discusses the concept and types of business ecosystems, analyzes the strategies for building business ecosystems and identifies prospects for their further development. Keywords: Ecosystem · Business model · Digitalization · Innovation · Business ecosystem · Digital ecosystem

1 Introduction The concept of “ecosystem” originated in the 1930s of the 20th century from the works of A. Tansley. Biological science views an ecosystem as a set of compactly existing living organisms in interconnection with their environment, the ecosystem is in constant energy exchange with the natural environment; i.e., it is an open system [2]. This concept was introduced directly into economics by J. Moore, according to whom an ecosystem is an economic community consisting of the company itself, its consumers, suppliers, market intermediaries, channels for the movement of goods, owners and other stakeholders, governmental and non-governmental organizations and even competitors [1]. In his report, Deloitte Touche defines business as dynamic and co-evolving communities, consisting of a variety of entities that create and receive new content in the process of both interaction and competition [3]. Business ecosystems are a more flexible form of business interactions than previously known hybrid forms of business organization, they more quickly allow one to create value © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 73–82, 2023. https://doi.org/10.1007/978-3-031-34329-2_8

74

E. Kharlamova et al.

chains, meet market needs for new products and services, but at the same time do not limit their participants in making strategic decisions [4]. In a business ecosystem, companies co-develop their potential around innovation. To promote new products aimed at meeting the ever-increasing demand and needs of consumers, and ultimately to implement the next stage of innovation, companies must resort to cooperation and competitive joint activities. The indisputable advantage of the business ecosystem is its customer focus and the desire to gain the trust of the audience, which allows the company to reduce the cost of attracting new consumers, customers, and also increase its life cycle [5]. The most famous and successful business ecosystems abroad include Apple, IBM, Amazon, Google and many others. It is important to note that one of the leading companies that began their journey in the business ecosystem is Apple, which managed to combine all the advantages and achievements of developing business ecosystems, and at the same time, gave many world-famous companies the impetus to this development. The world-famous ecosystem—Apple—covers, in its functioning, several large industries: personal computers, consumer electronics, information services and communication services. The company built its business on an extended network of component suppliers that included Motorola, Sony, Samsung and others. At the same time, the Apple ecosystem also included a significant number of customers from various market segments [6]. Among the Russian representatives of modern business ecosystems, one can single out Yandex, Megafon, MTS and many other companies that are not inferior to foreign ones in terms of significance and degree of development. In recent years, active development has been observed in the Sber business ecosystem, which has combined a number of services and created its own products, such as SberLogistics, SberSpasibo, SberMobile and others, intended not only for their consumers, but also for other third-party companies, and even the state. In contemporary economy, some business ecosystems may compete with each other for survival and market dominance. For example, the world-famous ecosystems IBM and Apple are constantly competing in the personal computer segment, X5 Retail Group, Ozon, Wildberries, Avito in the domestic retail sector. In the modern economy, the development of business ecosystems is observed in two directions: – Open ecosystems. Examples of such business ecosystems are Huawei, Xiaomi, offering the client the opportunity to build their own ecosystem by independently selecting the right devices, OS, services and applications. – Closed ecosystems (exemplified by Apple) offer an advantage for the client in the form of an all-inclusive system, providing everything at once from one account [7]. At the same time, it is fair to note that it is the competition of modern business ecosystems, unlike individual companies, that provides the most timely transformation and innovative development of industries and markets as a whole. Companies operating within the same ecosystem develop and produce their products and services that are compatible with each other. At the same time, they consolidate relationships inside their group that prevent outside market participants from penetrating into it.

Business Ecosystems as Innovative Models for the Development

75

Thus, in the modern world, traditional business models are actively transforming into business ecosystems by acquiring other companies, expanding their areas of activity and spreading their products and services to all areas of society. Modern business ecosystems create a huge potential for strategic opportunities and operational advantages for large companies. Within an ecosystem, companies can share resources with each other and create new business opportunities.

2 Methods The methodological and theoretical basis of the study are theoretical concepts and methodological developments of domestic and foreign scientists on the functioning of various innovative formations, network interactions and development of ecosystems in the modern economic space. The instrumental and methodological apparatus of the work is based on the use of a system-functional approach, within which general scientific methods were used: abstraction, analysis and synthesis, historical and logical, induction and deduction.

3 Results The main trend in the development of modern society—all services in one place—has led to the fact that IT giants began to diversify their sources of income (or, at least, their areas of activity), capturing all new segments, including those that are not even characteristic of them. Smartphone manufacturers are doing charity work, banks are doing food delivery, and the largest online retailer is opening a network of offline stores, but without sellers. Currently, seven of the ten largest companies in the world by capitalization are represented by business ecosystems. Here is a rating of the largest companies in the world by capitalization (Fig. 1). As can be seen from the hereinafter presented rating, the business models of the world’s largest companies Apple, Amazon, Google clearly demonstrate how significant the right and timely decision to build an ecosystem is for business success.

Fig. 1. Rating of the largest companies in the world by capitalization, billion dollars (February 2021) [8]

76

E. Kharlamova et al.

There are 5 key trends that stimulate the formation and development of business ecosystems in the modern economy: (1) Development of new products and services through a combination of an increasing number of technologies and innovations. (2) Development of the Internet of Things, making the physical world more accessible. (3) Companies’ focus on their core competencies and collaboration to develop innovation. (4) The growth in the number of platform business models that stimulate partnerships between companies. (5) Development of the economy of Algorithms, generating new business models of cooperation. Therefore, it can be argued that business ecosystems are now becoming the basis of the business development model of companies and a key element of the business strategy of most organizations. The development of a new digital world involves the participation of companies in 5 types of business ecosystems [8]: 1. Ecosystem Platforms The core of this ecosystem is a software platform, which is the basis for interaction between all participants. Each type of participants creates its own ecosystem: – Platform owner ecosystem; – Ecosystem of developers; – Customer ecosystem. The success of such a business model depends on the growth in the number of services and services (Offerings) offered by the platform. The more services appear that customers begin to use, the more dependent on the Platform they become and it becomes more and more difficult for them to switch to other services outside the platform. Examples of Business Platform Ecosystems are Apple, Google, Facebook, Dropbox, Airbnb, Uber, and General Electric. 2. Ecosystem of Innovations In the context of increasing competition and technological progress, companies are forced to develop cooperation. Modern progress does not give companies a chance to compete effectively in the market, relying solely on their own knowledge and experience in the development of new products and services. Therefore, there are now more and more examples of cooperation between companies within the framework of the concept of Open Innovation [7]. At the same time, the following tasks are set in the creation of innovation ecosystems: accelerating the development of new products and services, reducing the time to bring new products and services to the market, increasing the number of successful new products and services, and cutting research costs.

Business Ecosystems as Innovative Models for the Development

77

The application of the Open Innovation model allows companies to solve many problems: – Inclusion of external ideas in the innovation process. Procter & Gamble’s Connectand-Develop initiative is one of the clearest examples where 50% of a company’s innovations come from outside its walls. – Innovation Exchanges—like any other Exchanges, they allow one to connect idea providers with those who need them. – Crowdsourcing—to create innovations, companies collaborate with communities of developers and researchers. There are many platforms (Topcoder, Kaggle) where companies post their tasks, and developers compete with each other to come up with the best solutions. – Collaborative Innovation—Boeing collaborated with more than 50 design firms to produce the Dreamliner. One of the brightest examples of an industry-wide ecosystem is the approach to innovation in Banks—from building their own innovation centers to deploying an open API and attracting startups from the Fintech industry. The examples of the Innovation Ecosystem are P&G Connect and Develop, Kraft Foods, GE Open Innovation, Samsung, Riversimple [9]. 3. Ecosystem of Interests The global and rapid development of social networks and the mobile Internet in the modern world has led to the emergence of many communities of interest—from individuals to millions of participants. Therefore, here companies co-operate work with ecosystems, primarily, to promote their products and services. At the same time, such communities can serve as a tool for the development of innovations, as they are a direct channel for companies to receive feedback from consumers about the quality of purchased goods or services, as well as a source of suggestions for improving the degree of customer satisfaction. The Ecosystem of Interests is exemplified by Reddit, Renren, Edmodo, Cyworld. 4. Ecosystem of Commerce A classic example of the Ecosystem of Commerce is the supply chain. If we consider the chain of delivery of goods from the producer to the consumer, then the participants in this chain are elements of the ecosystem. In this case, innovations will be born in new business models. A key task of the Ecosystem of Commerce is to optimize the relationship between participants rather than optimize the entire ecosystem as a whole. Participants in such an ecosystem are very slow to respond to any changes and innovations. The purpose of this ecosystem is to coordinate the participants in the transaction

78

E. Kharlamova et al.

to achieve maximum individual or collective efficiency. The examples of the Ecosystem of Commerce are logistics supply chains, IT market (consultants, integrators, etc.). 5. Ecosystem of Things The creation of the Ecosystem of Things proceeds from the current trend of recent times—i.e., the reduction in the cost of devices, the development of the Internet, mobile technologies. In this connection, companies are thinking about new business models and increasing business efficiency. Three key trends should be identified that determine the further development of the Ecosystems of Things:—the emergence of new business models due to real-time access to information about the use of products and services (e.g., the cost of insurance depending on driving style);—low cost of sensors connected to the Internet, which removes barriers to their wide distribution;—big data and analytics contain huge amounts of information that companies need [10]. Examples of these ecosystems are logistics supply chains, the industrial Internet, and healthcare. Thus, business ecosystems are formed at the intersection of technologies, open standards and architecture. Any company is a member of a particular ecosystem. However, hardly every company is able to form its own ecosystems, to extract the maximum benefit and experience from their functioning.

4 Discussion For the Russian market, the creation of business ecosystems is a relatively new trend. At the same time, there is no generally accepted unified methodology for the external evaluation of companies in terms of their ecosystem. Analyzing the experience of functioning of Russian business ecosystems, it is necessary to point out the most significant of them, namely Sberbank, Yandex, VK and Tinkoff. At the same time, we note that they all develop in approximately the same directions and form a similar set of services, despite the existing differences in the key business. Here is a classification of the largest business ecosystems of modern Russia: – Banks—Sberbank, Tinkoff, VTB. This category can conditionally include the “Network of Partnerships”, among the partners of which is Gazprombank; – IT and telecom—Yandex, VK, MTS, MegaFon; – Retail and classifieds—X5 Retail Group, Ozon, Wildberries, Avito. Having determined which services are included in each ecosystem and which verticals they belong to, a total of 22 verticals can be distinguished. Three of them present the connecting elements of ecosystems: single IDs, subscriptions and loyalty programs, voice assistants. The remaining 19 are business verticals such as finance, shopping, health, mobility, etc. An analysis of the main segments in which the leading digital ecosystems compete is presented in Table 1.

Business Ecosystems as Innovative Models for the Development

79

Table 1. Basic segments of competence for digital ecosystems Digital content

Finance

Telecommunication E-commerce

Sber

Okko online cinema, Rambler Media, Sber-Sound music service

Sberbank, YuMoney service (former Yandex.Money)

Virtual mobile operator “SberMo-bile”

MTS

Kion online cinema, MTS Music services, MTS Library, WASD.tv

MTS Bank, MTS Cashback, MTS Kassa, NUUM services

Operator of cellular MTC Live and fixed-line communication, pay TV

Yandex Yandex Search, Kinopoisk

VK

Preparing to launch Yandex Mail a number of financial services on the basis of the Akropol Bank purchased in 2021

Portal Mail.ru, VK Pay service social networks VKontakte, Odnoklassniki, Moi [email protected], MyGames

Mail.ru, ICQ, “Agent Mail.ru” messengers, “TamTam”

SberMarket, DomClick, Rambler Kassa

Yandex. Market

AliExpress Russia marketplace through a joint venture with Alibaba, RDIF and USM, VK Market and Yula services

Source Based on [11]

A business ecosystem consisting of many heterogeneous services cannot exist without unifying mechanisms. One of these mechanisms is currently the multi-service subscription, which modern consumers perceive as the next step in the evolution of loyalty programs. Of the 12 ecosystems presented above, 9 have such a subscription. Let us present the existing multi-service subscriptions of Russian business ecosystems in Table 2. As practice shows, in the modern economy, it is the subscription that is the most convenient service for consumers since it allows you to simultaneously cover a number of needs and at the same time significantly save the budget due to existing discounts, bonuses, increased cashback and other subscription privileges. An obligatory attribute of a multiservice subscription is access to online cinemas or music services provided to users free of charge or at a significant discount. Here, ecosystems develop in the following areas: – Creation or acquisition of media services (Kinopoisk/VK music); – Investment of financial resources in the production of own content (Yandex, Sberbank, MTS); – Inclusion of media services in the partner package: for example, Premier and discounts on IVI are included in the Fire subscription, Megogo is included in the Package and

80

E. Kharlamova et al. Table 2. Multiservice subscriptions of Russian business ecosystems, January 2022

Ecosystem

Subscription

Year of launch

Number of subscription users, 2021 (mln man)

Number of ecosystem users, 2021 (mln man)

Penetration of a subscription into the user base (%)

Yandex

Yandex +

2018

10.3

104

10

MTS

Premium

2020

7.4

79.7

9

VK

Combo

2020

4.5

90

5

Sber

SberPrime

2020

4

103

4

Tinkoff

Tinkoff

2020

1.3

19

7

OZON

Premium

2019

0.8

21.3

4

MegaFon

Megafon +

2021

n/o

74+

n/o

X5 retail group

Package

2021

n/o

72.5

n/o

Partnership network

Ogon (Fire)

2021

n/o

5+

n/o

Source Based on [12]

OZON premium subscription (besides Kion is also included), and Start and SberZvuk are included in Megafon +. Currently, it is the subscription provided by media services that is the most attractive for consumers, acting as one of the motivators for its registration. However, other privileges play a significant role here, such as free shipping. Thus, one can observe the process of development of business ecosystems in the modern Russian economy, in which companies seek to satisfy, first of all, the most urgent and frequent needs of the audience. Therefore, we can expect that in the near future, in addition to the growing competition in the field of finance and purchases, competition will increase in the following areas: – Entertainment (Ozon has agreed to cooperate with Megogo and Kion video services, including access to online cinemas in the Ozon Premium subscription); – Mobility (Tinkoff launched the sale of cars—the Tinkoff Auto service) (Fig. 2); – Health care (X5 plans to enter the online drug sales market in the near future, and Yandex is strengthening its existing positions in this vertical by obtaining a pharmaceutical license, due to which it will be able to participate in an experiment on the online sale of prescription drugs).

Business Ecosystems as Innovative Models for the Development

81

Prospects for the development of Russian ecosystems also largely depend on state regulation. Until recently, this area of activity was not regulated in any way, but the strengthening and growth of ecosystems, their entry into ever new verticals, has forced the state to pay special attention to this type of activity. In particular: – The Ministry of Economic Development is working on introducing additional measures into the legislation affecting the activities of digital ecosystems and platforms, while paying close attention to strengthening the positions of Russian market participants compared to foreign ones;

Fig. 2. Elements of the Tinkoff Bank ecosystem [13]

– The Ministry of Industry and Trade is preparing a code of interaction between marketplaces and sellers, one of the significant goals of which is to protect suppliers of goods from pressure on pricing policy from digital ecosystems; – The Central Bank plans to regulate banking ecosystems and limit companies’ investments in non-core assets; – The Federal Antimonopoly Service has developed a set of basic principles that take into account the “features of digital markets and their innovative development” and are aimed at creating an institution of self-regulation [12]. Thus, the study allows us to state that business ecosystems are a modern trend that is becoming a sustainable trend in the modern world, contributing to the development of not only individual companies but also business in general.

82

E. Kharlamova et al.

At present, most technologically advanced consumer companies are already business ecosystems. In the future, new strong ecosystems will appear in Russia, with a developed technological platform and well-integrated services. However, it can be assumed that this will happen through partnerships—according to the “Partnership Network” model. This is a challenge for existing market participants since such partner ecosystems will be able to offer customers a choice of more services including market-leading ones.

References 1. Moore, J.F.: Predators and prey: a new ecology of competition. Harv. Bus. Rev. May/June, 75–86 (1993) 2. Tansley, A.G.: The use and abuse of vegetational terms and concepts. Ecology 16(3), 284–307 (1935) 3. Moore, J.F.: Business ecosystems and the view from the firm. Antitrust Bull. 51(1), 31–75 (2006) 4. A look inside the new trends in business. https://www.fastcompany.com/3045104/a-look-ins ide-the-new-trends-in-business. Last accessed 7 Dec 2022 5. Moore, J.: The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. Harper Business, New York (1996) 6. Luke: The Apple ecosystem. Apple Magazine. https://applemagazine.com/the-apple-ecosys tem/36702 (2018). Last accessed 7 Dec 2022 7. Heimala, P., Suokas, J.: Open ecosystems—a new way to create value for customers, companies and partners. https://www.sitra.fi/en/articles/open-ecosystems-a-new-way-to-createvalue-for-customers-companies-and-partners/ (2021). Last accessed 7 Dec 2022 8. What is a digital ecosystem? https://handh.ru/post/digital_ecosystem. Last accessed 7 Dec 2022 9. Valkokari, K.: Business, innovation, and knowledge ecosystems: how they differ and how to survive and thrive within them. Technol. Innov. Manag. Rev. 5(8), 17–24 (2015) 10. Mazhelis, O., Luoma, E., Warma, H.: Defining an Internet-of-Things Ecosystem. Lecture Notes in Computer Science, vol. 7469. Springer, Berlin, Heidelberg (2012) 11. Experts named companies with signs of ecosystems. https://www.rbc.ru/technology_and_ media/01/02/2022/61f3d76f9a794775ff544309. Last accessed 7 Dec 2022 12. How Russian companies develop ecosystems—2022. https://vc.ru/u/163530-nikolay-sed ashov/349623-kak-rossiyskie-kompanii-razvivayut-ekosistemy-2022. Last accessed 7 Dec 2022 13. Online financial ecosystem centred around the needs of its customers. https://tinkoff-group. com/company-info/summary/. Last accessed 7 Dec 2022

Modeling Method in Ecological and Economic System Development of a Transport Company Julia Tagilceva1 , Elena Kuzina2 , Marina Vasilenko3(B) , Leonid Limanchuk2 , and Evgeny Nazarov2 1 Russian Customs Academy Rostov Branch, Rostov-on-Don, Russia 2 Russian University of Transport, Moscow, Russia 3 Rostov State Medical University, Rostov-on-Don, Russia

[email protected]

Abstract. The article considers the ecological and economic system of a transport enterprise, which is understood as a set of interrelated economic, technical, social and natural factors in the world around a person, the integration of economics and nature, which is an interconnected and interdependent functioning of the company and the flow of natural processes in nature. Ecological systems are the subject of research in various branches of science: biology, medicine, physics, chemistry, mathematics, economics, sociology. Recently, environmental management has become an independent section of management. As with any complex system, many research methods are applicable to the ecological and economic system. In this paper, such a method as modeling is analyzed, and the object of modeling is the control mechanisms of the ecological and economic system under conditions of uncertainty. The search for an optimal ecological and economic model in conditions of uncertainty and risk is becoming a trend phenomenon for transport companies in connection with the reorientation of relations between countries in the conditions of world economy. Many companies in the transport industry have significant structural and organizational problems. The authors propose an optimal ecological and economic model based on ensuring public safety and realizing the interests of society, the state and the transport company. Keywords: System · Environment · Modeling · Ecology · Safety · Risk · Management decision · Transport company

1 Introduction Today, humanity is trapped in acute environmental problems of its own socio-economic development. The quantitative and qualitative increase in the volume of energy and material exchange between society and the natural environment through the acceleration of the pace of scientific and technological development, the involvement of an increasing number of natural resources in economic turnover, the increase in the scale of nature use, the strengthening of anthropogenic pressure on the environment—all this has created a tense resource-ecological situation critical given the assimilation and restoration capabilities of the environment; the solution to the problem “environmental pollution has become global”. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 83–91, 2023. https://doi.org/10.1007/978-3-031-34329-2_9

84

J. Tagilceva et al.

The study of the problem of modeling and managing the sustainability of transport companies in Russia in a volatile internal and external environment is particularly relevant at the present stage of development. Currently, a large number of models are being developed aimed at solving specific problems of ensuring the sustainable development of enterprises [1], the need for assessing ecological and economic interactions based on models that collectively describe a system of environmental, social and economic processes is justified [2, 3]. The works of domestic researchers are devoted to the development of the theoretical foundations of modeling such systems: Borisov A.N., Krumberg O.A., Fedorov I.P., Diligensky N.V., Dymovoy L.G., Sevastyanov P.V., Nedosekin A.O., as well as a number of foreign scientists Adams G., Williams C., Bader J., Biegun S., Eydeland A., Wolyniec K. [4]. Modeling is one of the universal methods of scientific research, in which the system under study is replaced by a model that describes with sufficient accuracy the processes occurring in this system in order to obtain information and implement actions that allow configuring the activities of the system under study. Modeling makes it possible to evaluate the effectiveness of the proposed solutions without resorting to experimental research on a real object. Also, this method is designed to reduce the consumption of resources necessary for the design and modeling of transport facilities and communication routes.

2 Approaches to Modeling of Ecological and Economic Systems of Enterprises In the field of theoretical knowledge about the processes of sustainable development, three main approaches to the design of company development models can be conditionally distinguished, which systematize the existing variety of ideas: resource, biosphere, integrative (Table 1) [4, 5]. The use of mathematical models and methods in economics, ecology and sociology to study the processes of human activity in interaction with nature in the study of other large-scale problems has its own history. The first intersectoral model, which covered the relationship between the economy and the environment, was developed by V. V. Leontiev and D. Ford. V. V. Leontiev presents the intersectoral balance as a set of flows of goods and services, which are displayed in the input-output table and characterize the main structural changes in individual sectors of the economy. The balance method allows you to establish and coordinate resources and cost proportions in economic activity. At the same time, the laws of conservation in the balance form must be fulfilled, including the flows of natural raw materials, materials and pollutants, and the like. The basis of the idea of intersectoral balance in our time is the ability to reveal in the most detail the intersectoral relationships that develop in the process of expanded reproduction of the product. This allows us to show, on the one hand, how the products of each branch of production are used, and on the other hand, to discover the structure of production costs and newly created value. The intersectoral balance model, taking into account the environmental factor, was first built on the assumption that the costs of cleaning measures are directly proportional to the mass of pollutants treated, that is, the cost of neutralizing a unit of each pollutant is

Modeling Method in Ecological and Economic System Development

85

Table 1. Approaches to the construction of ecological and economic models of development Attribute

Approach Resource (anthropocentrism)

Biospheric (biocentrism)

Integrative (sustainable development)

The basic principle

Nature for man

Man for nature

Rational interaction of man and nature

The key idea

Nature is a source of resources to meet the growing needs of humanity

Nature is a single system that organizes itself. Man is a part of nature

The development of humanity within the development of the laws of nature

Management development strategy

Ensuring the “prosperity” of humanity through technological and technological progress

The principle of “back to nature”. Giving nature the opportunity to resume its functions by rejecting man from the benefits of civilization

Conscious restrictions on the consumption of natural resources. Meeting needs taking into account the possibilities of renewing natural resource

constant. Natural processes that reflect the dynamics of the ecosystem are not described in the model or are described to a much lesser extent than production and economic activity [5]. Using this model for variational calculations, it is possible to obtain information at the macro level regarding the sectoral structure of environmental protection costs, their impact on other indicators. Since the appearance of this model, a wide experience of its practical use has been accumulated, in particular at the regional level, and a large number of its modifications have been developed. The practical use of ecological and economic models allows us to solve a number of problems (see Fig. 1). Along with this, most projects in the field of organization or modernization of chemical production are focused on rather complex models and require large amounts of high-quality information. This causes certain difficulties for their use in order to carry out the previous operational assessment of the impact of the development of industrial and economic activities of enterprises on the environment. In this regard, it is relevant to develop relatively simple models that do not require large amounts of information and allow you to quickly assess the impact of individual steps of government and business on environmental, social and economic indicators and the consequences of the activities of enterprises.

86

J. Tagilceva et al.

Fig. 1. Ecological and economic development strategy of the company.

3 Model of the Ecological and Economic System of the Transport Company The model of the ecological and economic system of a transport company (see Fig. 2) includes four types of participants: – – – –

state (society); economic agents (enterprises interacting with transport companies); transport company environment.

The state is interested both in the “economic” achievements of transport companies and other economic agents, and in ensuring the required level of security (or minimizing the level of risk to the required limits, etc.). Their capabilities consist in establishing the conditions for the activities of enterprises (imposing fines, granting benefits, etc.). At the qualitative level, the task of the state is to choose such conditions for the activities of enterprises that would encourage the latter to choose actions that lead to the most beneficial results for the governing bodies.

Modeling Method in Ecological and Economic System Development

87

Fig. 2. An ecological and economic model of a transport company.

It is worth noting that the result of the functioning of the transport company is a service (moving). This fact determines the participation of the transport company in the production processes of other economic agents. From the point of view of management tasks, the specifics of the ecological and economic system of a transport company are, among other things, as follows: – the results of the activities of managed entities are multidimensional (there are at least two components of the results—“economic” and “environmental”) and are subject to the influence of many uncontrolled, uncertain and random factors; – the interests of various governing state bodies may not only not coincide with the interests of enterprises, but also contradict each other; – the costs of regularly obtaining reliable and complete information are quite high; – the ecological and economic system of a transport company cannot independently defend its interests, its reaction is inertial and occurs with a delay [6];

88

J. Tagilceva et al.

– significant, and in many ways decisive, are the institutional restrictions (regulatory framework) of the activities of enterprises and their interaction with each other and with public authorities. The listed characteristic features of the ecological and economic system of a transport company require their consideration when developing appropriate management mechanisms [7].

4 Mechanisms for Managing the Ecological and Economic System of a Transport Company in a Risk Environment When studying risk management mechanisms, we will assume, as is customary in the theory of active systems [6], that the structure of the ecological and economic system in which the mechanism operates is two-level (see Fig. 2). The upper level is occupied by the state represented by the environmental protection authority, municipal, regional or federal authorities. The state administers both the production, economic and other activities of transport companies and economic agents of managed entities, as well as the level of security. The state also represents the interests of society, so insurance organizations can be located at the top level [8]. The lower level of this system is occupied by economic agents and transport companies, i.e. objects whose activities carry a potential threat to the environment. The main organizational and economic mechanisms of safety management in the ecological and economic system [9] are shown in Fig. 3.

Fig. 3. Structure of the system of organizational and economic risk management mechanisms.

As can be seen from Fig. 3, mechanisms of complex risk assessment play a special role. This is due to the fact that the parameters of all control mechanisms must be adjusted depending on the observed or measured level of risk. The assessment of the level of security plays a central role in determining norms, quotas, fines in economic liability mechanisms, in determining insurance premiums in insurance mechanisms, in

Modeling Method in Ecological and Economic System Development

89

developing plans for the formation of centralized funds and the allocation of budget funds, and finally, in determining tax policy and preferential lending policy [10]. We will give a brief description of the main classes of mechanisms. Mechanisms of economic responsibility. This group of mechanisms includes a system of standards (norms, norms, quotas), deviation from which leads to certain economic sanctions (from fines to production stoppage, prohibition of construction, etc.) distributed from the company’s profits. The relevant standards relate primarily to the applied production (or construction) technologies, organizational and technical measures to ensure the safety of production, restrictions on maximum permissible concentrations, emissions or discharges. It is advisable to include the mechanisms of expertise (projects, enterprises) to the same group of mechanisms, in which the assessment of the level of safety is carried out by an expert commission, and economic responsibility is determined depending on the results of the examination. An important class consists of damage compensation mechanisms in which economic liability is directly related to the amount of damage caused by the occurrence of an emergency. The mechanisms of economic responsibility include the mechanisms of fines, mechanisms of risk payment and audit mechanisms discussed in [11]. Mechanisms of risk redistribution. Basically, these are insurance mechanisms (state, independent and mutual insurance). One of the most important problems arising in the development of insurance mechanisms is the development of procedures for determining insurance rates [12]. Mechanisms of risk redistribution include mechanisms of economic motivation and mechanisms for optimizing regional programs, namely the initiative of a transport company or other economic agents in revising existing regulations. Mechanisms for stimulating risk reduction. This includes mechanisms of preferential taxation, as well as preferential crediting of measures to improve the level of security [13]. Risk reduction incentive mechanisms include risk reduction financing mechanisms, risk reduction compensation mechanisms, mechanisms for reducing expected damage, mechanisms of economic motivation and, in part, mechanisms for coordinating the interests of management bodies. Redundancy mechanisms in case of emergencies. This includes mechanisms for the formation of reserves of labor resources (firefighters, rescuers, etc.), material resources (stocks of food, raw materials, medicines, transport, etc.), capacities for the rapid organization of production of products and services necessary to eliminate or reduce losses from emergencies. Unlike the previous classes of mechanisms aimed mainly at increasing the level of safety or reducing risk, redundancy mechanisms are aimed at creating conditions for the early elimination of an emergency situation and reducing losses from it. Insurance mechanisms can be conditionally attributed to the reservation mechanisms. Mechanisms for the formation and use of centralized funds. Here, the problem often comes to the fore not the formation of the fund, but its effective distribution [5]. Mechanisms for the formation and use of centralized funds include mechanisms for financing risk reduction and mechanisms of economic motivation.

90

J. Tagilceva et al.

The state, in the interests of society, using primarily mechanisms to stimulate risk reduction, reservation and the formation and use of centralized programs, develops a mechanism for managing state and regional programs.

5 Conclusion In modern market conditions, an important condition for the functioning of transport companies is to ensure its economic stability in relation to the final results of production and economic activity. In the domestic and foreign scientific literature, there is a large number of studies examining the impact of uncertainty, risk and crisis situations on the activities of the enterprise and the development of directions for its development. Despite the development of the theory of anti-crisis management of the enterprise and financial diagnostics, many issues related to the development of a mechanism for sustainable development of the enterprise in conditions of uncertainty remain unresolved. This fact determines the need for further scientific research in this direction, research and modeling of the sustainability of enterprise development as an economic system and a comprehensive criterion for assessing the sustainability of enterprise development.

References 1. Nekrasova, I.Yu., Romanov, A.D.: Assessment of ecological and economic security in road construction. StudNet 3, (2021) 2. Kuznetsova, S.N., Romanovskaya, E.V., Kubyshkina, E.V., Kuznetsova, A.D., Bezrukova, N.A., Tsapina, T.N.: Interrelation of ecological and economic security of the enterprise: forms and evaluation. Mosc. Econ. J. 4, 318–329 (2022) 3. Lysov, G.M.: The use of the modeling method in the design and modernization of transport infrastructure facilities. Young Sci. 37(432), 14–16 (2022) 4. Kuzina, M.A., Drozdov, N.A., Kuzina, E.L., Vasilenko, M.A., Tagiltseva, J.A.: The safe solutions system modeling in a digital technology enterprise. In: 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), pp. 21–26 (2020). https://doi.org/10.1109/ITQMIS51053.2020.9322992 5. Medvedev, V.A.: Sustainable development of society: models, strategy. Publisher Academy, Moscow (2021) 6. Vassilyev, S.N., Baturin, V.A., Lakeyev, A.V.: Ecologo-economic model and solvability of harmonization problem. In: Proceedings of IEEE International Conference on Systems. Lille: Man Cybern. 5, 339–343 (2019) 7. Mozhaisky, Y., Minat, V.N.: Methodological aspects of diagnostics of ecological and economic security of agro-industrial production. Bull. Rural Dev. Soc. Policy 2(14), 42–50 (2017) 8. Drozdov, N.A., Vasilenko, M.A., Kuzina, E.L., Tagiltseva, J.A.: Modeling of efficiency assessment for enterprises economic activity in environmental system. In: 2018 IEEE International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS), pp. 98–102 (2018). https://doi.org/10.1109/ITMQIS.2018.852 4947 9. Govorov, Y., Kalyagin, V.A., Dormidontova, T.V.: Engineering and environmental surveys. Eurasian Union Sci. 5–1(62), 39–41 (2019)

Modeling Method in Ecological and Economic System Development

91

10. Tagiltseva, J.A., Kuzina, E.L., Bortnik, O.A., Shlikov, E.E., Magomedov, S.Sh., Vasilenko, M.A., Drozdov, N.A.: Modeling the effectiveness of solutions for technogenic safety in the electrical industry. In: 2019 International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS), pp. 100–105 (2019). https:// doi.org/10.1109/ITQMIS.2019.8928324 11. Khromushina, L.A.: The role of ecological and economic security in the formation of the resource potential of agricultural enterprises. Bull. Belarusian State Agric. Acad. 2, 33–36 (2016) 12. Garina, E.P., Garin, A.P., Batsyna, Ya.V., Shpilevskaya, E.V.: Ensuring economic security of sustainable development of the machine-building enterprise. Econ., Entrep. Law 1, 37–52 (2020) 13. Hofbauer, J., Sikmund, K.: Evolutionary Games and Population Dynamics. Cambridge University Pres, Cambridge (2018)

The Ecological and Economic Transport System Influence on the Non-transport Effect Growth Marina Vasilenko1(B) , Elena Kuzina2 , Julia Tagilceva3 , Pavel Nadolinsky2 , and Marina Kuzina2 1 Rostov State Medical University, Rostov-on-Don, Russia

[email protected]

2 Russian University of Transport, Moscow, Russia 3 Russian Customs Academy Rostov Branch, Rostov-on-Don, Russia

Abstract. The negative impact on the environment of all modes of transport and the infrastructure that ensures its functioning is accompanied by significant environmental pollution. At the present time, air, water, soil, biosphere, hydrosphere, noise, radioactive and thermal pollution is the biggest problem, since it entails a threat to the environment, people, plants, animals and all living organisms. The article describes the principles of functioning and anthropogenic impact of all modes of transport on the environment and human health. The analysis of the dynamics of the production and economic activities of transport companies and the damage caused by them to the environment is carried out. The types of emissions and the main sources of physical impact of transport on the environment are presented. A comparative characteristic of the main exhaust gas emissions by means of transport in 2021 is given, the damage from their impact on the environment is analyzed. Keywords: Anthropogenic Factors · Transport · Greening · Risks · Pollution · Environment

1 Introduction The negative impact of transport on the environment is enormous, and is a global and most pressing problem [1]. Particular attention should be paid to the generation of solid waste and noise pollution. Anthropogenic changes in the relief, water and atmosphere have become irreversible. This is mainly due to the fact that a large number of roads, the largest sea and airports, railways were built without attention and even without taking into account environmental damage [2]. The purpose of this study is to propose measures to ensure a close relationship between the production and environmental activities of transport enterprises in order to increase the environmental and economic efficiency of their functioning and increase the non-transport effect. The degree of influence of the transport complex on environmental pollution, the issues of environmental and economic efficiency of transport enterprises were reflected in their works by Russian researchers: Bespalov V.I., Kuzina E.L., Lapidus B.M., Makeev V.A., Macheret D. A., Sidorenko V.F., Tereshina N.P., Khotko N.I. and etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 92–98, 2023. https://doi.org/10.1007/978-3-031-34329-2_10

The Ecological and Economic Transport System Influence

93

2 The Impact of Transport on Humans and the Environment Operating transport spreads harmful substances into various ecosystems that directly affect humans, flora and fauna [3]. The environmental consequences of the impact of modes of transport on the environment and human health occur according to the types presented in Fig. 1.

Fig. 1. Environmental impacts of modes of transport on the environment and human health.

Emissions from transport enterprises in the regions of the Russian Federation in 2021 compared to 2020, as a percentage, are shown in Table 1. The dynamics of emissions from transport enterprises in the regions of the Russian Federation in 2021 compared to 2020 in % is shown in Fig. 2.

94

M. Vasilenko et al.

Table 1. Emissions from transport companies in the regions of the Russian Federation in 2021 compared to 2020 in % Region of the Russian Federation

Example (%)

Russia total

−3.50

Moscow

−1.7

Altai region

−2.6

Stavropol region

−3.3

Moscow region

−2.7

Krasnoyarsk region

−0.1

Sverdlovsk region

5.1

Saratov region

−1.1

St. Petersburg

−3.50

Fig. 2. Dynamics of emissions from transport enterprises in the regions of the Russian Federation in 2021 compared to 2020.

As can be seen from Fig. 2, in general, there is a 3.5% decrease in the negative impact on the atmosphere from mobile sources in the country. This is primarily due to the fact that old models of vehicles are being decommissioned, the share of vehicles with more environmentally friendly engines is increasing, and vehicles are switching to natural gas fuel. At the same time, in a number of regions, the indicator of the total mass of emissions increased. These regions include: Sverdlovsk region (emissions from transport increased by 5.1% in the region), St. Petersburg (+0.3%), Bashkortostan (+6.4%). This is primarily due to the fact that the system of operation and environmental control of vehicles is poorly developed in the regions. The number of atmospheric pollution per capita by regions of the Russian Federation in 2021 kg/person shown in Fig. 3. In 2021, the average resident of Russia accounted for 152.6 kg of harmful substances emitted into the atmospheric air by transport. At the same time, the smaller the number of people living in a particular region, the greater the mass of emissions per inhabitant.

The Ecological and Economic Transport System Influence

95

Fig. 3. Number of atmospheric pollution per person by regions of the Russian Federation in 2021.

According to Fig. 3, the Krasnoyarsk Territory, Sverdlovsk Region and Altai Territory account for the largest amount of emissions [4].

3 Environmental Risk Assessment of Transport Enterprises The problem of improving the environment has grown from national to international and has become the subject of constant attention of the United Nations [5]. At present, the problem of the preservation of the environment in the process of production activities of transport companies is ripe [6]. The algorithm for assessing the environmental risk of transport enterprises is shown in Fig. 4. It is not an easy task to choose the most effective in terms of “environmental” criteria for transport management. The goal of management is to prevent exceeding the permissible pollution standards, and the achievement criterion is to reduce the risk of hazardous environmental pollution [7]. The problem of environmental protection, rational use of natural resources in order to increase the non-transport effect requires the search for optimal solutions in the provision of transport services, in the planning of organizational and technical environmental measures and in the management of environmental risks [8, 9]. The economic environmental decision should have the following form: R = Ak + Bn + Cm

(1)

96

M. Vasilenko et al.

Fig. 4. Algorithm for assessing the environmental risk of transport companies.

where R Ak Bn Cm

economic environmental decision of the transport company; production processes carried out by the transport company; elements of the production processes of the transport company; components of the environment.

In accordance with the Environmental Strategy, the priority areas for the development of transport enterprises include environmental protection and minimization of the negative impact of the company’s activities on the environment [10, 11].

4 Conclusion The environmental situation around the world is deteriorating daily, therefore, in the Russian Federation, the creation of conditions for federal and regional environmental risk management is of great importance, among which are the following: accelerated

The Ecological and Economic Transport System Influence

97

depreciation; benefits (deductions) in taxation; subsidies, grants and government programs; formation of sources of accumulation of funds for financing and stimulating the development of environmental protection measures when planning the net profit of transport companies; introduction of concessional lending to objects of environmental significance; improvement of the pricing mechanism through tax deductions in the calculation of indirect taxes; planning the creation of enterprises for the processing and disposal of waste at the expense of the accumulation fund of the transport company [12, 13]. Thus, the greening of transport activity in the direction of increasing the non-transport effect is possible both with the application of institutional and structural changes, and with the use of an environmentally oriented management method [14, 15]. As a result, it is possible to ensure the interest of each transport company in environmental protection in the face of growing profitability.

References 1. Gosudarev, V.M.: Assessment of economic risks in the transport system (theoretical aspect). Sci. Problems Water Transp. 21, 156–164 (2007) 2. Drozdov, N. A., Vasilenko, M.A., Kuzina, E.L., Tagiltseva, J.A., Galkin, V.A., Prokopchuk, V.Y., Korenyakina, N.N., Laponogova, A.A.: A systematic approach to modeling sustainable production and economic activities of transport organizations. Issue: E3S Web Conference, Vol. 244, XXII International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies (EMMFT-2020) Article Number: 11045 Number of page(s): 9 Section: Energy Management and Policy. Published online 19 March 2021. References NASA ADS Abstract Service (2021). https://doi.org/10.1051/e3sconf/202 124411045 3. Bychkova, A.A.: Measures to reduce environmental risk in transport in the regions. Bull. GUU 8, 65–73 (2021) 4. Vasilenko, M.A., Kuzina, E.L., Tagiltseva, Y., Galkin, V.A., Sheremetyeva, N.A.: Approaches to assessing the functioning of elements of the socio-ecological-economic system in transport. Quality. Innov. Educ. 1(165), 78–85 (2020) 5. Golikov, R.A., Surzhikov, D.V., Kislitsyna, V.V., Steiger, V.A: The impact of environmental pollution on public health (literature review). Sci. Rev. Med. Sci. 5, 20–31 (2017) 6. The Constitution of the Russian Federation (adopted by popular vote on 12.12.1993 with amendments approved during the all-Russian vote on 01.07.2020). The official Internet portal of legal information. http://www.pravo.gov.ru. Last Accessed 25 February 2023 7. Voronchikhina, D.N.: Modern concepts of environmental safety. Problems of environmental policy implementation in the Russian Federation. Discourse-Pi 4 (37), 79–96 (2019) 8. Vasilenko, M.A., et al.: The algorithm of organizational and technical environmental decisions development in transport organizations. Transp. Res. Procedia 63, 2719–2726 (2022). https:// doi.org/10.1016/j.trpro.2022.06.314 9. Vasiliev, N.A., Medunitsina, N.D.: Ecology and morbidity of respiratory organs. Russian Med. J. 1, 13–14 (2017) 10. Galaburda, V.G., Proskurin, D.S.: Criteria for evaluating the efficiency and quality of various modes of transport. Econ. Railw. 5, 86–9511 (2013) 11. Kuzina, E.L., Vasilenko, M.A., Tagiltseva, J., Drozdov, N., Parkhomenko, R., Korzhin, S., Prokopchuk, V., Kozlov, A.: Extra-transport effect in choice of methods for assessment of transport competitiveness. In: Conference: International Scientific Conference «Social and

98

12.

13.

14. 15.

M. Vasilenko et al. Cultural Transformations in the Context of Modern Globalism» dedicated to the 80th anniversary of Turkayev Hassan Vakhitovich. October (2020). https://doi.org/10.15405/epsbs.2020. 10.05.342 Vasilenko, M.A., Kuzina, E.L., Tagiltseva, Y., Drozdov, N.A.: Substantiation of the need to study the influence of transport factors on the country’s economy in the conditions of advanced marketing. Quality. Innov. Educ. 5(156), 161–169 (2018) Kuzina, E.L., Vasilenko, M.A., Kurenkov, P.V., Tagiltseva, J.A.: Evaluation of transport security taking into account extransport effect «FarEastCon» International Scientific Conference “FarEastCon”, IOP Conference Series: Earth Environment Science 666, 062080. https://doi. org/10.1088/1755-1315/666/6/062080 On the state of the natural environment in the Russian Federation in 1999: State Report. Moscow: REFIA: 198 (2000) Reshetko, N.I., Vasilenko, M.A., Belozerov, V.L., Vakulenko, S.P., Kurenkov, P.V., Zmeškal, E.A., Chebotareva, E., Solop, I.A., Kuzina, E.L., Barashyan, V.Y.: Company product policy for the production and sales of electric vehicles. In: «Transcom-2021» 14th International Scientific Conference in Sustainable, Modern and Safe Transport, 26–28 may 2021, Zhilin, Slovak Repablic/ransportation Research Procedia. Vol. 55, pp. 356–361 (2021). https://doi. org/10.1016/j.trpro.2021.06.041

The Russian Federation is on Its Way to a Climate-Neutral Economy Sergei Muzalev1(B) , Tatiana Muzaleva2 , Marina Gordova1 , and Irina Demina1 1 Financial University Under the Government of the Russian Federation, Moscow, Russia

[email protected] 2 Academy of Management of the Ministry of Internal Affairs of Russia, Moscow, Russia

Abstract. Against the backdrop of the accelerating pace of global warming due to the influence of anthropogenic and technological factors of human progress, the inevitable 4th energy transition is brewing in the world—the transition to renewable energy sources and building models of state economies aimed at minimizing damage to the planet’s ecosystem. Today, almost all world countries are convinced of the need for a speedy transition to a climate-neutral economy, including the Russian Federation. This work is devoted to the analysis of current trends in the transition to a climate-neutral economy in the Russian Federation, identification of the problems of introducing and using various types of green energy, as well as a review of existing and promising approaches to solving these problems. Among them, it is worth noting the creation of the necessary infrastructure for the operation of electric transport and government support measures for the active introduction of electric public transport. Keywords: Global Warming · Climate-Neutral Economy · Green Energy · Renewable Energy Sources · Alternative Energy · Carbon Neutrality · Green Economy

1 Introduction Today, considering the colossal intensification of scientific and technological progress and, as a result, the sharply increasing anthropogenic and man-made negative impact on the environment, the problem of global warming appears in the forefront of the entire world community. The pronounced dependence of the economies of states on exhaustible energy sources, the low focus of the vast majority of industrial sectors on reducing the carbon footprint, population growth, the geopolitical situation in the world and the ensuing energy and food crises—all these factors have a negative impact on the environment. According to the World Meteorological Organization (WMO), greenhouse gas mole fractions reached a new high in 2020 with a globally averaged surface mole fraction of carbon dioxide (CO2 ) of 413.2 ± 0.2 ppm (parts per million), methane (CH4 )—1889 ± 2 ppb (parts per billion) and nitrous oxide (N2 O) at the level of 333.2 ± 0.1 ppb, which is 149%, 262% and 123% of the pre-industrial level (1750), respectively [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 99–111, 2023. https://doi.org/10.1007/978-3-031-34329-2_11

100

S. Muzalev et al.

An increase in the share of greenhouse gasses has led to a negative trend in the overall increase in global ambient temperature compared to the pre-industrial period (Fig. 1). According to the data presented, in 2021 the global temperature was 1.11 ± 0.13 °C higher than the average for 1850–1900. 2021 is the seventh year in a row (2015–2021) when the global temperature was more than 1 °C higher compared to the pre-industrial period [2].

Fig. 1. Dynamics of global temperature change compiled by WMO based on data from six international databases [2]

One of the significant consequences of global warming is the rise in the level of the world ocean, while the dynamics of growth is increasing every year. Between January 2013 and January 2022, global sea level rose by an average of 4.5 mm/year, which is 1.6 times higher than in 2003–2012. And 2.1 times higher than in 1993–2002 [1]. Such dynamics of global sea level growth directly threatens the island and coastal territories of states with frequent flooding and increased natural climatic phenomena, which, according to the UN World Food Program (WFP), increases the number of crop and livestock losses, thereby exacerbating the food crisis [3]. The problem of global warming has not bypassed the Russian Federation and, most of all, the Russian Arctic. In the area of the Arctic Ocean, due to the increase in temperature, the area of ice cover in the period from 1970 to 2010. Decreased from 5.9 million km2 to 4.7 million km2 [4]. Over the past 30-year period, the increase in atmospheric temperature in various areas of the Arctic Ocean ranged from 2 to 4 °C and led to active melting of glaciers [5]. Also, global warming in the Arctic leads to an increased release of methane in permafrost regions, increasing the greenhouse effect, thus creating a vicious circle (Fig. 2). Changes in the Arctic climate have a negative impact on the functioning of the main transport artery of the Russian Arctic—the Northern Sea Route (NSR). Due to the increase in drifting ice floes and icebergs, the danger of escorting ships increases, which inevitably leads to an increase in the cost of navigation in this region, in particular, to an increase in the cost of ships, justified by their strengthening and modification. “Strengthening and modification of vessels reduces their speed of passage along the NSR

The Russian Federation is on Its Way to a Climate-Neutral Economy

101

Fig. 2. Vicious circle of global warming due to melting glaciers (compiled by the authors on the materials of [5])

by 5–10%, and almost negates the gain in the distance along the route in comparison with alternative southern directions” [6]. “Global warming is spreading to the rest of our country” [7]. The average annual temperature of the Black Sea coast from 1980 to 2020 turned out to be three times higher compared to 1937–2017. To determine the predicted indicators of the growth of the average annual temperature, one should refer to the calculations of Roshydromet based on the global climate models CMIP5 (Fig. 3).

Fig. 3. Projected indicators of the average annual temperature growth in the middle lane and regions of the far north of Russia, max. °C (compiled by the authors based on [8])

The above negative consequences of global warming in the aggregate are a significant obstacle to the prosperity and development of the state and society, while the right way to neutralize the causes of global warming will be the transition of the state economy to a

102

S. Muzalev et al.

climate-neutral “green” economy with the development of an energy network of renewable energy sources, reducing the carbon footprint existing and projected industries, as well as the widespread introduction of a circular economy. The purpose of this study is due to the analysis of existing trends towards the transition to a climate-neutral economy in the Russian Federation, the identification of the problems of introducing and using various types of green energy, as well as the necessary review of existing and promising approaches to solving these problems.

2 Methods and Materials Green economy is a model of economic development, the main distinguishing feature of which is the desire to reduce the negative impact of human economic activity on the environment. An important part of such an economy is determined by the use of energy, which has reduced or zero negative impacts on nature and climate. Note that there is no single approach to the definition of this energy. In various studies, there are various names of such energy: alternative energy, renewable energy and green energy. For further research, it is necessary to form definitions on this issue. “Alternative energy is a combination of many ways to obtain, transmit and use energy that is not related to traditional heat and hydropower methods, as well as nuclear energy. Alternative energy is aimed at reducing the risk of harm to the environment. Energy of this type includes: hydrogen-bioenergy sphere, mini hydroelectric power plants, solar and wind power plants, geothermal and thermonuclear energy of controlled nuclear fusion” [9]. “Renewable energy sources or renewable energy (renewable energy) considers the available unlimited or renewable resources: wind, solar, water, tidal, geothermal heat” [10]. The key factor of the designated energy sector is the sustainable use of renewable resources as a result of natural processes or processes that are constantly taking place in the natural environment to generate energy. The main types of renewable energy work at the expense of the sun and wind (hydroelectric power plants, geothermal bioenergy stations). “Green energy (Low-carbon energy) is a low-carbon methods of obtaining energy” [11]. Reducing the negative impact on the environment is the main principle of green energy. The maximum reduction or elimination of greenhouse gas emissions into the environment, which is the main cause of global warming, is determined by the primary goal of this economy. Green energy includes alternative, renewable and traditional types of energy that significantly reduce or eliminate the carbon footprint as a result of the financial and economic activities of economic entities. When conducting the study, the materials of the World Meteorological Organization and Roshydromet were used as initial data. In addition, the regulatory documents of the UN Framework Convention on Climate Change, the Kyoto Protocol to the Framework Convention, the Doha Amendment to the Kyoto Protocol, the Paris Agreement, as well as the Strategy for Social and Economic Development of the Russian Federation with Low Greenhouse Gas Emissions until 2050 were analyzed. The study was carried out using the method of content analysis of scientific publications and reports with their subsequent study, evaluation and systematization of data.

The Russian Federation is on Its Way to a Climate-Neutral Economy

103

3 Discussion The first significant step towards reducing the greenhouse effect and slowing down the global effect was the United Nations Framework Convention on Climate Change (hereinafter referred to as the Convention), concluded in 1992 and ratified by 197 countries, including Russia. “The Convention enshrines the concepts of global warming and the greenhouse effect, clearly identifies problems due to global warming, and also indicates the direct impact of anthropogenic factors on the acceleration of global warming” [12]. Subsequently, the Kyoto Protocol to the Framework Convention was adopted, directly obliging certain countries to reduce or limit greenhouse gas emissions. “The protocol limited the period for reducing emissions until 2020 (together with the introduced Doha Amendment to the Kyoto Protocol)” [13]. To further move towards minimizing global warming, as a logical continuation of the Framework Convention, the Paris Climate Agreement was adopted in 2015. “The agreement set a long-term goal to limit the increase in global temperature to less than 2 °C compared to the pre-industrial level and take measures to keep this indicator within 1.5 °C” [14]. Although the Paris Agreement did not oblige the countries to limit emissions quantitatively, the countries themselves turned out to be extremely motivated in achieving the goal of this agreement—the Russian Federation was among them. In pursuance of the Decree of the President of the Russian Federation of November 4, 2020 No. 666 “On the reduction of greenhouse gas emissions”, the Government of the Russian Federation approved the Strategy for the socio-economic development of the Russian Federation with low greenhouse gas emissions until 2050 (hereinafter referred to as the Strategy) [15]. This strategy includes two scenarios for the transition to a low-carbon economy: inertial and intensive. The inertial scenario assumes the preservation of the current economic model during the implementation of the energy transition. As part of the decarbonization measures, according to the scenario, a gradual renewal of obsolete fixed assets, as well as a transition to “best available technologies” in the fuel and energy complex, is expected. At the same time, the Strategy itself states that when this scenario is implemented, it is not possible to achieve carbon neutrality on the planning horizon. The intensive scenario, which is the main one, assumes the achievement of targets for reducing greenhouse gas emissions along with the socio-economic and economic development of the country in the transition to green technologies. The implementation of this scenario, according to the Strategy, is possible subject to technical, financial and tax regulation aimed at reducing greenhouse gas emissions. The priority issues for solving the objectives of the Strategy are: carbon pricing, a system of quotas for greenhouse gas emissions, the development of regulatory documentation for the mandatory introduction and use of technologies with a low carbon footprint and high energy efficiency, adjustment of the mineral extraction tax, etc. No less effective would be: – introduction of certificates of origin of energy, confirming the fact of energy production at carbon-free energy facilities or facilities with a low level of greenhouse gas emissions;

104

S. Muzalev et al.

– introduction of mandatory public non-financial reporting on compliance with the environmental program of low-carbon development for large industrial companies and organizations; – widespread introduction and implementation of green technologies in energy, industry and other sectors of the economy. To support the introduction of these technologies, the state will provide financing assistance through special loans and funds. The development of green technologies in the electric power industry provides for “the introduction of modern technologies, the formation of combined cycle generation, nuclear power plants, hydroelectric power plants and renewable energy sources, the maximum use of the potential for reducing greenhouse gas emissions in coal-fired energy” [15]. With a full transition to innovative technologies in terms of solving climate problems, as well as the development and application of technologies for capturing, using and storing greenhouse gases, it will accelerate the implementation of the green economy. In industry, in particular carbon-intensive industries, it is planned to introduce systems for capturing, storing and reusing greenhouse gases. Strengthening and dissemination of resource- and energy-intensive technologies with a low level of greenhouse gas emissions, a transition to a circular economy is expected, ensuring the minimization of production waste, as well as their reuse as raw materials in the framework of waste-free production. In the field of transport, it is planned to carry out mass electrification of transport and the development of networks of charging stations, the transition of road transport to hybrid units, as well as the promotion of the use of transport models with zero greenhouse gas emissions. The implementation of the proposed measures will reduce the level of greenhouse gas emissions by 2050 by 60% compared to the level of 2019 and by 80% compared to 1990. Investments in the implementation of these emission reduction measures in 2022– 2030 will amount to 1% of GDP, and in 2031–2050—1.5–2% of GDP. At the same time, the authors of the strategy indicated that GDP growth due to the implementation of these projects by 2050 will exceed the volume of investments by 25%. Such dynamics allows us to judge the positive prospects for the low-carbon development of the Russian Federation at the stage of the global energy transition. Nevertheless, within the framework of the implementation of the announced low-carbon development projects, there are a number of problems, the solution of which will determine the positive outcome of the energy transition. In the energy sector, the key role is played by the development and implementation of renewable energy sources (hereinafter referred to as RES). “RES is a special prospect for the implementation of a climate-neutral economy, confirmed by environmental friendliness and a systematic reduction in the cost of electricity produced” [16]. However, in the structure of energy generation in the Russian Federation, the share of energy produced at RES facilities remains at the level of 2%. The main problem of RES is the initial high cost of investing in such innovative projects, despite the well-established trend towards a decrease in the cost of such technologies. To solve the problem, the Low-Carbon Development Strategy provides for the previously described support and financing measures. At the same time, the key obstacle to the mass introduction of renewable energy sources

The Russian Federation is on Its Way to a Climate-Neutral Economy

105

is the instability of the energy received depending on climatic conditions: in the absence of wind or a cloudy day, wind and solar power plants, respectively, stop generating energy, which leads to uneven energy production and interruptions in energy supplies to consumers. In carbon-intensive industries, it is proposed, in particular, the implementation and implementation of hydrogen technologies to reduce greenhouse gas emissions. “The amount of energy spent on production exceeds the amount of energy that can be obtained from the resulting hydrogen by 154–451%, depending on the production technology” [17]. The most common technology for producing hydrogen is the steam reforming of natural gas—methane. When implementing this method, up to half of the source gas is flared, which entails significant greenhouse gas emissions, and the energy unprofitability of the process reduces the economic feasibility of the project. Let us note a number of significant problems in the transport sector that impede the implementation of the goals of the Low-Carbon Development Strategy. In turn, the electrification of passenger vehicles in Russia is hampered by several factors: the high cost of electric vehicles, the low level of development or the complete absence of infrastructure, in particular charging stations, the low presence of international auto concerns on the market for electric vehicle models, etc. A significant obstacle is the current sanctions policy towards Russia, when the world’s major auto giants one by one leave the Russian market, which leads to a multiple rise in prices or a shortage of cars in general.

4 Research Results The problems voiced in the study are significant and their overcoming, as well as the search for optimal solutions, predetermine the successful implementation of a climateneutral economy. The Russian Federation has taken a course towards planning and implementing promising solutions to these problems. Thus, in the field of renewable energy sources, the solution to the key problem of efficient storage of the received energy to ensure the uninterrupted, stable operation of power plants and the delivery of electricity to the consumer is the creation of an efficient electricity storage system based on redox flow batteries (hereinafter referred to as PRB). The technology of redox flow batteries or redox-flow batteries originates in the 1970s. The PRB is similar in principle to the well-known fuel cells, but the main purpose of such batteries is energy storage. The battery design consists of two separate electrolyte reservoirs and a membrane-electronic unit, which is an assembly with electrodes and an ion-exchange membrane (Fig. 4). As an electrolyte in PRB, vanadium salts of various oxidation states are most common. “During the charging or discharging of the battery, energy is accumulated or released” [19]. PRBs as an efficient energy storage system for wind and solar power plants have a number of distinct advantages: • reliability and durability: the number of effective charge/discharge cycles for such batteries is more than 200 thousand, and the service life is more than 20 years;

106

S. Muzalev et al.

Fig. 4. Design of a flowing redox battery [18]

• practically inexhaustible possibilities of increasing the capacity and power by increasing the electrolyte volumes and installing additional electrode blocks; • ease of maintenance due to the separate design of the electrode unit and electrolyte reservoirs; • environmental friendliness: the electrolyte in such batteries can be recycled and reused as part of a circular economy; • the cost of electricity for such batteries is significantly cheaper than common lithiumion ones. “The development of redox batteries in the Russian Federation is carried out by many groups of scientists from Moscow State University. Lomonosov, RKhTU im. Mendeleev, IPPC RAS, IPChE RAS, while the technological level of these projects is comparable to the world, which allows us to judge the positive prospects for the use of PRB for RES” [20]. “In terms of solving the problem of the unprofitability of hydrogen production, a whole new direction of energy was identified—nuclear-hydrogen energy. Research on this topic was carried out by employees of the National Research Center “Kurchatov Institute” together with JSC “Afrikantov OKBM” [21]. As noted earlier, the most common hydrogen production technology today is the steam reforming of methane from natural gas. At the same time, taking into account the specifics of the chemistry of this reaction, in order to carry out and maintain the reaction, very large volumes of the initial natural gas are used as fuel and burned, which negatively affects the economy of the process and increases greenhouse gas emissions

The Russian Federation is on Its Way to a Climate-Neutral Economy

107

into the atmosphere. To eliminate negative factors, a technology has been developed for supplying heat to carry out the reaction of steam reforming of methane from a high-temperature gas-cooled nuclear reactor with a helium coolant. The technology is a nuclear power plant (AETS) that excludes emissions of greenhouse gases into the atmosphere. At its core, AETS is a high-temperature gas-cooled reactor and a high-temperature heat exchanger, where helium coolant heated to 750 °C (as a result of passing through the reactor core) heats a mixture of methane with steam to carry out the steam reforming reaction with hydrogen evolution (Fig. 5). The mentioned reactor is a generation IV nuclear reactor, which indicates the safety of its technology and protection against emergency situations and accidents. When considering the economics of hydrogen production by this method, its significant advantages over classical technologies become clear. “A nuclear power plant with a thermal capacity of 2400 MW (4 HTGRs of 600 MW each) is capable of producing more than 0.8 million tons of hydrogen per year in the AKM process (adiabatic methane conversion) without emitting CO2 into the atmosphere. The complex is characterized by the consumption of ~2600 million Nm3 of natural gas and 32 million tons of water vapor per year in the production process. Economic estimates show a low cost of production: less than 120 rubles. For 1 kg of hydrogen at a discount of 7%” [22]. The AETS project is currently being implemented by Rosatom enterprises (Rosenergoatom, OKBM Afrikantov JSC, RIAR JSC, VNIINM JSC and others), the commissioning of the reactor is scheduled for the early 2030s. However, in the case of considering nuclear technology, it should be mentioned that this energy sector cannot be attributed to green energy due to one key problem—the disposal of spent nuclear fuel (SNF). In the development of the nuclear industry, countries such as Canada, Finland, Germany, Italy, the Netherlands, Sweden, Switzerland, Spain, the United States and China prefer to store spent nuclear fuel in spent fuel storage facilities with subsequent disposal. “The Russian Federation is one of the 5 countries where a closed fuel cycle is implemented—SNF from thermal neutron reactors goes for processing and is used as fuel for fast neutron reactors” [23]. Fast neutron nuclear reactors, such as the BN-600 and BN-800 of the Beloyarsk NPP, are world-leading, and the high rate of progress of domestic nuclear scientists in this area indicates the prospect of using environmentally friendly nuclear energy in the future, in the implementation of a climate-neutral economy. The previously identified problems hindering the development of the electric transport segment also have different solutions. The high cost of electric vehicles in Russia is due to the shortage of the car market due to the sanctions policy, as well as due to the high import duty of 15% (for example, in other EAEU countries, electric vehicles are imported at zero duty). To solve this problem, it is necessary to look for new car suppliers from countries that have not imposed sanctions against Russia, as well as ensure the introduction of zero import duties to reduce the cost of an electric car and increase demand. “One such major supplier is China, which is the largest market for electric vehicles in the world” [24]. “There are many manufacturers of both electric vehicles and batteries for them in China (more than half of the manufacturers of batteries for electric vehicles are made in China)” [25].

108

S. Muzalev et al.

Fig. 5. The design of the HTGR reactor for hydrogen production [22]

The authors of [26] proposed a phased concept for the development of electric transport in the Russian Federation. From 2021 to 2025 it is proposed to create a network of the minimum necessary infrastructure in the form of charging stations, as well as stimulate the emergence of a sufficient number of users to promote environmentally friendly transport. In this case, special attention should be paid to the electrification of such areas where vehicles are used constantly, with high mileage and have a permanent base point: taxis, delivery services, carsharing. The economic and environmental benefits of the spread of electrification in this area will allow the creation of a network of charging stations, will be economically beneficial due to the long period of use, and will also create an initial positive impetus towards the mass electrification of transport. One of the possible and primary areas for the introduction of electric transport is public transport. A number of researchers of this problem [24] mention the positive experience of introducing electric buses. Since 2017, electric bus routes have been running on the streets of Moscow, St. Petersburg, Kazan, Yekaterinburg and other large cities, and infrastructure for charging them has been created in these cities. It should be noted that without state support in the form of benefits for connecting to the electricity grid, preferential tariffs for electricity and preferential loans for owners of charging stations, the implementation of the “clean transport” project is not possible. In the operational plan, public authorities need to pay attention to the creation of regulatory documents

The Russian Federation is on Its Way to a Climate-Neutral Economy

109

obliging them to provide adjacent territories and parking lots for construction projects with charging stations. Incentives for the population to buy and use electric vehicles, according to leading experts, are possible through concessional lending for the purchase of electric vehicles, providing benefits for owners in the form of free parking, etc. At the first stage, localized production of charging stations and, in the future, batteries and electric vehicles themselves should be developed. At the second stage of development (2026–2030), it is necessary to expand the existing base for the mass market segment in order to create a sustainable segment of the Russian market, and at the third stage (after 2030)—to cancel most of the subsidies, thereby transferring this part of the market to free development.

5 Conclusion In conclusion, we note that the challenge that all world powers face in the 21st century is unique. Never before in the history of mankind has there been such a unifying threat for all countries of the world as global warming. The scale of the possible consequences of ignoring this problem is covered in detail in the regular reports of international organizations, and today each state has finally determined that in order to ensure its continued existence, a new global energy transition is inevitable, and a climate-neutral economy is the only true model for the development of society. The Russian Federation supports and implements the goals of a climate-neutral economy. According to the Low Carbon Development Strategy, Russia must achieve carbon neutrality by 2060. Undoubtedly, the indicated path is fraught with many problems in various sectors of the economy, but the projects considered in our study in the field of alternative energy, environmentally friendly hydrogen production within the framework of the AETS and in terms of the development of transport electrification allow us to conclude that there are positive dynamics and good prospects for realizing the goal of the Strategy, namely the transition to a climate-neutral economy.

References 1. State of the Global Climate 2021. WMO-No.1290. WMO (2022). https://library.wmo.int/ doc_num.php?explnum_id=11178. Last Accessed 10 November 2022 2. Madge, G.: 2021 continues warm global temperature series. https://www.metoffice.gov. uk/about-us/press-office/news/weather-and-climate/2022/2021-hadcrut5-wmo-temperaturestatement#:~:text=Authoritative%20assessment,of%20the%20six%20data%20sets. Last Accessed 10 November 2022 3. WFP and FAO.: Hunger Hotspots. FAO-WFP early warnings on acute food insecurity: October 2022 to January 2023 Outlook. Rome (2022). https://docs.wfp.org/api/documents/ WFP-0000142656/download/?_ga=2.14391159.1391782325.1664991279-1390614435.166 4991279. Last Accessed 10 November 2022 4. Matveev, A.S., Matveev, D.O., Matveev, O.A.: Development of transport infrastructure in the Arctic is the most important condition for attracting investment to use the resource base of the macro-region. Colloquium-J. 15(39) (2019) 5. Report on the peculiarities of climate on the territory of the Russian Federation in 2019. M., 97 s (2020). https://meteoinfo.ru/images/news/2020/03/12/o-klimate-rf-2019.pdf. Last Accessed 11 November 2022

110

S. Muzalev et al.

6. Kruglov, V.V., Lopatin, M.A.: On the strategic importance of the Northern Sea Route. Military Thought 9 (2020) 7. Prihod’ko, I.A., Verbickij, A.J., Sergeev, A.J.: Analysis of climate change on the Russian black sea coast. IACJ 1, 366–383 (2022) 8. Scenario forecasts based on CMIP5 global models. Roshydromet Climate Center. https://cc. voeikovmgo.ru/ru/klimat/izmenenie-klimata-rossii-v-21-veke,.Last Accessed 11 November 2022 9. Schleeter, R.: Alternative energy use. National Geographic Society. https://education.nation algeographic.org/resource/alternative-energy-use. Last Accessed 11 November 2022 10. What is renewable energy? United Nations. Climate Action. https://www.un.org/en/climat echange/what-is-renewable-energy. Last Accessed 11 November 2022 11. Moses, M.: What is low-carbon energy? EDF. United Kingdom. https://www.edfenergy.com/ for-home/energywise/low-carbon-energy. Last Accessed 11 November 2022 12. United Nations Framework Convention on Climate Change. https://www.un.org/ru/doc uments/decl_conv/conventions/climate_framework_conv.shtml. Last Accessed 11 November 2022 13. Kyoto Protocol to the UN Framework Convention on Climate Change. https://unfccc.int/sites/ default/files/resource/docs/russian/cop3/kprus.pdf. Last Accessed 11 November 2022 14. The Paris Agreement on Climate Change. https://unfccc.int/files/essential_background/con vention/application/pdf/english_paris_agreement.pdf. Last Accessed 11 November 2022 15. Decree of the Government of the Russian Federation of 29.10.2021 № 3052-r «On Approval of the Strategy of Socio-Economic Development of the Russian Federation with Low Greenhouse Gas Emissions up to 2050. http://static.government.ru/media/files/ADKkCzp3fWO3 2e2yA0BhtIpyzWfHaiUa.pdf. Last Accessed 11 November 2022 16. Moscow School of Management SKOLKOVO Energy Center. Forecast of the Development of the Energy Sector of the World and Russia. Skolkovo, Moscow. https://energy.skolkovo.ru/ downloads/documents/SEneC/Research/SKOLKOVO_EneC_Forecast_2019_Rus.pdf. Last Accessed 12 November 2022 17. Petin, S.N.: Energy efficiency of hydrogen production and consumption. Vestnik MEI. Bulletin of Moscow Power Engineering Institute 2, 29—36 (2019) 18. Skyllas-Kazacos, M., Chakrabarti, M.H., Hajimolana, S.A., Mjalli, F.S., Saleem, M.: Progress in flow battery research and development. J. Electrochem. Soc. 158(8), 55–79 (2011) 19. Godjaeva, M.V., Kazarinov, I.A., Voronkov, D.E., Oliskevich, V.V., Ostroumov, I.G.: Flowthrough batteries based on organic redox systems for large-scale electric energy storage. Electrochem. Energy 2, 59–85 (2021) 20. Russian chemists have created a new battery design for the future. https://ria.ru/20200817/ 1575882865.html. Last Accessed 12 November 2022 21. Ponomarev-Stepnoj, N.N., Stoljarevskij, A.Ja., Pahomov, V.P.: Nuclear-Hydrogen Power. Publishing House Energoatomisdat (2008) 22. Ponomarev-Stepnoj, N.N., et al.: Nuclear energy technological complex with hightemperature gas-cooled reactors for large-scale environmentally friendly production of hydrogen from water and natural gas. Gas Ind. 11(777), 94–102 (2018) 23. Efimenko, N.A., Uhalina, I.A.: Russia’s competitive advantages in the global spent nuclear fuel market. Global Nucl. Secur. 4(13), 96–98 (2014) 24. Fashiev, H.A.: The electric car market—the flywheel has spun. All-Russian Econ. J. ECO 2(548), 102–122 (2020) 25. Borisov, M.G.: Prospects for electrification of road transport in Asian countries. East. Anal. 3, 41–50 (2020) 26. Semikashev, V.V., Kolpakov, A.J., Jakovlev, A.A., Rostovskij, J.-K.: Development of the electric vehicle market in Russia as a prerequisite for benefiting from the global trend toward electrified transport. Forecast. Probl. 3(192), 52–63 (2022)

The Russian Federation is on Its Way to a Climate-Neutral Economy

111

27. Filimonov, S.V., Nikiforova, N.A.: Risk analysis of investment choice based on the blackscholes option pricing model (Black-Scholes option pricing model) on the example of the coal company ARCH COAL INC. Ugol 3, 49–53 (2020) 28. Tolmachev, M.N., Latkov, A.V., Mitrofanov, A.Y., Barashov, N.G.: Economic dynamics of Russia: approach based on the solow-swan model. In the Collection: Proceeding of the International Science and Technology Conference “FarEastSon 2020”, pp. 1063–1072. Singapore (2021)

Towards Scalable Consortium-Based Organic Agri-Food Systems Denis Galkin(B) Altai State Agricultural University, Barnaul, Russia [email protected]

Abstract. The study is devoted to the possibilities of progressive development of organic agri-food systems, overcoming their inertia and spatial heterogeneity. The aim of the work is to develop recommendations to improve the resilience of organic agri-food systems based on finding a balance of interests of small and large actors (agri-food producers). During the research, the author takes into account (1) the features of existing organic agri-food systems; (2) foreign and national experience of government support for organic agri-food systems; (3) the main beneficiaries of the progressive development of organic agri-food systems. The study is based on the synthesis of modern, traditional and mixed organic agri-food systems. This made it possible to propose a new type: hybrid organic agri-food systems. We show that cooperatives and associations can become the most acceptable forms of these hybrid systems at the initial stage of their formation. Adding an agro-industrial holding (integrator) to the system would allow the formation of consortiums, which is a mechanism for becoming hybrid organic agri-food systems scalable. Also, such cooperation is a fundamental for creating full-fledged export sectors based on small and medium-sized businesses. A necessary condition for the formation of organic consortiums is a favorable institutional environment, which can be created by government structures. The importance of the discussing public-private partnership is also emphasized in the distribution of functions for the development of hybrid organic agri-food systems. Keywords: Agri-food Systems · Consortium · Farmers · Government Support · Integration · Organic Products

1 Introduction It is well known that organic agri-food systems include the primary agricultural production of organic food and non-food products, their distribution, as well as household consumption [1]. The main participants of such systems are: agricultural organizations and farmers, resource providers, storage, processing and transportation organizations, wholesalers and retailers, and consumers. Resilient organic agri-food systems must have the capacity to adapt to a changing environment while providing (1) fair income distribution between participants and (2) minimal negative environmental impact. However, according to international experience, many organic agri-food systems are not viable [2, 3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 112–122, 2023. https://doi.org/10.1007/978-3-031-34329-2_12

Towards Scalable Consortium-Based Organic Agri-Food Systems

113

The principal resources and technologies for organic food production are known, but there is no proper understanding of (1) how to increase the resilience of organic agri-food systems in the conditions of separate national economies; (2) how to distribute management functions among the beneficiaries of these systems; and (3) what forms of management are acceptable for these systems. The main objective of the study is to develop recommendations for ensuring the viability of organic agri-food systems based on finding a balance of interaction between large agro-industrial holdings and small farmers. The hypothesis of the unification of small and large producers of organic food has already been reflected in the previous works of the author [4]. This hypothesis determines certain advantages of each business entity, as well as the fact that in Russia the resources for the production and sale of food are concentrated mainly in agro-industrial holdings. According to the author, the balance of interaction between economic entities requires additional research and acts as a reserve for increasing the resilience of organic agri-food systems by embedding rural areas represented by small farmers in international or domestic organic food value chains.

2 Materials and Methods The basis of the study is a systematic approach that allows us to present reality as the interaction of two elements: (1) the organic agri-food system under study, and (2) the external environment. In the paper, we primarily study the organic agri-food system. An important feature of such a system is that its purposeful as well as non-purposeful elements are integrated not only to obtain resources from the external environment, but also for entrepreneurial activity, which provides the system with additional income. The agri-food system is needed by the external environment as a source of satisfaction of its needs for producing final products. The absence of final products is a principal problematic situation that contributes to the emergence of an unsatisfactory state of the elements of the external environment. That is, the organic agri-food system is a means by which a solution to the problem could be found. Thus, we think that the conceptual framework for the development of such a system should be aimed at reducing the costs of production and sales of products, including transaction costs and maximizing benefits for all stakeholders. Taking into account the above, in Sect. 3.1 we reveal the concept of the organic agrifood system and characterize its types and introduce the concept of a hybrid organic agri-food system. Then, in Sect. 3.2, we analyze the experience of developing organic agri-food systems and determine the possibility of its extension to the national model for the development of organic agriculture. Previously, we found that organic agriculture in Russia is developed on the basis of Hegestrand’s theory, and for the emergence of more organic farmers, it is necessary to increase their resilience [2]. In Sect. 3.3, we justify acceptable forms of hybrid organic agri-food systems. In Sect. 3.4, we identify stakeholders in the development of hybrid organic agri-food systems. The stakeholder analysis was used to identify stakeholders and evaluate their benefits. It can be viewed as an approach to identifying the key parties involved in the functioning of the system and assessing their interests [5]. This method can be applied to the importance of the participation of each actor in the development of hybrid organic agri-food systems [6].

114

D. Galkin

3 Results and Discussions 3.1 The Concept and Types of Organic Agri-Food Systems The most common types of agri-food systems are: modern, traditional and mixed [7]. The transition to sustainable development contributed to an increase in the role of environmental principles, the development of state concepts of organic agriculture, and the determination of the volume and tools of government support for organic food producers. The government stimulating policy and the growing demand for organic products contributed to a gradual increase in the number of organic farmers as well as a change in the specialization of some agricultural organizations. Thus, in modern, traditional and mixed agri-food systems, organic subsystems of the same name have been formed. Next, we consider their features and manifestation in the economy. The modern organic agri-food system, in which resources for the production and sales of organic food are concentrated mainly in agro-industrial holdings. Based on classical works, we identify features of this system: (1) a predominantly vertical form of integration that combines production, processing and sales through large retail chains; (2) efficient production due to high labor productivity, economies of scale, accurate planning of cash flows, control of various types of risk, and possession of market power; (3) long supply chains [8]. Agro-industrial holdings in the production of organic food represent an alternative to small businesses and their communities. The highest concentration of production is observed in the organic sector of the USA and China [9, 10]. In the US market, Danone is the leader in the production of organic products, accounting for 4.2% of retail sales. In China, according to USDA FAS data, the main producers of organic milk are Yili Industrial Group (28% market share) and Chinga Mengniu Dairy Co (13% market share) [10]. A typical example of a system in the Russian economy is the agro-industrial holding EcoNiva-APK LLC. Since 2019, EcoNiva-APK Holding LLC has been implementing several projects in the field of organic agriculture. The holding includes the following participants in the organic products market: Savinskaya Niva LLC (Kaluga Region), Smolenskaya Niva Organic LLC (Smolensk Region), Severnaya Niva Organic LLC (Orenburg Region), Stupinskaya Niva Organic LLC (Moscow Region), Sibirskaya Niva Organic LLC (Novosibirsk Region) [2]. The main direction of detail of organizations is meat and dairy farming as well as crop production. The predominance of holdings in organic agriculture is in line with the general trends of large-scale commodity production in the country. Traditional organic agri-food systems are systems based on small-scale farming: personal subsidiary plots and farms. The system is characterized by (1) proximity of the producer and consumer; (2) orientation of the producer mainly to the local market; (3) short supply chains, (4) low level of use of environmental resources; (5) inherited forms of production [11]. Traditional organic agri-food systems are most common in Latin America and Asia [7]. Mixed organic agri-food systems which combine elements of modern and traditional systems. Mixed type of farmers use the resources of the external environment, and, in the same time, adhere to the traditional production technology. Processing of agricultural organic products and supply chains are not limited to local markets [11]. This type of

Towards Scalable Consortium-Based Organic Agri-Food Systems

115

organic agri-food systems includes European biodistricts, which in 2017 were recognized by Italian laws. These are the areas where farmers, processors, consumer associations and administration enter into formal agreements to promote organic agriculture and manage local resources. As of 2019, 34 biodistricts are registered in Italy. In biodistricts, there is cooperation between all local actors who consider organic agriculture as the basis of sustainable development [12]. In Europe, the increase in the number of biodistricts is driven by increased resilience of local farmers and increased consumer confidence in organic products. The development of the concept of biodistricts in Italy is aimed at (1) increasing the profitability of agriculture by creating opportunities for the sale of organic products; (2) improving the sustainability of agriculture by reducing the environmental impact of the industry; (3) stabilization of migration processes and creation of jobs; (4) improving the quality of local organic products [13]. Meanwhile, in Russia, according to the Union of Organic Farming, 260 certificates of compliance with national organic standards have already been issued [14]. Many organic producers and representatives of small businesses are located in the central part of Russia (Moscow Region, Yaroslavl Region, Republic of Mordovia, Saratov Region, Kaluga Region, Tver Region, Vologda Region, Smolensk Region and others) [2, 15]. It can be assumed that one of the factors for locating the production of organic products in Russia is proximity to the market for the sale of products. On the other hand, modern, traditional and mixed organic agri-food systems have certain disadvantages. In the modern type, there is no connection with rural areas, since agro-industrial holdings are not interested in their development, unlike small and medium-sized entrepreneurs. In the traditional and mixed types, on the contrary, there is a significant proportion of small farmers who are interested in the development of rural areas, but cannot compete with agro-industrial holdings in the national and global organic food markets. The limitations of each type create prerequisites for creating an alternative concept for the development of organic agri-food systems, taking into account the balance of interests and opportunities of large and small producers. The author’s concept of the development of organic agri-food systems requires consideration of the fourth type: the hybrid organic agri-food system. This type has already proved itself in the national economy on the example of the interaction of the agro-industrial holding TDS-Group LLC (Tomsk Region) with farmers and agricultural organizations wishing to start organic production. A feature of TDS-Group LLC is the provision of services for organizing the production of organic products. Such consulting services include (1) selection of biological products; (2) payment for certification; (3) selection of buyers of organic products; (4) harmonization of crops and prices [2]. However, this example is not widely used. To assess the reasons hindering the development of organic agri-food systems in the national economy, it is necessary to consider the global experience of their functioning and government support. 3.2 Models for the Development of Organic Agri-Food Systems According to Fibl (Forschungsinstitut für biologischen Landbau), the dynamic development of the organic food market is observed in the EU countries, the USA and China

116

D. Galkin

[16]. Let us consider the specific instruments of the state stimulating policy for the development of organic food markets in such countries. The EU Organic Agriculture Development Model is a model focused exclusively on the internal market. This is evidenced by the volume of financial support for organic farmers under the common agricultural policy (CAP) and the implemented the so-called “Farm to Fork Strategy”, which is considered the basis of the European Green Deal. It is also worth noting the CAP 2021 reform, according to which, spending on supporting small farmers and sustainable agricultural practices will increase from 2023 [17]. All these measures are aimed at promoting social inclusion, poverty reduction and progressive socio-economic development in rural areas, including the creation and preservation of jobs. The US Organic Farming Development Model is a model of a constant increase in demand for organic food. A significant part of the market is occupied by milk and dairy products, in the production of which the Danone company is the leader. Market dominance by large organizations is typical of the US and the organic food sector is no exception. Organic agriculture development programs are the main instrument of stimulating state policy. Within the framework of the main programs OCCSP, EQIP and CRP, an increase in the volume of financial support for organic farmers is expected [18]. The specifics of the sector are taken into account in the general programs for the development of the industry, including insurance and lending programs. China’s Organic Agriculture Development Model is more export-oriented [19]. This is evidenced by the main models for the production of organic products and the Chinese standard GB/T 19630-2019, which largely corresponds to the requirements of the Japanese JAS standard and the American NOP standard. However, the Chinese standard has not received any recognition in the world. This is due to consumer distrust of Chinese organic products. China competes with the main exporters of organic products (Argentina, Brazil, India). Principal exported products are cake, tea and soybeans, while the principal imported organic products are powdered and sterilized milk. Since 2020, organic agriculture in China has been considered as a basic element of rural development, similar to the EU model [20]. The spatial distribution of organic agriculture in Russia was substantiated by us earlier using the theory of spatial diffusion of Hagerstrand innovations [2]. We concluded that the principles and stages of the theory of spatial diffusion of innovations can be as well applied to organic agriculture in Russia. There was (1) convergence between the center and the periphery in terms of the number of producers; (2) the placement of acceptors of organic products in Moscow and Tomsk regions, as well as the concentration of followers around these regions. According to Hagerstrand’s theory, an increase in organic producers can be expected in the future. In Russia, the basic prerequisites for the development of organic production have already been created, namely, the minimum necessary legal framework and public initiatives. However, organic agriculture continues to develop inertially. This is due to the absence of the main driver for the development of the sector. To increase the intensity and spatial homogeneity of the development of organic agri-food systems in Russia,

Towards Scalable Consortium-Based Organic Agri-Food Systems

117

it becomes necessary to determine (1) the forms of interaction between participants in these systems and (2) the possibilities of government support. 3.3 Forms of the Hybrid Organic Agri-Food System Earlier, we focused on the fact that in Russia the resources for the production and sale of food are concentrated mainly in agro-industrial holdings. In our opinion, this fact cannot be ignored when forming the conceptual foundations for the development of organic agrifood systems. In turn, small and medium-sized organic food producers are struggling to gain a share in foreign and national markets due to lack of experience, constantly changing environmental conditions and high borrowing costs. Given the predominance of agro-industrial holdings in the industry and the presence of organic farmers, the search for a balance of their interaction is relevant [4]. The possibility of merging large and small entities is one of the alternatives for the progressive development of organic agri-food systems [21]. The merge is supposed to take into account not only the interests of subjects with market power, but also farmers. Forms of associations of organizations can be conditionally divided into “hard” and “soft”. We propose that the main condition of partnership should include the legal independence of the subjects, therefore, further attention is focused on “soft” forms of associations. The most common soft structures in food production are (1) cooperative; (2) association (union); and (3) consortium. It should also be taken into account that in order to strengthen their position, small forms of management can unite into communities (cooperatives, associations) without the participation of a large integrator (agro-industrial holding). Therefore, we advise to consider the association of (1) small farmers into cooperatives; (2) small and large organizations into associations and consortiums (Table 1). The development of an organic agri-food system is possible through three types of associations. In turn, cooperatives and associations (unions) are suitable for the initial association of small and medium-sized farmers. Such communities allow (1) strengthening negotiating positions; (2) defending interests; (3) exchanging experiences; (4) applying uniform standards; (5) producing significant amounts of organic food. The main function of small and medium-sized entities in the organic agri-food system is production. The emergence of consortiums is possible if there is a leading economic entity capable of managing the project. In the consortium, agro-industrial holdings can become integrators of small farmers, as they have: (1) significant resource potential and management experience; (2) the ability to process and market organic food. The consortium is not tied to a specific territory and implies a form of joint project implementation, as well as a mechanism for scaling organic agri-food systems and creating export sectors based on small and medium-sized businesses. The consortium is also based on the ideology of collective learning, which enables small entities to gain the necessary experience and, after the collapse of the consortium, continue their activities independently or within the framework of a cooperative. Thus, the main function of agro-industrial holdings in a hybrid organic agri-food system is managerial. We propose the following support measures for an organic agri-food consortium: (1) grants for research and development; (2) subsidies for the purchase of equipment;

118

D. Galkin Table 1. Characteristics of associations of organic food production entities.

Criterion

Production cooperative

Association (Union)

Consortium

Purpose of creation

Service delivery, revenue growth, cost reduction

Provision of services

Increasing competitiveness in selected markets or in selected projects

Association basis

Membership

Membership

Agreement

Members

Limited number of small farmers

Unlimited number of small, medium and large organizations

Limited number of small, medium and large organizations

Participant need

Organization of collective activity

Expression of collective interests

Coordination of collective activities

Association object

Effort and property

Common goals

Effort and property

Organizational structure

Organic structure

Absent

Hierarchical

Equality of participants in decision-making

Present

Present

Absent

The procedure for the distribution of the result of activity

In proportion to labor Equal accessibility participation

In proportion to property contribution or labor participation

Single development project

Not required

Mandatory

Not required

(3) subsidies for educational programs; (4) subsidies for exhibition activities. The participation of the government in the development of an organic food consortium can be expressed in (1) pursuing a stimulating policy aimed at bringing economic entities together; (2) protecting the interests of small forms of business in relation to the interests of agro-industrial holdings. Thus, the main function of the government as an external element of the hybrid organic agri-food system is stimulating. 3.4 Analysis of Stakeholder Interests in the Development of Hybrid Agri-Food Systems To identify the main beneficiaries in the development of hybrid organic agri-food systems, it is advisable to use the “stakeholder analysis” method. Previous research on who should pay for the benefits of operating organic agri-food systems shows that these systems create public and private goods [22]. Both primary and secondary beneficiaries benefit from these goods [23]. Thus, the importance of public-private partnership in the development of such systems is emphasized. The beneficiaries of the development of hybrid organic agri-food systems are: farmers, agro-industrial holdings, the government and consumers. In Table 2 we present the

Towards Scalable Consortium-Based Organic Agri-Food Systems

119

effects of operating hybrid organic agri-food systems. The combination of effects is beneficial for each element of the system and the external environment. Table 2 lists only the primary beneficiary of any effect of a hybrid organic agri-food system. However, this does not mean that the effect cannot have secondary beneficiaries. Table 2. Primary beneficiaries of the main effects of hybrid organic agri-food systems. Effect

Farmer

Increasing the income of owners

+

Possibility of the diversification

Agro-industrial holding

Consumer

Government

+

Preservation of natural capital

+

Reduction in unemployment in rural areas

+

Reducing the rate of migration from rural areas

+

Increasing consumer demand in rural areas

+

Health care Involvement in the turnover of unused lands

+ +

The benefit of the farmer, first of all, is manifested in an increase in income and the possibility of its capitalization, as well as an increase in the standard of living. The main motive for the production of organic products is the price factor. High price premiums provide high returns and offset the lower productivity of organic agriculture. Price premiums are a tool to protect against price volatility for agricultural products [24]. Also, high prices are explained by higher costs for the production of organic products [25]. However, this does not mean that the farmer will not benefit from maintaining the quality of agricultural land and the opportunity to attract more skilled labor due to reduced migration from rural areas. A clear effect for the agro-industrial holding is the possibility of the diversification. High price premiums for organic food can also ensure the growth of the holding’s income. Additional research is needed to assess the possibility of increasing the income of an agro-industrial holding. Consumers of the final organic food will receive a healthy product. When buying organic products, consumers expect a positive impact on health. Another reason to buy organic products may be their taste. Studies show that the health factor significantly affects the willingness of consumers to buy organic products [26]. In this way, consumers can view organic purchases as an investment in health. At the same time, the government

120

D. Galkin

does not have to ensure the availability of organic food; the organic food market can remain segmented. In this case, the hybrid organic agri-food system is an automatic redistribution of income from urban to rural residents. The government is the beneficiary of (1) preserving natural capital; (2) reducing rural unemployment; (3) reducing rural migration; and (4) reclaiming unused land. At the same time, the government can also act as a secondary beneficiary of a healthy nation and an increase in farmers’ incomes. We state that the government is the main recipient of the effects from the development of hybrid organic agri-food systems, and the experience of the US, EU, and China proves this. All this indicates the need for more active participation of the government in supporting hybrid organic agri-food systems. The scaling up of hybrid organic agri-food systems in the form of consortiums requires the use of incentive public policy tools. Creation of conditions for export may be associated with the withdrawal of organic food from the general export mechanism (grain damper mechanism). It also requires the protection of small farmers from agro-industrial holdings with market power. This can be achieved by increasing the transparency of information, for example, the terms of contracts between holdings and farmers can be made public.

4 Conclusions This research on agri-food systems focuses on (1) modern; (2) traditional; and (3) mixed species. Taking into account the world experience in each of the three types, we have identified organic subsystems of the same name. We found that the modern organic agrifood system is represented by agro-industrial holdings that have concentrated resources for the production and sale of organic food. Traditional organic agri-food systems are represented by small farmers and agricultural organizations that are focused on the local organic food market. Mixed organic agri-food systems adhere to traditional production technologies, using the resources of the external environment. Nevertheless, every organic agri-food system has limitations. The modern type is focused solely on the scale of production and minimization of costs, but such production does not create positive effects in rural areas. Traditional and mixed types, on the contrary, cannot compete with the modern type in the national and world organic food markets, while they have a greater positive effect on the areas of placement. We came to the result that eliminating the shortcomings and limitations of each type of organic agri-food systems is associated with the emergence of the fourth type: the hybrid organic agri-food system. According to our study for the Russian case of organic agriculture, there is a beginning of convergence between the center and the periphery in terms of the number of organic food producers, and according to Hagerstrand’s theory, an increase in organic producers can be expected in the future. We expect that the development of hybrid organic agri-food systems is associated with the need to find a balance of interests of participants in value chains, namely, a balance of interests of large and small producers. Such a balance of interests can be ensured in the formation of hybrid organic agri-food systems in the form of consortiums. The considered global experience in the development of organic agriculture emphasizes the dependence of the viability of hybrid organic agri-food systems on government

Towards Scalable Consortium-Based Organic Agri-Food Systems

121

policy. The importance of government support for organic consortiums is also due to the fact that the government is the main beneficiary of the effects of the functioning of hybrid organic agri-food systems. The choice of a policy of government non-intervention, according to the author, would lead to an inertial path for the development of organic agri-food systems.

References 1. FAO.: Short review. The State of Food and Agriculture 2021. Strengthening the resilience of agricultural and food systems in the face of shocks and stresses (2021) 2. Galkin, D.: Spatial diffusion of organic agriculture: theoretical model and russian experience. In: International Conference, Ecosystems Without Borders, pp. 89–98. Springer, Cham (2022) 3. Rahmann, G., Reza Ardakani, M., Barberi, P., Boehm, H., Canali, S., Chander, M.: Organic agriculture 3.0 is innovation with research. Organic Agric. 7, 169–197 (2017) 4. Galkin, D.: Towards the managed transition to organic agriculture: searching for a strategic model. In: Agriculture Digitalization and Organic Production: Proceedings of the Second. International Conference, ADOP, pp. 55–66. Springer Nature Singapore (2022) 5. Le, N.P., Nguyen, T.T.P., Zhu, D.: Understanding the stakeholders’ involvement in utilizing municipal solid waste in agriculture through composting: a case study of Hanoi, Vietnam. Sustainability 10(7), 2314 (2018) 6. Guareschi, M., Maccari, M., Sciurano, J.P., Arfini, F., Pronti, A.: A methodological approach to upscale toward an agroecology system in EU-LAFSs: the case of the Parma Bio-District. Sustainability 12(13), 5398 (2020) 7. Pengue, W.A., Gemmill Herren, B., Balázs, B., Ortega, E., Acevedo, F., Diaz, D.N., Westhoek, H.: Eco-agri-food systems: today’s realities and tomorrow’s challenges (2018) 8. Tweeten, L.G., Flora, C.B.: Vertical coordination of agriculture in farming-dependent areas. Council for Agricultural Science and Technology 137 (2001) 9. Korotkikh, A.A.: Organic agriculture in the USA. Russia and America in the XXI Century 4, 8 (2020) (in Russian) 10. Hanser, A., Li, J.C.: Opting out? Gated consumption, infant formula and China’s affluent urban consumers. China J. 74, 110–128 (2015) 11. Fresco, L.O., Ruben, R., Herens, M.: Challenges and perspectives for supporting sustainable and inclusive food systems. GREAT Insights Mag. 6, 13–15 (2017) 12. Dias, R.S., Costa, D.V., Correia, H.E., Costa, C.A.: Building bio-districts or eco-regions: participative processes supported by focal groups. Agriculture 11(6), 511 (2021) 13. Basile, S.: The experience of bio-districts in Italy. FAO. http://www.fao.org/agroecology/dat abase/detail/es/c/1073217. Last Accessed 23 July 2022 14. Union of Organic Farming. https://soz.bio/. Last Accessed 12 June 2022 (in Russian) 15. Gracheva, R.G., Sheludkov, A.V.: Diffusion of organic agriculture in Russia: features and implications for rural development. Reg. Res. Russ. 11, 578–588 (2021) 16. FiBL.: Data on Organic Agriculture in the World, Research Institute of Organic Agriculture. https://statistics.fibl.org/. Last Accessed 21 June 2022 17. Schebesta, H., Candel, J.J.: Game-changing potential of the EU’s farm to fork strategy. Nature Food 1(10), 586–588 (2020) 18. NRCS program results (2021). https://www.nrcs.usda.gov/Internet/NRCS_RCA/reports/ fb08_cp_eqip.html. Last Accessed 11 July 2022 19. Zhao, H., et al.: China’s future food demand and its implications for trade and environment. Nat. Sustain. 4(12), 1042–1051 (2021)

122

D. Galkin

20. Donkers, H.: A review of organic growth in China’s agricultural and food systems. Int. J. Organic Agric. Res. Dev. 17(1), 126–157 (2021) 21. Galkin, D., Pospelova, I.: Organic food production: search for territories and types of organizations. In: IOP Conference Series: Earth and Environmental Science, vol. 677, p. 022035 (2021) 22. Kvakkestad, V., Berglann, H., Refsgaard, K., Flaten, O.: Citizen and consumer evaluation of organic food and farming in Norway. Org. Agric. 8(2), 87–103 (2017). https://doi.org/10. 1007/s13165-017-0176-8 23. Padel, S., Zander, K., Lampkin, N.H., Hildert Sanders, J.: The consumer or the citizen: who should pay for the benefits of organic farming? (2021) 24. Crowder, D.W., Reganold, J.P.: Financial competitiveness of organic agriculture on a global scale. Proc. Natl. Acad. Sci. USA 112, 7611–7616 (2015) 25. Górska-Warsewicz, H., et al.: Factors limiting the development of the organic food sector— Perspective of processors, distributors, and retailers. Agriculture 11(9), 882 (2021) 26. Gundala, R.R., Singh, A.: What motivates consumers to buy organic foods? Results of an empirical study in the United States. PLoS ONE 16(9), e0257288 (2021)

Innovation Ecosystems

Analytical Review of the Formation of Key Performance Indicators of Development Institutions Ruslan Polyakov1(B)

and Elena Nikiforova2

1 Kaliningrad State Technical University, Kaliningrad, Russia

[email protected] 2 Financial University Under the Government of the Russian Federation, Moscow, Russia

Abstract. In recent years, the development and implementation of national projects is characteristic of Russian practice. An important role in achieving the goals of the national projects in recent years is played by development institutions, through which the regulation of stimulating innovation processes and infrastructure development of the country is carried out. In fact, development institutions are aimed at performing the functions of regulator and social control over the distribution of budgetary funds for significant projects of society. Due to the importance of such institutions for the effective development of the country’s economy, it is necessary to develop criteria for evaluating the activities of such institutions as key performance indicators of their activities. The Russian regulatory framework has tools to analyze and evaluate the performance of the management of development institutions, but given the current realities of the changing market conditions, it is necessary to clarify the key performance indicators of development institutions. Keywords: Development Institutions · KPI · Key Performance Indicators · National Development Goals

1 Introduction Modern conditions of development of the Russian economy are based on the implementation of national projects. An important role in the effective implementation of such projects is assigned to development institutions. A.A. Duyeva writes that “development institutions are one of the instruments of public policy, stimulating innovation processes and infrastructure development with the use of public-private partnership mechanisms” [1]. The activity of development institutions in the business space of the country contributes to overcoming “market failures”, providing the solution of state tasks, through the regulation of investments aimed at eliminating the problems of the market mechanism to ensure sustainable economic growth and di-versification of the state economy. For the Russian economy Presidential Decree No. 474 of July 21, 2020 “On the national development goals of the Russian Federation for the period up to 2030” [2] (hereinafter—Decree No. 474) defines five national development goals of the country (see Fig. 1). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 125–132, 2023. https://doi.org/10.1007/978-3-031-34329-2_13

126

R. Polyakov and E. Nikiforova

Fig. 1. Key National Development Goals of the Russian Federation (compiled by the authors).

The regulation of investments, directed by the state to the economic entities of the real sector of the economy, requires in modern conditions the most rational approach and its most effective realization is possible with the involvement of development institutes, specially created for this purpose. In its turn, for effective use of budgetary funds by development institutions it is necessary to work out key indicators of assessment of their activity efficiency. The list of key performance indicators of development institutions, the procedure for their calculation and the procedure for setting target values are contained in the Russian Government Decree No. 3579-r dated December 28, 2020 “On Approval of Methodological Recommendations on the Formation and Application of Key Performance Indicators of Joint Stock Companies whose Shares are Owned by the Russian Federation and Individual Non-profit Organizations for Determining the Remuneration of Their Management Staff” [3]. Analysis of this document showed that it presents two groups of key performance indicators (hereinafter KPIs): – financial and economic; – industry-specific. Financial and economic key performance indicators of joint stock companies are formed by indicators of profitability (eg, ROIC or ROE), indicators of dividend yield, debt load, as well as the share of operating costs in the assets of the joint stock company. Industry key performance indicators of joint stock companies are formed by indicators aimed at achieving national goals, and specialized indicators, the list of which is established by their management. The analysis and evaluation of key KPIs will make it possible to more effectively monitor the work of development institutions, as well as to adjust the remuneration of top managers depending on the achievement of given KPIs.

2 Materials and Methods The achievement of the key national development goals of the Russian Federation is ensured by the sustainable development of the priority sectors of the economy. Thus, before proceeding to the consideration of KPIs, let us examine the main socio-economic

Analytical Review of the Formation of Key Performance Indicators

127

indicators achieved in the priority sectors of the economy over the past decade (see Fig. 2) [4]. The data in Fig. 2 show that “the structure of gross output—the total value of all goods and services produced in the economy during the reporting year remained virtually unchanged. Thus, the main share in the output (about 25%) is steadily occupied by manufacturing industries, and the minimum contribution to the final indicator (on average about 1.5%) belongs to the activities in the field of tourism industry. Significant reduction in the total gross output was shown by the industry associated with the production and distribution of electricity, gas and water—the reduction of the share in total gross output for the period from 2011 to 2020 was 0.6%, as well as construction—during the same period the share of this type of activity decreased from 7.7 to 6.7% (with the lowest value for the period under study being 6.4%) [5].

Fig. 2. Dynamics of the share of priority sectors of the Russian economy in gross output, in % of the total (compiled by the authors on the basis of [4])

The national goal of digital transformation is determined by the significant growth rate of the share (from 2.4 to 2.8% in the last five years) in the gross output of priority industries. The task set by the Government of the Russian Federation to ensure accelerated implementation of digital technologies in the economy and social sphere for the period up to 2030 determines further increase in the share of this industry in the total structure of the output of priority sectors of the Russian economy. It is also necessary to note the positive dynamics observed in the activities of health care and education, showing an increase in the share by 0.3% (from 2.8 to 3.1% for health care and from 1.9 to 2.2% for education). The dynamics of the share of the considered industries in the gross added value of the country is reflected in Fig. 3 [4].

128

R. Polyakov and E. Nikiforova

Fig. 3. Dynamics of the Share of Priority Sectors of the Russian Economy in Gross Value Added, as a % of the Total (compiled by the authors on the basis of [4])

The structure of gross value added shows that “the main share still belongs to manufacturing production. The share of manufacturing production was 14.8% in 2020, having increased by 1.5 p.p. over the last 10 years from 13.3%. In addition to the above-mentioned manufacturing industry, the socio-economic spheres—education and health care—showed positive dynamics ±0.7% and +0.5% in the structure of gross value added respectively for the period from 2011 to 2020 [5]. In addition to gross value added, social indicators, such as: the number of employees by industry and type of activity, the size of the average nominal wage, typical for the industry; the dynamics of the average monthly nominal accrued wage, the share of the average monthly nominal accrued wage by industry in the average monthly nominal accrued wage, the level of investment in the economy of Russia in the modern business space become important indicators of various industries in terms of ensuring sustainable development of the Russian economy. We believe that a number of the above-mentioned indicators should be reflected in the system of KPIs, which characterize the efficiency of development institutions. We will not dwell on the statistical parameters of these indicators in this article, as we have previously considered them in detail in the article by E.V. Nikiforova, V.I. Barilenko, S.V. Muzalev “Analytical substantiation of support for the development of priority sectors of the Russian economy”. As a result, the authors “formed an aggregate indicator of socio-economic performance for each of the studied industries as the total value of economic, social and legal factors” (Fig. 4) [5]. “The final analysis revealed that the leading positions in the aggregate indicator belonged to information and communication and health care activities” [5–10]. It is connected with cardinal influence rendered on these branches by coronovirus infection

Analytical Review of the Formation of Key Performance Indicators

129

Fig. 4. Aggregate indicator of socio-economic efficiency of priority industries (compiled by the authors)

which has swept the whole world in 2020, having caused prompt development of information and public health services. Informatization was also developed due to the fact that a restriction was imposed on the movement of the population, which caused the transfer of the personnel of economic entities to remote work mode.

3 Results Considering the above, we propose to present the key indicators of analysis and evaluation of the effectiveness of development institutions as follows. A. Economic Indicators: Economic performance: added value created and distributed; taxes paid to budgets at all levels; risks and opportunities for development institutions’ activities due to changing climate conditions; profitability and business performance; financial assistance received from public authorities. Market presence: significant regions of operation; market growth rate; relative market share; income level of the population; elasticity of demand. B. Environmental indicators: “used materials with an indication of their mass and volume, the proportion of materials that are recycled or reused waste” [11]; “total mass and generation of solid waste; responsible use of resources, control of pollution emissions into the atmosphere; implementation of innovative technologies to reduce environmental risks” [12–14]; volumes of consumed water and sources of consumed water, influencing water intake of the organization; energy consumption; area of disturbed land during the year; payment for the negative impact on the environment.

130

R. Polyakov and E. Nikiforova

B. Social indicators: “labour practices and decent work (employment, employeemanagement relations, safety in the workplace, etc.)” [15]; human rights (investments, human rights grievance mechanisms, assessment of suppliers’ compliance with human rights, etc.) [15]; human rights (investments, complaints mechanisms for human rights violations, assessment of suppliers’ compliance with human rights, etc.); the average number of employees; indicators of the age and gender structure of employees; length of employment with the business entity; the size of an economic entity’s average nominal wages; the size of average nominal wages specific to the industry; the number of socially oriented activities; availability of education (by education levels and job hierarchy); work with indigenous people in the territories state of working conditions (coefficient); staff turnover (separately—staff turnover by wage levels) satisfaction with working conditions; availability of equipped workplaces (personal computers; software; multifunctional devices).

4 Discussion The analysis of the state of development of priority sectors of the Russian economy has shown that the current and rather complicated conditions, both economic and political, indicate that state support through development institutions for priority sectors of the economy in terms of information technology, healthcare, education, agriculture, construction, tourism, etc. is necessary, which in turn will ensure transparency of development institutions and strengthen the position of the Russian Federation in the global space through sustainable Given the importance of Russian development institutions for the state to achieve key national goals, the analysis and evaluation of the performance of such institutions should be based on a systematic and comprehensive approach, ensuring state interests by using a system of indicators of development institution performance, consisting of the need to be based on the concept of sustainable development [16]. Given the importance of Russian development institutions for the state to achieve key national goals, the analysis and evaluation of the effectiveness of such institutions should be based on a systematic and comprehensive approach, ensuring the public interest through the use of a system of indicators of the effectiveness of development institutions, consisting in the need to be based on the concept of sustainable development. We can highlight the main performance indicators that are of particular importance for analyzing and evaluating the effectiveness of development institutions, such as: taxes paid to the budgets of all levels, the creation of new jobs, investment volumes, value added, etc. [17–20]. We believe that in order to analyze the effectiveness of development institutions, it is advisable to divide their performance indicators into the following groups: economic; environmental; social. The classification of indicators into the above three groups will make it possible to assess the implementation of the concept of sustainable development by development institutions and their focus on improving the efficiency of their activities without harming society and the environment [21, 22].

Analytical Review of the Formation of Key Performance Indicators

131

5 Conclusion Of the variety of indicators given above (the list is approximate, not exhaustive), each development institution must choose the most relevant key performance indicators for it, based on the concept of sustainable development [23–27].

References 1. Dueva, A.A.: Legal status of participants in innovation activities. (Dissertation for the degree of Candidate of Law). Moscow State Law University named after O.E. Kutafin. Moscow, Russia: Moscow State Law Academy (2014). https://dlib.rsl.ru/01007893199. Accessed 30 Mar 2023 2. Presidential Decree No. 474 of 21 July 2020 ‘On the National Development Goals of the Russian Federation for the period until 2030’. (n.d.). Rossiyskaya Gazeta. https://rg.ru/doc uments/2020/07/22/ukaz-dok.html. Accessed 30 Mar 2023 3. Decree of the Government of the Russian Federation No. 3579-r of 28 December 2020 On Methodological Recommendations on the Formation and Application of Key Performance Indicators of Joint-Stock Companies Whose Shares are Owned by the Russian Federation and Certain Non-Profit Organisations for the Purpose of Determining Remuneration of Their Management Staff. (n.d.). http://www.garant.ru/products/ipo/prime/doc/400047342/. Accessed 30 Mar 2023 4. Federal State Statistics Service: (n.d.). https://rosstat.gov.ru/. Accessed 30 Mar 2023 5. Nikiforova, E.V., Barilenko, V.I., Muzalev, S.V.: Analytical rationale for supporting the development of priority sectors of the Russian economy. RISK: Resourc. Inf. Supply Compet. 2, 27–37 (2022) 6. Lipinsky, D.A., Berdnikova, L.F., Schnaider, O.V.: Specific features of training of law makers with the help of remote technologies. In: Digital Economy: Complexity and Variety vs. Rationality 9, pp. 606–611. Springer International Publishing (2020) 7. Petrov, A.M., Nikiforova, E.V., Kiseleva, N.P., Grishkina, S.N., Lihtarova, O.V.: Creation of the reporting on sustainable development of companies based on socioeconomic measurement statistics. Int. J. Recent Technol. Eng. 8(2), 4005–4012 (2019) 8. Efimova, O.V., Nikiforova, E.V., Basova, M.M., Shnaider, O.V., Ushanov, I.G.: Practice of non-financial reporting disclosure by russian companies: bridging the gap between company disclosures on sustainability and stakeholders’ needs. In: Proceedings of the 5th International Conference on Engineering and MIS, pp. 1–5 (2019) 9. Bulyga, R.P., Nikiforova, E.V., Safonova, I.V.: Indicators of the universities control activities. Int. J. Innov. Technol. Explor. Eng. 8(9), 1409–1415 (2019) 10. Muzalev, S.V., Reshetov, K.Y.: Food security of Russia: problems and perspectives of sustainable development. Complex Syst. Innov. Sustain. Dig. Age 1, 495–502 (2020) 11. Shamaeva, E.F., Musina, N.M.: History and development of sustainability reporting. Sustain. Innov. Dev. Des. Manage. 14(4), 87–100 (2018) 12. Lvova, N.A.: Responsible investments: theory, practice, prospects for the Russian Federation. Scientific Journal of NRU ITMO. Econ. Environ. Manage. Ser. 3, 56–67 (2019) 13. Tolmachev, M.N., Latkov, A.V., Mitrofanov, A.Y., Barashov, N.G.: Economic dynamics of Russia: approach based on the Solow-swan model. In: Proceeding of the International Science and Technology Conference” FarEastCon 2020” October 2020, Vladivostok, Russian Federation, Far Eastern Federal University, pp. 1063–1072. Singapore: Springer Nature Singapore (2021)

132

R. Polyakov and E. Nikiforova

14. Tolmachev, M., Tsypin, A., Barashov, N.: Statistical study of dynamics of the agricultural production of post-soviet countries in the context of food security. In: Proceeding of the International Science and Technology Conference” FarEastCon 2019” October 2019, Vladivostok, Russian Federation, Far Eastern Federal University, pp. 699–711. Springer, Singapore (2020) 15. Baigildin, D.R.: Improving the monitoring of sustainable development of the oil and gas sector of the Russian Federation. (Dissertation for the degree of Candidate of Economic Sciences). Kazan National Research Technological University. Kazan, Russia: Kazan National Research Technological University (2020). https://www.swsu.ru. Accessed 30 Mar 2023 16. Dovlatova, G., Smakhtina, A., Bondarenko, O., Yakunina, I., Tishechenko, I., Agafonov, A.: International monitoring of institutions in overcoming the resource dependence of the Russian sector of the transport economy. Transp. Res. Proc. 63, 502–508 (2022) 17. Fields, G.S., Wan, H., Jr.: Wage-setting institutions and economic growth. World Dev. 17(9), 1471–1483 (1989) 18. Bulte, E.H., Damania, R., Deacon, R.T.: Resource intensity, institutions, and development. World Dev. 33(7), 1029–1044 (2005) 19. Di Tommaso, M.R., Tassinari, M., Barbieri, E., Marozzi, M.: Selective industrial policy and ‘sustainable’structural change. discussing the political economy of sectoral priorities in the US. Struct. Chang. Econ. Dyn. 54, 309–323 (2020) 20. Azam, M., Ftiti, Z., Hunjra, A.I., Louhichi, W., Verhoeven, P.: Do market-supporting institutions promote sustainable development? Evidence from developing economies. Econ. Model. 116, 106023 (2022) 21. Van Arkadie, B.: The role of institutions in development. World Bank Econ. Rev. 3(suppl_1), 153–176 (1989) 22. Nikiforova, E., Polyakov, R.: Analysis of the supporting tools of population entrepreneurial initiatives. In: AIP Conference Proceedings, Vol. 2636, No. 1, p. 040013. AIP Publishing LLC (2022) 23. Jütting, J.: Institutions and development: a critical review. OECD Development Centre Working Papers, No. 210, OECD Publishing, Paris (2003). https://doi.org/10.1787/341346 131416 24. Shirley, M.M.: Institutions and Development. Edward Elgar Publishing, In Institutions and Development (2008) 25. Khan, M.H.: Institutions and development. In: Nayyar, D. (Ed.) Asian Transformations: An Inquiry into the Development of Nations. Oxford (2019). https://doi.org/10.1093/oso/978019 8844938.003.0013. Accessed 5 Apr 2023 26. Jütting, J.: Institutions and Development: A Critical Review (2003) 27. Portes, A.: Institutions and development: a conceptual reanalysis. Popul. Dev. Rev. 32(2), 233–262 (2006)

Shaping Digital Ecosystem of the Eurasian Economic Union: Issues and Resolutions Sergey Kamolov and Sofia Glazyeva(B) Department of Asset Management, MGIMO University, Moscow, Russia [email protected], [email protected] Abstract. The article explores the conceptual framework of the digital agenda of the Eurasian Economic Union and the actions of the Eurasian Economic Commission to implement the goals and objectives in the field of digital transformation of the economy. The Eurasian Economic Union is the third integration association to include the digital dimension in the integration field of interaction. With formed foundations of the regulatory and legal framework that allows for pursuing joint digital initiatives, the implementation of the digital agenda is significantly delayed. Key digital projects are considered in detail, data on their effects, status and barriers that stand in the way of their implementation are systematized. The authors analyzed the key constraints that hinder the development of the digital infrastructure and digital ecosystem of the Union. Recommendations are developed to further converge the policies of the Member States in the field of digital transformation of the economy and overcome existing obstacles. Keywords: EAEU digital agenda · Eurasian integration · Digital economy

1 Introduction The digital transformation of the economy has been a global trend for many years, which most of both developed and developing countries are striving for. The desire to switch to digital rails is also outlined in the strategies of many integration associations. Thus, the European Union and the Association of Southeast Asian Nations have been implementing a consistent policy in the field of building digital ecosystems within the framework of integration entities for several years [1, 2]. The Eurasian Economic Union (EAEU) is also no exception to this trend. In 2017, the Eurasian Economic Union adopted the digital agenda, the purpose of which was the joint development of the digital economies of the Member States. The digital theme was not initially considered as a direction of cooperation between countries; therefore, it was not fixed in the Treaty on the Eurasian Economic Union, but gradually a common understanding began to emerge among the Member States that the digital transformation of the economy is one of the most important sources of growth and competitiveness of national economies [3, 4]. The tasks of the Eurasian digital agenda can be divided into two key dimensions—the modernization of the mechanisms and processes of integration interaction within the Union and the growth of the economies of the Member States by strengthening cooperation in the field of promising areas of the digital economy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 133–139, 2023. https://doi.org/10.1007/978-3-031-34329-2_14

134

S. Kamolov and S. Glazyeva

2 EAEU Digital Agenda: Digital Infrastructure and Digital Initiatives The active stage in the formation of the EAEU digital agenda began in 2016, which made it possible in 2017 to approve the Main Directions for the Implementation of the EAEU Digital Agenda until 2025. This fundamental document focuses on digital transformation of economic sectors and cross-sector transformation in the EAEU, digital transformation of markets for goods and services, financial markets and labor market, digital transformation of EAEU integration process, development of the EAEU’s digital infrastructure and ensuring the security of digital processes [5]. Activity in these areas is carried out in three stages: Modeling of digital transformation processes, elaboration of the first initiatives and launch of flagship projects (flagship projects are defined in the Appendix to the Main Directions for the Implementation of the EAEU Digital Agenda)—until 2019. Formation of digital economy institutions and digital assets as well as development of digital ecosystems—until 2022. Implementation of digital ecosystems and digital cooperation projects on global, regional, national and sectoral scales—until 2025. At the moment, the EAEU is at the second stage of the digital agenda implementation. Key projects have already been launched or are in the process of being developed. The activities of the EEC in the field of digitalization are usually divided into two fields: the integrated information system (IIS) and the digital agenda. The integrated information system is a key element of the EAEU digital infrastructure. Its tasks are to ensure information interaction between the state authorities of the Member States, information support for the work of the EAEU bodies and simplify the process of exchanging data and electronic documents. IIS is one of the very first projects related to digitalization and automation of processes within the EAEU. The decision to create an information system was made in 2009. During this time, many of the technical solutions incorporated in the IIS have become obsolete. In the fall of 2020, it was decided to conduct an expert examination of the system and further modernize it, but since then no visible progress has been made. As is known, the key foundation of the EAEU is the principle of four freedoms, including free movement of goods and labor. To facilitate these processes, three projects have been developed: the unified search system “Work Without Borders”, the ecosystem of digital transport corridors in the EAEU, the Eurasian network of industrial cooperation, subcontracting and technology transfer, and digital technical regulation within the EAEU. The only completed project on the EAEU digital agenda is the Work Without Borders initiative. Work without Borders is a search engine for information about applicants and vacancies posted by candidates and employers from the Member States of the Union. It is expected that the system will not only significantly expand the opportunities for both employers and the working population of the participating countries but will also become a source of information on labor migration within the EAEU. The system is based on the existing national platforms of the Member States of the Union. The project

Shaping Digital Ecosystem of the Eurasian Economic Union

135

was launched at the initiative of the Russian Federal Service for Labor and Employment in 2019 and is successfully operating today. According to official data, in 2022 this international system already includes more than 2 million resumes and 500 thousand vacancies in the territory of the Union [6]. The ecosystem of digital transport corridors in the EAEU is a promising project aimed at creating an open environment for the exchange of data and information in the field of logistics, in particular, cargo transportation, mainly carried out by road transport—about 70% of the total volume in the Union [7]. The objective of the project is to simplify and automate logistics operations, switch to electronic document management and data exchange, electronic surveillance system based on data collection and analysis. It is expected that the introduction of electronic services will significantly facilitate the work of both the business in the field of cargo transportation and the authorized state bodies of the participating countries. Among the services and elements of the ecosystem that are planned to be implemented are a digital map and a digital database of main roads and infrastructure facilities of the ITC (international transport corridors), services for booking roadside infrastructure facilities and queues at checkpoints, services for providing medical services for drivers, electronic services related to the use of an international consignment note and a waybill and the execution of weight and size control. In 2021, an initiative was approved to develop an information and communication “showcase” of national services of the ecosystem of digital transport corridors, the implementation of which should initiate the formation of the ecosystem. The main activities for the implementation of the initiative were planned for 2022. Another important project on the digital agenda of the EAEU is the Eurasian network of industrial cooperation, subcontracting and technology transfer. The project provides for the creation of an electronic information system for forming partnerships. The objective of the system is to help strengthen industrial ties within the EAEU, create favorable conditions for the involvement of small and medium-sized businesses in the Eurasian production chains and to stimulate innovation processes in the economies of the participating countries. Thus, the national manufacturers of the EAEU states will be able to find promising partners and customers on the platform. The project implementation was planned for 2019–2020, but was extended until the end of 2022. Finally, the Union is implementing an initiative to develop digital technical regulation. The project is aimed at digitalization of the processes of formation of mandatory requirements for products, development of technical regulations and lists of international and regional standards necessary for the application of technical regulation. It is expected that the project will significantly facilitate the interaction of consumers of digital services being developed, in particular the business community, developers of technical regulations and standards, certification bodies, testing laboratories, government agencies and the Eurasian Economic Commission (EEC). The project implementation is planned for 2021–2024. More than 10 thousand documents will be digitized as a result of the initiative [5]. The system of digital traceability of goods should become one of the most important components of the EAEU digital ecosystem. The system is defined in the Main Directions for the Implementation of the EAEU until 2025 as one of the priority areas of the EAEU digital agenda. The mechanism that opens electronic access to complete information

136

S. Kamolov and S. Glazyeva

about the EAEU goods (place and conditions of production, the route of goods) is aimed at solving several problems at once—reducing business operating costs, more efficient control over the payment of taxes, combating illegal circulation of goods, protecting interests and increasing convenience for consumers. It is assumed that work will be carried out in such areas as documentary traceability and digital marking. It is also expected that the system will be implemented on the basis of IIS. The project should be implemented by 2025, but its prospects are still unclear. The development of the project is highly fragmented by different groups of goods. In 2022, a pilot project was launched to create mechanisms for digital traceability of goods in relation to household refrigerators and freezers. To ensure transparent and efficient circulation of goods imported into the territory of the Union, it is necessary to extend the mechanism of digital traceability of goods to all categories of products sold both on the internal market of the EAEU and in foreign trade.

3 The Eurasian Economic Union Digital Ecosystem: Issues and Resolutions Since 2018, 93 initiatives have been worked out, only 15 were supported, but some of them were later either not approved by higher bodies (the Eurasian Economic Commission Council, the Eurasian Intergovernmental Council), or temporarily postponed. Three projects have been approved and are being implemented. The dynamics of the submission of initiatives did not live up to expectations. In 2020–2021 there was a significant decrease in the number of incoming initiatives, which is largely due to the delay in the EAEU digital agenda implementation in the early stages. In addition, an important role was played by the increased attention of the Member States to the independent development of the digital economy at the national level. As a result, many potential applicants were involved in digitalization processes at the level of the EAEU Member States, and regional initiatives were postponed. A significant obstacle to the digital agenda implementation is the unresolved issue of data circulation regulation within the Union. Many digital ecosystems planned for implementation involve cross-border data exchange in various interaction formats—g2g, g2c, g2b, b2b, b2c. However, today in the Union, many aspects of data circulation remain undeveloped. Thus, in the EAEU there is no terminological consistency of key concepts in this area; regulation in the field of data is poorly developed and common approaches to the legal categorization of data, approaches to risk management in this area have not been worked out. Legal relations arising from cross-border data exchange remain unsettled. As a result, regulatory measures lag far behind practical ones, which hinders the development of the digital agenda. The situation is complicated by the requirements fixed in the national legislations of the EAEU Member States, in particular, the requirements for the localization of personal data. It is necessary to develop and legislate a regime for cross-border data circulation in the EAEU, including both non-personal data and personal data, as well as an adequate mechanism for data protection [8]. The Union has been discussing the issue of developing an international agreement on data circulation and data protection for quite a long time, but the process of harmonizing approaches and developing an agreement remains difficult and lengthy.

Shaping Digital Ecosystem of the Eurasian Economic Union

137

There are also barriers in the field of electronic document management. Thus, it is necessary to improve legislation and develop common approaches in the field of electronic signatures. The problem of mutual recognition of electronic signatures is one of the key obstacles in mutual trade, which significantly complicates interaction with suppliers on the EAEU internal market and the procurement process. Effective use of the Union’s digital infrastructure appears impossible without eliminating legal gaps. A more complex challenge to the implementation of the digital agenda is the problem of uneven digital development of the Union Member States [9]. To illustrate this statement, it is proposed to consider the performance of the Member States in the Networked Readiness Index [10]. The Network Readiness Index was developed by the World Economic Forum in 2002 and then handed over to the Portulans Institute. The Index illustrates the level of development of information and communication technologies (ICT) in countries. The Index is an important indicator of technological and innovative potential of a country, as well as an effective tool for conducting a comparative analysis of the level of ICT development in states (Fig. 1).

Fig. 1. EAEU Member States in the Network Readiness Index 2020–2022

The above data allows to make a conclusion that when it comes to ICT, the EAEU space is developing unevenly. For instance, the gap between Russia and Kyrgyzstan was 45 points in 2022. Armenia, Belarus and Kazakhstan are at similar levels of ICT development, but their gap with Russia is also significant. Now this problem is not so noticeable: so far, all efforts are aimed at increasing the connectivity of state bodies of the EAEU Member States, modernizing the integrated information system, and implementing uninterrupted and secure electronic document management. However, in the future, when the digital initiatives of the Union will directly affect the interests of the population, the digital divide can significantly impact the efficiency of projects implementation. Moreover, currently digital initiatives are based on existing national services, and their different level of development [11] complicates the implementation of joint projects. To accelerate the digital transformation of the Member States, it is necessary

138

S. Kamolov and S. Glazyeva

to intensify the international exchange of experience in the field of digital technologies and the scaling of best technological practices. Finally, the EAEU Member States have already developed and are implementing national digital economy strategies [12]. However, the strategies do not reflect the EAEU digital agenda and do not include the commitment to joint digital transformation. The EAEU is mentioned in the digital economy strategies of only two states—the Russian Federation and the Kyrgyz Republic. Moreover, there is no conceptual and terminological consistency in the Member States strategies: the states lay significantly different meanings in the concept of digital economy [13]. Thus, as recommendations for improving the processes on the EAEU digital agenda, the following are proposed: 1. Improvement of the regulatory and legal framework. Eliminating legal gaps in the field of data circulation, electronic signatures and other critical aspects of common digital space will significantly accelerate the implementation of initiatives. 2. Creation of a platform for international exchange of experience. Giving the digital direction of the Union an additional role as a platform for the exchange of experience and best practices will allow scaling up the most efficient technologies used by the Member States. 3. Harmonization of the national strategies of the Member States in the field of the digital economy and alignment with the EAEU digital agenda as a fundamental factor in the ecosystem development of the Union’s common digital space. The implementation of national digital transformation strategies in accordance with the supranational agenda will allow the Member States to increase their overall competitiveness in the global digital market [13].

4 Conclusion To date, the digital ecosystem of the Eurasian Economic Union has been conceptualized: goals, objectives, mechanisms for the submission and implementation of digital initiatives have been formulated and regulated. Key projects of the digital agenda developed considering the principles and priorities of the Union are being implemented. Meanwhile, the pace of the implementation of the digital agenda tasks is significantly behind the planned one. Thus, the critically important internal digital infrastructure of the Union has not yet been built: the integrated information system does not function on a full scale. The implementation of most key projects on the digital agenda is also being delayed. The most significant obstacles standing in the way of the development of the digital ecosystem of the Union are gaps in the legal framework, the lack of conceptual consistency in the implementation of national strategies for the digital economy, and the uneven development of ICT in the region. Over the past years, the EAEU Member States have been actively building digital ecosystems at the national levels, both in the field of public administration and in the development of the digital economy. At the same time, the digital agenda of the EAEU did not match the pace of national digital ecosystems development. The delay in the first

Shaping Digital Ecosystem of the Eurasian Economic Union

139

stages has made it more difficult to agree on common approaches and strategies and has led to a reduction in the number of digital initiatives submitted. Efficient implementation of the goals and objectives of the digital agenda requires the consolidation of the efforts of the Union Member States in the field of digital transformation of the economy with more active involvement of national competence centers and national digital infrastructures.

References 1. Kamolov, S.G., Glazyeva, S.S.: Smart cities on the agenda of integration associations. J. Law Admin. 16(2), 98–105 (2020). https://doi.org/10.24833/2073-8420-2020-2-55-98-105 2. Costa, C., Murphy, M.: EU Digital Media Policies and Education: The challenge of a digital agenda for Europe. In: St. John, S.K., Murphy, M. (eds.) Education and Public Policy in the European Union, pp. 149–164. Springer, Cham (2019). https://doi.org/10.1007/978-3-03004230-1_7 3. Glazyev, S.Yu.: On the strategic directions of the EEU development. Euras. Integr. Econ. Law Polit. 1, 11–30 (2020). https://doi.org/10.22394/2073-2929-2020-1-11-30 4. Popova, I.: The challenges of implementing the EAEU’s digital agenda. Int. Organ. Res. J. 16(1), 127–144 (2020). https://doi.org/10.17323/1996-7845-2021-01-06 5. The EAEU Digital Agenda Homepage. http://digital.eaeunion.org/extranet/. Accessed 01 Dec 2022 6. Work Without Borders Homepage. https://trudvsem.ru/rbg/. Accessed 01 Dec 2022 7. Eurasian Economic Union in Figures: Brief Statistical Compendium: Eurasian Economic Commission, 189 p. (2022) 8. Mikhaliova, T.N.: Upgrading Legal regulation of integration in the context of digital economy: the Eurasian economic union agenda. In: Inshakova, A.O., Frolova, E.E. (eds.) Smart Technologies for the Digitisation of Industry: Entrepreneurial Environment. SIST, vol. 254, pp. 213–226. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4621-8_18 9. Filatova, O., Golubev, V., Stetsko, E.: Digital transformation in the Eurasian economic union: prospects and challenges. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O. (eds.) DTGS 2018. CCIS, vol. 858, pp. 90–101. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02843-5_8 10. Network Readiness Index Homepage. https://networkreadinessindex.org. Accessed 01 Dec 2022 11. Bolgov, R., Karachay, V.: E-participation projects development in the e-governance institutional structure of the Eurasian economic union’s countries: comparative overview. In: Chugunov, A.V., Bolgov, R., Kabanov, Y., Kampis, G., Wimmer, M. (eds.) DTGS 2016. CCIS, vol. 674, pp. 205–218. Springer, Cham (2016). https://doi.org/10.1007/978-3-31949700-6_20 12. Kamolov, S.G., Glazyeva, S.S., Tazhiyeva, S.K.: Smart cities in Eurasian economic union: outlook for Russian regional technological leadership. Russ. Econ. J. 5, 64–82 (2022). https:// doi.org/10.33983/0130-9757-2022-5-64-82 13. Platonova, I.N., Maksakova, M.A.: The problems of digitalisation in the Eurasian economic union. In: Strategies and Trends in Organizational and Project Management (DITEM 2021) Lecture Notes in Networks and Systems, vol. 380, pp. 312–317. Springer (2022). https://doi. org/10.1007/978-3-030-94245-8_43

A Compositional Approach to Labor Potential Evaluation and a Neural Network Model for Its Forecasting Oksana Ogiy1(B)

and Vasiliy Osipov2

1 Kaliningrad State Technical University, Kaliningrad, Russian Federation

[email protected] 2 St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg,

Russian Federation

Abstract. The paper presents a multidimensional model of labor potential. The basis of the model comprises the parameters of the actor’s properties and working conditions that are significant for the labor functions performance. It is indicative that such a multi-parameter model makes it possible to evaluate the potential of various actors with high efficiency: an individual employee, a team, an organization, a group of companies, an industry. Authors focus on labor potential managing process structure of an actor and a set of supporting neural network methods and algorithms. In order to formalize the current state of the labor potential, age grouping was used. The distribution of actors according to labor functions (positions) is provided for each of these groups, to which they most correspond or can potentially correspond. Paper defines a new space of labor potential states and links between them. Also the paper clarifies actor’s properties monitoring particulars and working conditions, as well as observed data encoding rules for processing them in impulse recurrent neural networks. Authors disclose a neural network model for predicting labor potential and implementing algorithms. The novelty of the obtained results generally lies in the development of labor potential neural network modeling, in the development of new models, methods and algorithms for its analysis and control decisions justification. Keywords: Labor Potential · Neural Networks · Compositional Approach · Forecasting

1 Introduction In order to manage the labor resources of an organization effectively, as complexly structured dynamic objects, it is necessary to be able to assess the current states and predict changes in the ability of actors (from an individual employee and primary labor collective to an enterprise) to achieve a certain performance. Their individual and combined abilities, competencies, individual and group behavior should be taken into account. Such an assessment and subsequent management is to be carried out with due regard for the influence of factors in the production environment and management. Within the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 140–153, 2023. https://doi.org/10.1007/978-3-031-34329-2_15

A Compositional Approach to Labor Potential Evaluation

141

framework of this problem a specific research task is to assess the influence of working environment interacting factors and the individual qualities of actors on the efficiency of labor activity. For these purposes, it is advisable to use the concept of labor potential management. The possibilities for creating effective methods, algorithms for assessing and predicting labor potential states in the future are significantly limited due to the difficulties of formalizing it with traditional approaches. A promising method for solving these problems is the use of artificial intelligence methods and, above all, artificial neural networks. Currently, there is no coherent system of views on labor potential neural network modeling structure and dynamics in the literature. Most of the well-known works on neural network modeling consider the solution of determining the dynamics of individual characteristics particular problems without assessing the impact on the results of projects and organizations. Any organization development problems solving success is largely determined by the presence and correct use of its labor potential, which is constantly changing under the influence of a large number of various external and internal factors. In particular, the labor potential is very sensitive to the conditions of training and work, the availability of production means, the psychological atmosphere, aspects of information exchange and other factors [1, 2]. In cases of insufficient or incorrect use of it, individual employee or organization possibilities of achieving the goal and high performance are extremely limited. The choice of the necessary conditions that optimally influence the realization of labor potential, in the sense of its goal-oriented formation and the highest completeness of use, becomes a key task of strategic management. It is important that the reproduction of labor potential requires significant expenditure of resources and must be justified by the return on it [3].

2 Compositional Model of Labor Potential and a Set of Neural Network Algorithms that Provide Its Management Analysis of studies published results on labor potential allows to conclude that most of them were carried out within the framework of the resource approach. It involves the analysis of quantitative and qualitative indicators of the labor force, followed by comparison with one or more indicators of target economic systems activities. The results of such studies are very useful for assessing the mutual influence of human resource factors and their impact on various aspects of organizational performance. However, the problem of assessing the potential capabilities of an actor, taking into account the internal and external conditions of his labor activity, “potential” as it is, remains outside the framework. Note that an individual worker, labor collective, organization, group of companies, industry, territorial economic systems can be considered as an actor [4]. Person’s potential capabilities in the labor process is a multidimensional phenomenon that includes three enlarged groups of characteristics: (1) abilities—person’s available psychophysiological resources; (2) competence—set of skills, experience and inclination for self-development, which determines the ability to perform labor functions; (3) behavior—motivation and value orientations that determine the level and nature of activity in the labor process.

142

O. Ogiy and V. Osipov

These characteristics dynamic interaction complex system determines the potential of an actor only on the basis of the properties inherent in a person or a social group. However, the performance of the labor function also depends on factors external to the employee, which we commonly call “working conditions”. There are a sufficient number of studies that convincingly prove the influence of working conditions on the significant characteristics of workers and labor productivity [5–8]. Working conditions are considered to be no less complex object of a study than the actor, and they include many parameters that directly affect the ability to perform a labor function and determine the effectiveness of labor. As a rule, criteria and indicators of working conditions depend on the system of national standards in the field of working life quality. They are quite well developed and include scientifically based assessment parameters adopted in a particular country or industry. It greatly simplifies the solution of the parameterization problem when developing a model for work conditions assessment. The workplace and the associated working conditions characteristics complex forms a kind of “rest potential”, while the properties of the actor form an “action potential”. The mutual influence of the complexes of the properties potentials has the greatest significance in assessing the labor potential rather than the actor and working conditions. Therefore, labor potential must be considered as a composition of factors and evaluated as a result of their interaction (Fig. 1). The compositional approach we propose to the labor potential analysis includes nine criteria for assessing an actor and four criteria for assessing working conditions, which, in turn, are specified in measurable parameters. Thus, our compositional model includes forty-seven indicators. We present a list of indicators for each criterion: current physical state: (1) body mass index, (2) operator performance, (3) health self-assessment; health potential: (1) chronic diseases, (2) health risk factors, (3) physical activity and exercise; personality traits (according to the BFI-2 method): (1) extraversion, (2) tendency to agree, (3) impulsiveness control, (4) negative emotionality, (5) openness to experience, (6) subjective assessment of the happiness level; the qualification level is determined by the highest confirmed level of education and is evaluated in points; competency-based proactivity: (1) professional development over the past year, (2) knowledge of foreign languages, (3) use of the Internet for learning and selfdevelopment, (4) experience in creating one’s own business; length of service and experience: (1) the duration of the general length of service and the performance of a specific labor function (work in a position), (2) an expert description of the professional experience and the career path; behavior: (1) type of work motivation (we use the Motype test by V.I. Gerchikov [9]), (2) value ideals (orientations), (3) value profile. To assess the value orientations and profile of employees, we use a value questionnaire by S. Schwartz [10]; hygienic factors of working conditions: (1) chemical factor, (2) biological factor, (3) aerosols of mainly fibrogenic action, (4) vibroacoustic factors, (5) microclimate, (6)

A Compositional Approach to Labor Potential Evaluation

143

Fig. 1. Labor potential factors composition.

light environment, (7) non-ionizing electromagnetic fields and radiation, (8) work with sources of ionizing radiation, (9) air aeroionic composition; applied equipment and technologies: (1) average age of machinery and equipment, (2) degree of wear of machinery and equipment, (3) renewal coefficient of machinery and equipment, (4) expert assessment of the production processes organization and management; the severity of labor: (1) physical dynamic load, (2) the mass of the load lifted and moved manually, (3) stereotyped working movements, (4) static load, (5) working posture, (6) body tilts, (7) movement in space; labor intensity: (1) intellectual loads, (2) sensory loads, (3) emotional loads, (4) monotony of loads, (5) working mode. As noted above, when assessing working conditions, other parameters can be used that are accepted in a particular industry or system of national standards. We relied on the methodological approach set out in [11]. So, the states of labor potential are characterized by the states of actors, which significantly depend on working conditions.

144

O. Ogiy and V. Osipov

It is possible to determine the state of labor potential in practice by higher level data that includes the information about the abilities and capabilities of actors to perform labor functions by type of work or position. This having been said, the state of the labor potential at the current moments of time under fixed working conditions can be determined by the distribution of actors by labor functions (positions) with weight coefficients. The initial data to form such distributions can be generated by personnel services. Monitoring is possible using various methods and algorithms. In cases where it is feasible to obtain depersonalized digital information about the capabilities of actors in solving problems in accordance with current and future job responsibilities, task can be reduced to statistical processing. However, the evaluation frequency question remains open. Seasonal, semi-annual, annual, five-year control is possible taking into account the specifics of the actor’s activity. In general, the structure of the labor potential management process includes the following stages: (1) monitoring the potentials of actors and working conditions, (2) labor potential neural network forecasting and development of managerial decisions; (3) implementation of management decisions. The process is cyclical. Based on monitoring data, as well as possible corrective actions, it is feasible to predict future states of the labor potential using neural network systems. Both feed-forward neural networks and recurrent neural network models are applicable solving the problem. Some of them are reviewed in [12–17]. Despite a significant number of well-known works on neural network prediction of various events [18], many questions remain open in relation to determining the future states of labor potential. These questions are associated with the need to take into account a large number of interrelated factors, identify the laws of potential change, determine the influence of control actions, and reduce the computational complexity of the task itself. It also refers to the need to develop special methods for encoding initial data to ensure their processing by artificial neural networks, as well as the use of special methods for training these networks. Management can be carried out in order to maximize the use of labor potential in solving the current and future tasks of the actor with restrictions on resource costs.

3 State Space of Labor Potential and Goals Feasibility To determine the space of labor potential for practical purposes, it is proposed to proceed from a finite set of different labor functions characteristic of the respective positions (professions, jobs). The successful performance of a labor function requires a certain set and combination of the actor’s qualities. In other words, the labor potential should be characterized by the actor’s potential corresponding to this labor function (position) at a given time interval under certain working conditions. If we focus on the appropriate distribution of the organization employees’ individual potentials, then by the sum of them, it is possible to determine the labor potential of the organization taking into account the relative importance of labor functions. In practice, the same labor functions are performed by workers with different individual potentials. In this case, to determine the total potential of the organization, it is also necessary to take into account the relative weight of individual potentials. Considering the dynamics of changes in potentials with a

A Compositional Approach to Labor Potential Evaluation

145

large number of employees, it is proposed to use a grouping by age and put the groups in line with implemented or prospective labor functions (positions) distributions as shown in Table 1. Table 1. Employee potential distribution Age groups (years)

Job functions F1

F2

F3

…

FN

1 (≤20)

C1,1

C1,2

C1,3

…

C1,N

2 (21–25)

C2,1

C2,2

C2,3

…

C2,N

3 (26–30)

C3,1

C3,2

C3,3

…

C3,N

4 (31–45)

C4,1

C4,2

C4,3

…

C4,N

5 (46–50)

C5,1

C5,2

C5,3

…

C5,N

6 (51–55)

C6,1

C6,2

C6,3

…

C6,N

7 (56–60)

C7,1

C7,2

C7,3

…

C7,N

8 (61–65)

C8,1

C8,2

C8,3

…

C8,N

9 (66–70)

C9,1

C9,2

C9,3

…

C9,N

10 (≥71)

C10,1

C10,2

C10,3

…

C10,N

Table 1 labor potential Cij i-th age group in relation to the j-th labor function (position) is defined as the sum of the potentials of employees appointed to positions of the j-th type, Cij =

Kij 

Cijk ,

(1)

k=1

where Cijk —the potential of the k-th actor from the i-th age group in relation to the j-th type of position. Potential Cijk depends on the reference labor potential Cij0 for the considered labor function (position) and relative weight Vijk actor towards it, Cijk = Vijk · Cij0 .

(2)

Taking this into account, the total labor potential of each age group is Ci =

Kij N  

Cijk

(3)

j=1 k=1

And all the groups– C=

Kij N  I   i=1 j=1 k=1

Cijk.

(4)

146

O. Ogiy and V. Osipov

Let us pay attention to the fact that the potential is determined for fixed moments of time ts and labor conditions Gs . Taking this into account, it can be expressed as C(ts , Gs ) =

Kij N  I  

Cijk (ts , Gs ).

(5)

i=1 j=1 k=1

As a result, the time interval T is characterized by a series of labor potential values C = {C(t0 , G0 ), C(t1 , G1 ), C(t2 , G2 ), C(t3 , G3 ), . . . , C(tS , GS ), . . . , C(tT −1 , GT −1 )} (6) with their working conditions. These conditions can also be presented in the form of a separate dynamic series G = {G0 (t0 ), G1 (t1 ), G2 (t2 ), G3 (t3 ), . . . , GS (tS ), . . . , GT −1 (T − 1)}

(7)

At each point in time, the working conditions can be characterized by a finite set of coded data. In view of the abovementioned, the energy consumption of the labor potential over a given time interval T is equal to W (T ) =

T −1 

C(tS , GS ) · tS ,

(8)

s=0

where tS —the time interval to which the s-th labor potential is tied C(tS , GS ). Considering the random nature of the labor potential, the probability of fulfilling (achieving) the given target indicators for the time T can be calculated as   W(T) , (9) P(T) = 1 − exp − W where W —the average value of the labor potential energy required to obtain a result or achieve a given target. The exponential nature of the change in P(T) is due to the total flow of this potential and its multifactorial nature. To determine the values of the labor potential for future moments of time, it is necessary to analyze its state in the past and predict events. It should be taken into account that the elements of labor potential not only strongly depend on working conditions, but are also interconnected. Such dependencies, which are difficult to detect, can only appear during statistical or associative processing of the results of the potential realization. The identification and use of these dependencies makes it possible to predict changes in the labor potential more accurately. In its favor, it is necessary to monitor the states of the actors’ potentials and working conditions.

4 Specific Features of Monitoring the Composition of Labor Potential Factors The periodic survey is the main method of monitoring the potential of an actor, which includes a structured survey and testing, as well as an analysis of statistics available to any personnel service (data on work experience, education level, career trajectory).

A Compositional Approach to Labor Potential Evaluation

147

Monitoring of working conditions is a periodic assessment of the working conditions indicators discussed in the second section. It should be noted that the monitoring of labor potential in order to predict possible events should be carried out at a frequency at which the errors associated with the omission of possible events will not exceed the specified values. Taking into account the labor potential parameters inertia of changes, the recommended maximum frequency of data collection and processing should not exceed twice a year. As a result of such data collection, multidimensional series are formed, which can be represented as successive sets of numerical matrices, reflecting both the states of the actor’s potential and working conditions. (Fig. 2).

t1

t2

t3

t4

t5

Fig. 2. An example of numerical matrices sequence reflecting the state of the labor potential at any one time

The elements of these matrices can take on values significantly greater than one. However, when planning the use of impulse neural networks to predict labor potential and develop managerial decisions, these matrices must be converted into Boolean matrices with elements 0 and 1. The conversion of each initial matrix, which carries information about labor potentials, to a Boolean form is possible in two main ways [19]: – conversion of each original matrix into a matrix of a larger size. In this case, each element of the original matrix is assigned a Boolean matrix of a given size; – representing each original matrix as a sequence of Boolean matrices of the same size. Let us explain the second way a little bit more detailed. There are two main groups of data coding for processing them in impulse neural networks [19]. These are coding by pulse repetition rate and coding by the time of pulses appearance in a given time slot. Mixed coding is also possible. Among the options for coding the pulse repetition rate, the time averaging rule is used most often. According to this rule, the speed of pulses V is defined as V =

Nu , τ

(10)

148

O. Ogiy and V. Osipov

where Nu —the number of pulses in a given time slot of duration τ . If by speed we mean Cij converted data, then Cij =

Nu . τ

(11)

In this case each meaning of Cij a specific value of pulses in a given time slot is matched. Knowing the maximum value of Cij and τ value we can find the maximum value of Nu , which will determine the number of Boolean matrices representing the original matrix of potentials and working conditions. To reduce the number of these Boolean matrices when processing large values of Cij we can use a shift of the latest by level or normalization with rounding. When encoding the time of pulses, each value of Cij is assigned to the moment of occurrence of a single pulse in a given time slot. The maximum number of these moments determines the maximum value Cij . With such conversions, instead of labor potentials initial matrices sequence (Fig. 2), sequences of Boolean matrices with elements 0 and 1 will be introduced into the impulse neural network.

5 Neural Network Model for Labor Potential Forecasting For dynamic series prediction, various neural network models and methods are applicable [12–17, 20]. The most promising include models and methods that involve the use of recurrent neural networks [21, 22]. Their potential is significantly higher than the potential of direct distribution networks. However, these networks are not well developed. In particular, to predict the labor potential, some networks are applicable to some extent such as recurrent networks based on multilayer perceptrons, networks with long-term short-term memory, associative memory devices, bidirectional associative memory, selforganizing neural network structures. Training of recurrent neural networks is possible with and without a teacher. After training with a teacher, followed by fixing the parameters, successful forecasting is possible only with stable flow laws. When these laws change, retraining of such networks is required. The main disadvantage of traditional neural network models lies in the narrow functionality in solving creative problems, including dynamic series forecasting. Streaming pulsed recurrent neural networks (RNNs) with controlled elements [23], are self-learn based on current data; they have significant potential for expanding the ability to predict dynamic series that reflect labor potential. The layers of these RNNs can be provided with linear, spiral, loop and other logical structures. At present, the applicability of pulsed RNNs with controlled elements for predicting labor potential has not been practically studied yet. In a generalized form, the structure of a pulsed RNN with controlled elements [22] can be represented as a diagram (Fig. 3). Previously decomposed into components signals are introduced into this network. In this case, each component is transformed into a sequence of single images that carry information about the initial values of individual potentials. The first and second layers of the network are identical and contain N impulse neurons each. Each neuron of one layer

A Compositional Approach to Labor Potential Evaluation

149

Fig. 3. Diagram of a streaming impulse recurrent neural network with controlled elements.

is generally connected by its synapses with all the neurons of another layer. Neurons of the same layer do not have connections with each other. When transmitting single images aggregation (SIA) from layer to layer, by controlling synapses, spatial shifts of these collections are carried out. Due to such shifts, the layers of the network are divided into logical fields, and SIAs move along the layers. SIA promotion schemes along the layers can be different: linear, spiral, loop, and others. Due to the priority of short links in this network, a one-to-one correspondence is established between its input and output. As they move along the SIA layers, they bind to each other, which manifests in a change in the weights of synapses, and call the signals associated with them from long-term memory, exciting the corresponding neurons of the network. Each neuron is fired if the potential at its input exceeds a preset threshold. When a neuron is fired, a single image (impulse) is formed at its output, then the neuron goes into a state of immunity. In this state, it stays no less than the delay time of single images in the formed two-layer network contours. Its associative capabilities largely depend on the adopted scheme for promoting SIA along the layers of this network. Taking into account work [23], we will refine the proposed algorithm for the functioning of the recurrent neural network. The value of the potential at the output of the i-th impulse neuron of the receiving layer of the RNN can be determined as   1, N j=1 xj (t) · wij (t) ≥ U0 ; ti0 ≥ TR . (12) xi (t) = 0, In this expression, the designations are adopted: xi (t) values of the output potentials of the neurons of the RNN transmitting layer; wij (t) synapse weights connecting the i-th receiving neuron with the j-th transmitting neurons; N number of neurons in each RNN layer; neuron firing threshold; U0 time elapsed since the previous firing of the i-th neuron; ti0

150

TR

O. Ogiy and V. Osipov

neuronal immunity time after firing. Weights wij (t) of the synapses are un the form wij (t) = kij (t) · βij (t) · ηij (t),

(13)

where kij (t)—weight coefficient,

  kij (t) = th γ · gij (t) ; th(z) =

ez − e−z , ez + e−z

(14) (15)

gij (t) conditional number of impulses that passed through the ij-th synapse,

gij (t) = gij (t − t) ± gij (t),

(16)

gij (t) increment gij (t). This increment is positive when the firing pulse arrives at the receiving neuron in the standby state; ηij (t) weakening function of convergent single images; βij (t) attenuation function of divergent single impulses transmitted from j-th neurons to i-th neurons, βij (t) =

 2 1 , r (t) = ( xij (t) + nij (t)d 1 + αij · rij (t) ij  2 1 + yij (t) + mij (t)q ) 2 ; nij (t) = ±0, 1, . . . , D − 1; mij (t) = ±0, 1, . . . , B − 1;

(17)

rij

remoteness of neurons connected through synapses (distances between them on the X, Y plane). It is assumed that the distance between the interacting layers of the neural network tends to zero; xij , yij are projections of the connection of the j-th neuron with the i-th on the X, Y axes without taking into account spatial shifts; d, q are the values of unit shifts, respectively, along the coordinates X, Y; L, M are the number of columns and rows, respectively, into which each layer of the neural network is divided due to shifts. The product of d × q determines the area of the working field of the network each layer. This area is equal to the number of neurons included in the field. Erasure of information about single pulses from synapses is feasible due to partial reflection of single pulses from the layers of the network. To increase the efficiency of determining the weights of synapses, the necessary tables of values can be pre-formed. These are tables of possible values of the attenuation function for given shifts of aggregates of single images along the layers of the network and weight coefficients of synapses for different values gij (t). Such tables are formed at the first step of the algorithm. In order to endow the layers of the neural network with the required logical structure, two-dimensional arrays are formed from one-dimensional arrays of neurons states in the

A Compositional Approach to Labor Potential Evaluation

151

layers of the network. Then they are divided into logical fields of size d × q neurons. As a result, each layer can be viewed as a sequence of lines, each of width q, consisting of separate d length fields. To implement the required spiral structure of the layers, it is sufficient to set the corresponding parameters nij and mij for the synapses involved in the transmission of single images aggregation from the second layer to the first or from the first layer to the second. Other structures of neural network layers can be formed similarly. In the interests of predicting the labor potential, a sequence of single images aggregation is introduced into considered RNN, carrying information about the potentials of actors and working conditions at snapshots. When passing through such a sequence of SIA, a model of the perceived process is formed in the neural network. If after the last SIA we strengthen the associative recall of signals from the RNN memory in the direction of its input, the network will recall single images from the memory, reflecting future events at snapshots. In our case, the network will recall the encoded potential values for fixed age groups from memory. After the inverse transform of such forecast results, it is possible to obtain the decoded values of the required labor potentials.

6 Conclusion Based on the monitoring data of the actor’s properties and working conditions, as well as possible corrective actions on these potentials, it is possible to predict its future states using neural network systems. In its favor, both feedforward neural networks and recurrent neural network models are applicable. Managing the labor potential is feasible by various methods and algorithms, both by influencing actors and by changing working conditions. It is possible through special information impacts on employees, stimulation, redistribution of efforts, changing qualification requirements, introducing new competencies, optimizing labor functions, updating equipment and technologies, and also some other ways. It is proposed to use its energy as an integral indicator of labor potential. The advantage of the compositional approach is the ability to evaluate and manage the labor potential as energy, and at the same time perform decomposition operations, evaluating individual parameters or their groups to solve various management problems. The proposed method for substantiating appropriate ways to manage labor potential using neural network forecasting is based on the following initial data and tools: recurrent neural network with controlled elements; dynamic series characterizing the potentials for the identified actors age groups; dynamic series of conditions for the manifestation of labor potentials, tied to dynamic series; a finite set of possible conditions for the manifestation of labor potentials; means of encoding initial data and decoding forecasting results; requirements for the forecast horizon; requirements for forecast accuracy, taking into account the forecasting horizon. Reducing the complexity of neural network processing is achievable by decomposing difficult tasks into simple tasks, considering the work function specific, organization

152

O. Ogiy and V. Osipov

activity project or area. In all the cases, it is necessary to rely on real dynamic series. It is necessary to conduct preliminary studies to obtain practically significant forecasts of the labor potentials of actors in various professional fields using the proposed models, methods and algorithms. The goal of the studies is to describe labor functions and collect data on the working conditions factors that are significant for professional activity particular area. Such analytical work will make it possible to carry out neural network data processing and forecasting in the best possible way, and then, as a result, to substantiate appropriate decisions.

References 1. Llenado, H., Lyndon, A.: Mediating effect of employee accountability on the relationship between working condition and organizational health. Am. J. Multidisc. Res. Innov. 1, 228– 243 (2022) 2. Heo, J.: A study on the factors affecting satisfaction with working conditions in agriculture. Kor. J. Agric. Manage. Policy 49, 484–503 (2022) 3. Yudina, L., Kosareva, E.: Nejronnye seti kak instrument obiyektivizacii ocenok trudovogo potenciala (Neural networks as a tool for objectifying the assessment of labor potential). Biznes. Obrazovanie. Pravo. Vestnik Volgogradskogo instituta biznesa 2(39), 110–113 (2017) 4. Ogiy, O., Osipov, V., Tristanov, A., Zhukova, N.: The process of managing labor potential of the fishery complex as an object of modeling using artificial neural networks. AIP Conf. Proc. 2661(1) (2022). https://doi.org/10.1063/5.0107815 5. Watanabe, K., Kawakami, N.: Effects of a multicomponent workplace intervention programme with environmental changes on physical activity among Japanese white collar employees: a protocol for a cluster randomised controlled trial. BMJ Open 7(10), 1–10 (2017) 6. Lima Hostensky, E., Blanch, J., Ochoa Pacheco, P., Roesler, V.: Working conditions and meanings of working experience: the case of the justice workers. Psicologia—Teoria e Prática 24(3) (2022). https://doi.org/10.5935/1980-6906/ePTPSS15512.en 7. Conway, P., Rose, U., Formazin, M., Schoellgen, I., d’Errico, A., Balducci, C., Burr, H.: Long-term associations of psychosocial working conditions with depressive symptoms and work-related emotional exhaustion: comparing effects in a 5-year prospective study of 1949 workers in Germany. Int. Arch. Occup. Environ. Health (2023). https://doi.org/10.1007/s00 420-023-01959-8 8. Korošec, D., Dominika, V., Stiglic, G.: Health conditions and long working hours in Europe: a retrospective study. Int. J. Environ. Res. Publ. Health 19 (2022). https://doi.org/10.3390/ije rph191912325 9. Gerchikov, V.: Tipologicheskaya koncepciya trudovoj motivacii (Typological concept of labor motivation). Motivaciya i oplata truda 2, 53–62 (2005) 10. Schwartz, S., Zanna, M.: Universals in the content and structure of values: theory and empirical tests in 20 countries. Adv. Exp. Soc. Psychol. 25, 1–65 (1992) 11. Labor hygiene. Guidelines for the hygienic assessment of factors of the working environment and the labor process. Criteria and classification of working conditions—R 2.2.2006-05. 2.2 (in Rus). http://www.consultant.ru/document/cons_doc_LAW_85537/. Accessed 01 Mar 2023 12. Singh, S., Shukla, H.K., Singh, A.P., Srivastava, R., Gangwar, M.: Comparative analysis of neuro-fuzzy model for human resources. Int. J. Sci. Technol. Res. 9, 246–254 (2020) 13. Nunes da Silva, I., Spatti, D.H., Flauzino, R.A., Bartocci Liboni, L.H., Silas Franco dos Reis Alves: Artificial Neural Networks: A Practical Course. Springer International Publishing, Switzerland (2017)

A Compositional Approach to Labor Potential Evaluation

153

14. Kraus, M., Feuerriegel, S., Oztekin, A.: Deep learning in business analytics and operations research: models, applications and managerial implications. Eur. J. Oper. Res. 281(3), 628– 641 (2020) 15. Perez-Campdesuner, R., De-Miguel-Guzman, M., Sanchez-Rodrıguez, A., Garcıa-Vidal, G., Martınez-Viva, R.: Exploring neural networks in the analysis of variables that affect the employee turnover in the organization. Int. J. Eng. Bus. Manage. 10, 1–11 (2018) 16. Sexton, R.S., McMurtrey, S., Michalopoulos, J.O., Smith, A.M.: Employee turnover: a neural network solution. Comput. Oper. Res. 32(10), 2635–2651 (2005) 17. Akinyede, R.O., Daramola, O.A.: Neural network web-based human resource management system model (NNWBHRMSM). Int. J. Comput. Netw. Commun. Secur. 1(3), 75–87 (2013) 18. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, New York (2008) 19. Auge, D., Hille, J., Mueller, E., Knoll, A.: A survey of encoding techniques for signal processing in spiking neural networks. Neural Process. Lett. 53, 4693–4710 (2021) 20. Stavrou, E.T., Charalambous, C., Spiliotis, S.: Human resource management and performance: a neural network analysis. Eur. J. Oper. Res. 181, 453–467 (2007) 21. Cerisara, C., Kral, P., Lenc, L.: On the effects of using word2vec representations in neural networks for dialogue act recognition. Comput. Speech Lang. 47, 175–193 (2018) 22. Osipov, V., Osipova, M.: Space-time signal binding in recurrent neural networks with controlled elements. Neurocomputing 308, 194–204 (2018) 23. Osipov, V.: Rekurrentnaya nejronnaya set’ so spiral’noj strukturoj sloev. Informacionnoupravlyayushchie sistemy 6, 28–32 (2012)

The Use of Neural Network Technology in Bank Digital Ecosystems Alexey Snytnikov1 , Marina Solovey1 , and Larisa Zelenina2(B) 1 Kaliningrad State Technical University, Kaliningrad, Russia 2 Nothern (Arctic) Federal University Named After M.V. Lomonosov, Arkhangelsk, Russia

[email protected]

Abstract. The article deals with the application of methods and systems of artificial intelligence in the banking sector. Attention is paid to digital ecosystems as an actual direction for the development of companies, including methods of big data processing, the data being generated by modern ecosystems. Artificial intelligence is understood as the ability to learn and use knowledge. Neural networks are considered as a tool for implementing these abilities. The development of artificial intelligence systems and methods is shown by the example of the activities of the federal center for competence of the Artificial Intelligence project of Sberbank PJSC, which implements the best AI solutions in various sectors of the economy. Due to the fact that neural networks and machine learning can be considered as part of the digital ecosystem of the banking sector of the economy, the article discusses both the main stages of working with neural networks (neural network architecture) and their application in the banking sector using the examples of credit risk assessment, forecasting stock indicators, fluctuations in the exchange rate. This paper also demonstrates the use of a neural network as a universal tool for approximating functions used to predict the stock price of a company’s shares. Keywords: Artificial Intelligence · Ecosystem · Internet Banks · Banking · Artificial Neural Network · Banking Sector

1 Introduction Digital ecosystems are a fairly new direction in the development of large companies in various industries. The advantages of ecosystem approach are that by combining various suppliers of goods and services on one platform, the time of customer service is reduced. Moreover, the quality of the company’s business processes is growing. Another advantage is that the functionality is expanding due to the introduction of new services and technologies. Ecosystems are being used into many areas, such as logistics, tourism, financial sector, transport, housing and communal services, state and municipal management. Digital ecosystems have become widespread in the Russian banking sector. The largest ecosystems in this sector are those of banks such as VTB, Gazprombank, Alfa Bank and others. For example, Tinkoff Bank’s ecosystem offers such services as insurance, stock market, mobile communications, travel, various educational programs and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 154–162, 2023. https://doi.org/10.1007/978-3-031-34329-2_16

The Use of Neural Network Technology in Bank Digital Ecosystems

155

services in addition to standard banking services. In addition, the bank is expanding its partner network in the sections “Movies”, “Restaurants”, “Fuel”, “Goods” and others. Other banks pursue a similar strategy. One of the first ecosystems in the Russian banking sector was Sberbank’s ecosystem, which began functioning on this bank’s platform in 2016 [1, 2]. Currently, this ecosystem is actively developing, primarily through the creation or acquisition of various services. At the same time, companies that do not necessarily provide banking services, but offer other services to clients, and are different legal entities, interact on the same platform. Different companies cooperate on the same digital platform because it is profitable for them. According to open sources, the ecosystem now includes about six hundred services offering various features not only for citizens (more than 100 million active retail clients have registered in the Sberbank ecosystem so far), but also for businesses (more than three million legal entities). Thanks to the ecosystem, a consumer-friendly environment is formed, which makes it possible to solve many issues with minimal time and financial losses. The figure (see Fig. 1) shows Sberbank’s ecosystem diagram in the context of the main services for B2B (business to business) and B2C (business to consumer).

Fig. 1. Sberbank ecosystem diagram

The consolidation of various services and services on banking platforms and the creation of ecosystems leads to an avalanche-like growth of information, as information comes from various sources, which allows centralized tracking of the behavior of each customer in the market and forming its “digital footprint” in the ecosystem. The example of the Sberbank ecosystem suggests that traditional means and methods of information processing and storage, such as storage, search and issuance of information

156

A. Snytnikov et al.

upon request, are insufficient for the successful functioning of such a large information object. Approaches such as Data Mining and Big Data have been developed to process and analyze large volumes of information generated by ecosystems. Each of these approaches has great potential for use in ecosystems. Let us consider the capabilities of each of these tools.

2 Methods The article deals with such methods of information processing as Big Data, Data Mining, Neural networks and machine learning. Big Data technology Big Data technology, as we know, is designed to process large volumes of structured and unstructured information for organizational purposes. This significantly reduces the time for decision-making. For example, to issue a loan, the bank must first process information about the customer, his ability to pay, credit history, etc. Previously this took several days, but now, thanks to Big Data technology, it takes a few minutes. Also owing to this technology the bank may analyze the client base in terms of clients’ behavior in the market, their preferences, and their attitude towards new banking services. Such information is accessible because within the ecosystem the client performs certain actions in various services and services, thanks to which the bank effortlessly forms a digital portrait of the client. This makes it possible to develop specific offers for individual clients, and to work “ahead of the curve”, i.e. to offer banking and other services within the ecosystem individually for each client. Also, thanks to this technology, the bank can foresee the bankruptcy of its clients—legal entities by indirect signs, such as a decrease in the organization’s income. This will make it possible to decide as quickly as possible whether to grant or refuse a loan to an organization. Also, thanks to Big Data banks can actively use online lending, because the decision-making in specific cases takes very little time in the presence of objective analytical information. Another option for the use of this technology can be the identification and prevention of dubious transactions based on the analysis of customer behavior [3, 4]. Data Mining Data Mining is widely used in the ecosystem of banks. It has successfully proven itself in such tasks as the analysis of credit risks, finding patterns in the use of banking products and services by customers, various types of forecasts, securities portfolio management, detection of fraud, analysis of investment projects, and more. The main tools of Data Mining can be divided into cluster analysis, decision trees and neural networks. Thanks to the Data Mining technology banks get the data they need to make appropriate decisions. We can conclude that ecosystem technology increases the efficiency, profit, quality and functionality of companies, but in order to use ecosystems as effectively as possible, it is necessary to introduce advanced methods of information processing, such as data mining, reliable storage of large amounts of information.

The Use of Neural Network Technology in Bank Digital Ecosystems

157

Neural Networks and Machine Learning as Part of the Digital Ecosystem of the Banking There are several types of artificial neural networks depending on the structure and operating principles. They are fully coupled direct propagation networks (multilayer perceptron) and radial basic functional networks. A neural network consists of three types of neuron layers. The first layer or input layer is responsible for data input. The second layer is responsible for data processing and obtaining intermediate results, and the third layer is responsible for outputting results. Artificial neural network is trained by comparing input and output data [4]. The most widely used neural networks are feedforward neural networks. They are functions of the following kind: ⎛ ⎞ N  wj xj ⎠, . . . , i = 1, . . . , M (1) yi = f ⎝ j=1

Here xj are the input values of the neural network (a set of independent variables), yj—output values (dependent variables, the values of function to be approximated), wj—weights, f—some nonlinear function which depends on the problem under study. When there are several such functions (or several tens of them), called in series so that the output values of one of them are input values for the other, we speak of a multilayer or deep neural network. Training of the neural network, i.e. selection of weighting factors in order to provide approximation of the required function (for example, value of shares from time, exchange rate, probability of credit default etc.) with the help of a neural network is performed as follows. It is necessary to set the loss function, which will somehow determine the degree of approximation to the approximated function, for example, using the values themselves, where they are known, or using some property of this function. Next, the minimization of the loss function is performed. The result is an expression that approximates the desired function.

3 Results The result of the study is the development of recommendations on the use of data processing and analysis methods, which were discussed above, in banking ecosystems. Artificial Intelligence Artificial intelligence in general and neural networks in particular, used in Data Mining processes, as described above, are actively being introduced in the banking sphere. These concepts are interrelated, since a neural network is one of the artificial intelligence technologies. The term “artificial intelligence” implies the ability to learn and use knowledge, and neural networks allow to implement this process [5, 6]. The main tasks that can solve these technologies are complex nonlinear predictions, pattern recognition and speech. Neural networks, which are from the mathematical point of view a universal tool for approximating functions, including such functions whose nature is completely unknown,

158

A. Snytnikov et al.

can be used in the assessment of credit risks, creating chat-bots for working with clients on the Internet or predicting stock indicators and currency fluctuations. A significant stimulus for the introduction of artificial intelligence in the banking sector has been the development of Internet banking. Currently, the main goal of the introduction of artificial intelligence in the banking sector is to provide personalized and high-quality services, focused on the needs of customers [7]. The presence of rapid and widespread changes in the competitive environment (including by high-tech nonbanking organizations), the processing of large amounts of information, the high speed of business processes stimulates the introduction of innovative systems and methods of artificial intelligence in the modern banking environment [8]. Sberbank is a leader in the use of artificial intelligence systems and methods, striving to implement artificial intelligence in all organizational processes and extend it to many areas of business. Sberbank as a federal center of competence of the federal project “Artificial Intelligence” helps to implement the best AI solutions in various sectors of the economy. Let us consider only some of them. In 2016, Sberbank CEO Herman Gref said that in five years Sberbank will be able to make 80% of all decisions using artificial intelligence. In 2017, a plan was developed to replace call center employees with artificial intelligence in 3–5 years, and by 2025 the bank expects to automate almost all simple actions. That same year, about 3,000 jobs were scheduled to be freed up due to the introduction of a family of robotic lawyers. In its 2018 annual report, Sberbank summarized the first results of the implementation of AI-transformation (Artificial Intelligence, artificial intelligence): routine operations in 53 processes at the bank began to be performed by robots instead of employees, with software robots in 30 processes beginning to handle 100% of industrial volume of operations, in 23 processes handled 50–80% of industrial volume. As a result, back office efficiency was improved by 25%. In 2019, Sberbank began using artificial intelligence for corporate lending. At the end of 2020, the economic effect of using artificial intelligence in corporate and retail collection processes at Sberbank exceeded 2 billion rubles. In 2021, the first program created by Artificial Vision (Artificial Intelligence), which is part of a software suite developed by the Sber AI team as part of the research and direction of AGI (Strong Artificial Intelligence), was registered in Russia. This program allows artificial intelligence to recognize and analyze objects in virtual reality, in particular to create an AI model capable of existing in a virtual environment and learning to perceive visual scenes. A transformer neural network was used, with additional training in a corpus of programming languages. In the same year, the ruDALL-E neural network was created in Russia, capable of creating images based on a text description in Russian. In 2022, thanks to several thousand developed AI models, the financial effect in Sberbank was 205 billion rubles. At the same time, out of approximately 2,200 processes and 450 client paths, more than 65% of the processes and 90% of the client paths are AI-based. As noted earlier, neural networks are widely used in the banking sector of the economy. Let us consider their use in the assessment of credit risks and prediction of stock market indicators and exchange rate fluctuations. Credit risk Assessment

The Use of Neural Network Technology in Bank Digital Ecosystems

159

Credit risk is associated with the possibility of a customer defaulting on contractual obligations, such as mortgages, credit card debts and other types of loans. Minimizing the risk of default is a major challenge for financial institutions [9]. For this reason, commercial and investment banks, venture capital funds, asset management companies and insurance companies, among others, are increasingly relying on technology to predict which customers are more likely to stop paying their debts. Machine learning models are helping these companies improve the accuracy of credit risk analysis by providing a scientific method for identifying potential debtors in advance. The following three algorithms are most commonly used to predict credit risk: XGBoost LightGBM CatBoost There is a dilemma of evaluation metrics in this task. This means that decision makers will have to analyze the big picture using machine learning algorithms [10]. Predicting Stock Market Indicators and Currency Fluctuations Let’s demonstrate the use of neural networks for financial forecasting with the example of predicting Amazon’s stock price (data from http://www.Kaggle.com). For the predictions a neural network of LSTM architecture is used, the loss function is the modulus of the difference between predicted and known values. The result is shown in Fig. 2.

Fig. 2. Comparison of known stock values (blue curve) and predicted by the neural network (red)

Stock Price Forecasting Based on GRU Neural Network

160

A. Snytnikov et al.

Let us consider a standard implementation of a neural network for forecasting of exchange prices. A neural network of GRU type (Gated Recurrent Units) is a kind of recurrent neural networks used for time series forecasting [11, 12]. A neural network of this type contains three sets of logic gates (each representing a neural network). The update gate: determines how much information from previous points in time needs to be transferred to the future. This is similar to the output gateway in the recurrent block of the LSTM neural network, another type of neural network also often used in time series analysis. Reset Gate: determines how much of the past knowledge should be forgotten. This is similar to the combination of input and forgetting valves in the LSTM recurrence block. The current state valve: is used to introduce some non-linearity into the input, and to bring it to zero mean. Its other task is to reduce influence of previous information on current information, which is transmitted to the future. The scheme of GRU neural network in terms of mathematical operations is shown in Fig. 3. Here s—sigmoid function, tanh—hyperbolical tangent, —Hadamard product.

Fig. 3. Scheme of the GRU neural network

Consider an example of the implementation of the neural network of GRU type using the PyTorch library in the Python language. An important part of the forward method, which implements direct information propagation in the neural network, is the calculation of the hidden state of the GRU neural network, in this case represented as tensors h0 and hn , representing, respectively, the initial value of the hidden state and the current value of the hidden state. The Fig. 4 shows the listing of implementation of the neural network.

The Use of Neural Network Technology in Bank Digital Ecosystems

161

Fig. 4. The implementation of the neural network

4 Discussion In order to maximize the efficiency of using digital ecosystems, it is rational to introduce advanced information processing methods, such as data mining, used in the processing of large amounts of information and based on Data Mining and Big Data methods. Digital technologies are designed to have a significant impact on the functioning of the banking sector as a digital ecosystem, which is a client-centric business model and allows meeting the needs of customers of a new digital quality [13]. Sberbank’s best practices for implementing artificial intelligence systems and methods, aimed at supporting the implementation of the best AI solutions in various sectors of the economy, undoubtedly contribute to the active development of digital banking ecosystems. Artificial neural networks, machine learning methods, being part of the digital ecosystem of the banking sector of the economy, are widely used in the banking sector and can act as a universal tool in the tasks of analyzing and forecasting economic indicators.

5 Conclusion Digital technologies have a significant impact on the functioning of banking as a digital ecosystem, which is a customer-centric business model and allows us to meet the needs of new digital customers. It can also be argued that the mechanisms of neural networks are a universal method for solving a whole range of problems in banking practice.

162

A. Snytnikov et al.

References 1. Analytical source of Sberbank. https://spec.tass.ru/sber180/ekosistema-sbera?ysclid=l5tcrj ae60423716283. Accessed 12 Feb 2023 2. FinPriz analytical portal. https://finprz.ru/sberbank/ekosistema.php. Accessed 16 Jan 2023 3. Semenov A.V.: Application of “Big Data” technology in the Russian banking sector. Issues of Russian justice (2019). https://cyberleninka.ru/article/n/primenenie-tehnologii-big-data-vrossiyskom-bankovskom-sektore/viewer. Accessed 08 Feb 2023 4. Rodrigues A.R.D., Ferreira F.A.F., Teixeira F.J.C.S.N., Zopounidis C.: Artificial intelligence, digital transformation and cybersecurity in the banking sector: a multi-stakeholder cognitiondriven framework. Res. Int. Bus. Finan. 60 (2022) 5. Zelenina, L., Khaimina, L.E., Khaimin, E., Khripunov, D.D., Zashikhina, I.M.: The problem of images’ classification: neural networks. Math. Informat. 64(3), 289–300 (2021) 6. Zelenina, L.I., Khaimina, L.E., Khaimin, E.S., Antufiev, D.I., Zashikhina, I.M.: Neural networks in a character recognition mobile application. Math. Informat. 63(5), 484–500 (2020) 7. Stankevich, G.V., Amvrosova, O.N., Kasevich, C.V., Atayan, G.Yu., Kara-Kazaryan, T.V.: Features of artificial intelligence technologies and their use and impact on transformation in the banking sector. Adv. Res. Russ. Bus. Manage. 499–506 (2021) 8. Pallathadka, H., Raghunath, M.P., Bandi, A., Tongkachok, K.: Towards applicability of artificial intelligence in healthcare, banking and education sector. In: First International Conference on Technologies for Smart Green Connected Society 2021. ECS Transactions, pp. 16665–16671 (2022) 9. Tutygin, A.G., Chizhova, L.A., Zelenina, L.I., Tutygin, R.A.: Loan portfolio as an object of agent-oriented modeling. Econ. Manage. 5(127), 53–58 (2016) 10. Chollet, F.: Deep Learning with Python: Manning (2017) 11. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling (2014). https://arxiv.org/abs/1412.3555. Accessed 11 Jan 2023 12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 735–780 (1997) 13. Goncharenko, I.A.: Artificial intelligence and automation in financial services: the case of Russian banking sector. Law Econ. Yearly Rev. 8(1), 125–147 (2019)

The Concept of Digital Transformation in Educational Discourse Irina Guseva(B) and Elena Pliva Kaliningrad State Technical University, Kaliningrad, Russia [email protected]

Abstract. The article is devoted to the results of the study of modern educational discourse, the central aspect of which today is the topic of digital transformation. From the standpoint of cognitive-discursive analysis, the conceptual sphere of the concept of “digital transformation” in higher education is investigated, the key concepts of the area under consideration are highlighted, and the most significant ones are analyzed. Some obvious challenges in this process are identified, such as staffing imbalances, the need to take into account the virtues of traditional teaching approaches along with the acceleration of technological processes, increasing the level of digital maturity of the university and the strategic management of big data. There is a tendency to change the entire paradigm in education towards an ecosystem approach, which involves the evolution, renewal, interaction and interconnection of all components of the digital age in education. Keywords: Educational discourse · Digital transformation · Cognitive-discursive analysis · Conceptual sphere · Concept · Traditional teaching approaches · Digital maturity · Ecosystem approach

1 Introduction Currently, the ecosystem approach is actively spreading in the world in all spheres of the economy and society, including in the field of education. An ecosystem in its generic ecological understanding presupposes the coexistence, interconnection and interdependence of all its elements and components from each other. The understanding of the ecosystem approach in education has not yet fully developed, but already today researchers consider educational ecosystems or the educational environment as networks of interconnected participants in the educational process throughout life. Such systems should unite “students and communities, striving to unlock their individual and collective potential”. They are diverse, dynamic and constantly evolving. The new educational paradigm should become “a living ecosystem of knowledge, where everyone has their own ecological niche and the right to choose” [1]. The modern model of education is defined in scientific discourse as flexible personalized, personality-oriented communication in learning. This approach involves the use of new technological infrastructure, in particular, the transition to digital platforms and networks of educational opportunities. “…the network infrastructure is supported by © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 163–172, 2023. https://doi.org/10.1007/978-3-031-34329-2_17

164

I. Guseva and E. Pliva

digital technologies and creates conditions for joint activities and effective interaction with each other on a single technological platform, where each of the participants has access to common “ecosystem” resources, which he initially did not have or had, but in insufficient quantity” [2]. The digital educational environment or ecosystem is increasingly acquiring an innovative and project character and thereby contributes to achieving a synergetic effect as a result of the coordinated and joint work of the system elements of the triad “Science— Education—Real Sector (Business Environment)” on a single digital platform. This principle of work unites (or “brings together”) the processes of creating new knowledge, transmitting it to listeners, using and disposing of knowledge in the real sector [ibid.]. At the same time, the digital ecosystem acts as a means of digital transformation [3], the tool without which the implementation of digital transformation is impossible. The ultimate goal of building such an ecosystem model of education is to provide the economy with the necessary specialists today and ensure the technological sovereignty of the state, and hence the welfare of citizens.

2 Materials and Methodology The purpose of the research is a cognitive-discursive analysis of the discourse in the field of education, identification of the main conceptual areas and new concepts reflecting modern domestic and world trends and approaches in education. As the basis for the definition of discourse, we take Arutyunova’s understanding of it as “a coherent text in combination with extra-linguistic, pragmatic, socio-cultural, psychological and other factors; a text taken in the event aspect; speech considered as a purposeful social action, as a component involved in the interaction of people and the mechanisms of their consciousness (cognitive processes)”. Discourse is a speech “immersed in life” [4], that is, knowledge about it, presented at various times by texts, and not only texts, but opinions; knowledge emanating in this study from specialists, teachers, scientists. Discourse is not only the text being analyzed, but perhaps the situation behind it [5], preceding and possible subsequent texts, etc. Discourse is various types of text actualization, considered from the point of view of mental processes and in connection with extra-linguistic factors [6]; the process of generating, processing, transforming and transmitting information organized as texts [7], but also those ones behind which there is a special grammar, a special lexicon, a special semantics—a special world [8]. The definition of the concept of “discourse” through the concept of “speech” indicates the dynamic nature of this phenomenon. The most general definition of discourse is given by V. Z. Demyankov: discourse is a “fragment of text consisting of more than one sentence or an independent part of a sentence”. Often, but not always, it concentrates around some basic concept; it creates a general context describing actors, objects, circumstances, times, actions, etc., determined not so much by the sequence of sentences but by the world common to the creator of the discourse and its interpreter, which is “built” in the course of the discourse unfolding. The initial structure for the discourse has the form of a sequence of elementary propositions interconnected by logical relations of conjunction, disjunction, etc. [9]. In general, as noted by leading linguists, among the works on the problems of discourse, “the interest of cognitive scientists is shifting from the general principles of the

The Concept of Digital Transformation in Educational Discourse

165

abstract language structure to how a specific speaker thinks, builds a speech strategy, manages information known to him when he is speaking and writing” [10], and that the direction of “discourse analysis”, which has formed into an independent linguistic discipline, focuses on a functional approach, i.e. seeks to explain the observed phenomena by studying discourse. The cognitive-discursive approach takes into account cognitive, discursive and pragmatic factors, allows us to identify those cognitive structures that are behind the key or new terminology of the studied discourse that represents them, since the terms are carriers of information, belong to the discourse, and are part of it. Such an approach also allows us to use the full range of necessary background knowledge in understanding all the current conceptual areas of the concept of digital transformation in educational discourse. Thus, the object of the study is digital educational discourse, and the research material is discussions on forums, in chat rooms, in the media, on the pages of scientific articles; the opinions of specialists—scientists and teachers of the Department of Information Technology of the Kaliningrad Technical University—were also taken into account.

3 Results In the study, we use the concept of “educational discourse” in relation to the changes that occur in higher education. In the education system of recent years there have been such trends as globalization (adherence to the Bologna system), universalization (state exams), increased competition in the market of educational services, their large-scale commercialization and, finally, digitalization of the institutions of Russian society, the emergence of new information and communication channels have made the transformation of the education system inevitable. The exchange of knowledge, the ways of transmitting and consuming information have changed significantly. The free access to limitless expanse of knowledge gave rise to an information and educational electronic environment with virtual communication capabilities and various models of online education. It should be noted that new challenges and opportunities are ambiguously perceived in the educational environment. However, the reality is that today we are talking not just about digital discourse, but about the digital version of our whole life, where there is an article from the website of a periodical, and a video conference with colleagues from another city on the Zoom platform. The formation of conditions and the introduction of digital transformation processes into the socio-economic development of countries is one of the main global trends today. “The basis of the digital transformation of society is the higher education system” [11]. The studied discourse reflects contradictory opinions, sometimes negative attitudes towards total digitalization in education, pros and cons, how society perceives and discusses inevitable changes in the educational environment, how it sees the transformation, in particular, of higher education. We analyze fragments of discourses of the studied area and identify the conceptual components of the concept of digital transformation in education. To understand how this concept is structured, we have considered many definitions of its basic element, namely, the concept of digital transformation. Digital transformation is one of the most popular topics of recent years, but a clear definition of this phrase has not yet been given, and over time it is getting more and

166

I. Guseva and E. Pliva

more interpretations. Thus, A. Tarasov gives the following definition: “Digital transformation (digitalization) is a change in the form of business in the conditions of digital reality based on data. Digital transformation means, first of all, new business processes, organizational structures, regulations, new responsibility for data, new role models.” At the same time, he emphasizes that the main process of digital transformation is strategic data management (Data Governance) [12]. In other sources digital transformation (DT or DX) is described as the process of implementing digital technologies by an organization, accompanied by optimization of the control system of the main technological processes. At the same time, its goal is emphasized—digital transformation is designed to accelerate sales and business growth, increase the efficiency of organizations that are not purely commercial (for example, universities and other educational institutions) [13]. TalentTech experts, speaking about digital transformation, emphasize the role of the human factor in this process: “the use of blockchain, artificial intelligence or robots alone does not make your company digital. Technology is part of the mosaic. And its main connecting element is people” [14]. Digital transformation is defined by them as a rethinking of the ways of organizing the work of employees for their effective interaction with customers through the use of modern technologies and data analysis. At the same time, experts emphasize the goal of digitalization—to increase the efficiency of the organization: to spend less, earn more and bypass competitors and former themselves. To achieve this, it is necessary to rely on the development of the potential of employees [14]. The concept of digital transformation in the given fragments of discourses represents the “concept of activity/process” aimed at radical changes in the form of the organization, changing its working methods and management in the direction of optimization, acceleration, efficiency improvement and rethinking of existing approaches. Organizations such as companies, universities or other educational institutions represent the concept of the “object” of the above-mentioned activity. The concept of “tools/methods” of ongoing transformations is represented by such elements as: digital, data, artificial intelligence, robots, Blockchain technology and other modern approaches. The concept of an “actor” is reflected in these definitions by people, stakeholders, because the essence and purpose of the processes taking place in the world is to increase the efficiency of the objects to which they belong, through the development of the potential of workers. The connecting link of all these components are, therefore, people, it is on them that the success of the strategies depends on. The connotation of “responsibility” and the ability to “interact effectively” determines the pragmatic component in the characteristic of “activity”, strengthening the verbal impact on the recipient of information, since discourse, as is known, is a category of pragmatics. The integrated conceptual areas are structured by frames/propositions, which in the discourse are filled with specific roles and properties [15]. A chain of interrelated concepts is drawn based on belonging to a single topic represented by the discourse under study: business/organizations—technologies—people, where it is the human resource that plays a key role. Let’s consider how these same trends are reflected in the discourse of foreign publications.

The Concept of Digital Transformation in Educational Discourse

167

“Digital transformation refers to the process and strategy of using digital technology to drastically change how businesses operate and serve customers” [16]. “The digital transformation of businesses … will require managers responsible for developing supply chain relationships, such as account managers or supply managers, to adopt a boundaryspanning mindset in order to facilitate collaboration, experimentation, and trust across organizational boundaries” [17]. Digital transformation is again a “process”, a “strategy” aimed at cardinal “changes” that are designed to “serve” people. Again, such qualities as “responsible”, “interactions/relationships (collaboration)” are profiled, which are enhanced by the concept of “boundary-spanning mindset/erasing boundaries”. The need for new thinking is emphasized, covering processes that are aimed at the joint implementation of activities and the creation of new knowledge or product by employees of different departments; at communication processes that erase boundaries. The meaning of the concept of “boundary spanning mindset” is considered as “situated” [18], on context based meaning, immersed in actual practice and past experience; on the other hand, such meanings create this experience. The meaning is multiple, flexible and in the light of a cognitive-discursive approach is considered in accordance with local culture and social practice [19]. In the discourse unfolding further additional meanings are built up. On the one hand, we are talking about the formation of a process, which means that organizations face certain difficulties and risks. On the other hand, digital transformation creates unique challenges and opportunities for a holistic change in IT, business and society. What does the concept of digitalization look like to the participants of the educational discourse? Qiao Lanjuui understands the digital transformation of the educational process as a creative system of relations between its participants and stakeholders, formed as a result of the development and introduction of modern information technologies and corresponding communication devices into the learning process, the end result of which will be the creation of a model of a “digital university” as a set of regulatory requirements for the digital environment of the university [20]. Minina highlights the following aspects related to the digitalization of higher education: the introduction of digital tools and technologies into traditional educational programs and academic disciplines; the development of online education; the creation of a virtual (digital) educational environment; a change in the approach to the management of educational organizations. At the same time, she notes that these trends are interrelated, but at the same time, each of them has its own specifics [21]. The authors of scientific papers also note the need for the formation of an integrated environment, retraining and advanced training of teaching staff in relation to digital formats of the learning process, since the training system is the main source of digital development of the country. Speaking about the process of digital transformation in the educational discourse and about the criteria for the development of this concept in the educational system, the researchers also note such a concept as the digital maturity of the university, which is understood as a desired goal, the ultimate point to be achieved. The authors note that the Russian scientific citation index contains 480 publications on the topic of digital maturity and only 15 of them are devoted to the digital maturity of a university or higher

168

I. Guseva and E. Pliva

education, which indicates that the study of this concept in the educational context has not been yet sufficiently developed and is being set up [22]. In addition, attempts have been made to measure digital transformation, in particular, a number of researchers propose a methodology for calculating the digitalization index or digital maturity, and 7 indices have been formalized and justified [11]. Does this mean that digitalization in education means only the transfer of communication to a digital format? We consider the process to be much broader, it is regarded in scientific publications as a paradigm shift in education and, as can be seen from the analysis of the definitions of digital transformation above, they have many points of contact: training and development of the potential of personnel, the relationship of processes and communications, the development and implementation of technologies, fundamental changes, dynamism, etc. Andrey Komissarov shares the same opinion, emphasizing that the transfer to digital format is, first of all, the transition to a new culture—the culture of organizing activities and of course, the transition to a new methodology. The methodology of digitalization of education is based on several key things: this is pedagogical design, the construction of individual trajectories and personalization in general, as well as the collection and analysis of confirmed educational results [23]. In discussions at forums dedicated to education, the following opinions and judgments are heard. With the advent of digitalization, the paradigm of education in our country should also change, it should become more practice-oriented [24]. It is noteworthy that the researchers themselves, speaking about the traditional model of education and the new one, liken the first to a conveyor belt, an industrial enterprise, emphasizing the flow, mechanical and industrial nature of the process as opposed to personalized and individual learning trajectories [25]. The CEO of the company Maximum Education Mikhail Myagkov also links digital transformation with individualization of learning: “We believe in big data. We believe that the data can allow us to create systems that increase student involvement in the process” [24]. For successful digitalization in education, it is worth thinking how to take the best of both worlds—technology and the education system we have now [24]. It is noteworthy that the other side of the digitalization process doesn’t escape the attention of researchers. There are concerns that digital transformation can cause some issues: “The problem here is that first you need to train teachers how to rebuild training, taking into account the requirements dictated by the world and the state” [24]; “Although digital technologies in higher education have the potential to introduce new and more compound learning activities, they may alienate teachers and students from the learning process” [26]. All discussions emphasize the comprehensive nature of digital transformation, the fact that this process is not limited to the transition to a digital format. In addition, such features of this concept as consistency and interconnectivity with all elements are highlighted through all the analyzed statements, and this allows us to talk about the ecosystem nature of the modern approach to education. At the same time, the ecosystem itself is a stimulating environment for cognition and development, giving the opportunity to develop in the chosen direction “without

The Concept of Digital Transformation in Educational Discourse

169

external will or expert opinion.” So, researchers liken being in such a system to a “digital exoskeleton”, a device for enhancing capabilities that “will help to learn on your own and not to be led by a teacher” [23]. Exploring the concept of digital transformation and its systemic nature, it is impossible not to mention a number of other concepts that are inextricably linked with it and make up its conceptual field. Within the framework of this work, we will list only some of them (Fig. 1).

Fig. 1. Conceptual field of digital transformation.

A digital footprint is a huge and unstructured array of data that we leave in the global information network from any of our actions and which can carry extremely useful information. In education, a digital footprint is a student’s written work, notes, tests, online courses, photographs, etc. [27]. Experts consider digital footprint a powerful tool that can be used to accompany student learning, to measure the effectiveness of the efforts made by the student and his involvement in the learning process, as well as to create an adaptive educational environment and improve the quality of learning. An analytics system of learning behavior is being developed, based on the analysis of digital footprints [28]. Experts are sure that the future of online education is in the use of digital footprints and patterns. This will allow creating individual educational trajectories and personalizing the educational process, which will help make graduates more popular and successful. “Online education, unlike traditional education, allows you to track the digital footprints of students… And then the fun begins. If we can track student’s learning behavior, analyze it, highlight behavior patterns, predict, create an adaptive learning system and constantly improve it based on feedback, then such education is much more productive than conventional one. The use of big data based on captured digital footprints allows you

170

I. Guseva and E. Pliva

to move on to personalized education, which most often relies on artificial intelligence” [28]. Pavel Luksha links the concept of a digital footprint with a digital twin of a person, by which he understands a single profile that captures information about school education, university, courses, work and accompanies it throughout life and describes the entire trajectory of a student’s life [25]. It is the digital model of a person, or the digital twin, that performs the function that previously was performed by diplomas and certificates [25]. Some experts associate digital model, digital twin of a person, digital twin of a university and economic benefit: “Now one of the main ideas is to implement a digital twin of a university. The system will know exactly how many people and in what audience is at any given time. Thus, it will be possible to calculate the full logistics within the institution. And based on the electronic model, draw up a schedule that would allow more optimal use of the available premises. It can be not only a university, but any other organization” [29]. The concept of digital transformation is inextricably linked with the concept of lifelong education. The principle of continuous education throughout life assumes that the student is highly motivated, clear on his goals and knows how to learn. It is clear that new approaches focused on the development of such skills are born in the education system itself, and then supported and scaled up by the state. At the same time, as the researchers note, lifelong education aims to move away from the industrial conveyor model to the concept of a continuous educational process, in which there are “many streams, flexible networks” [25]. “In order to remain in demand in a rapidly changing world, a modern specialist must be motivated for constant self-realization and a willingness to learn continuously” [30]. To understand modern students, researchers use the concept of digital natives, which is introduced by M. Prensky, to whom he opposes the participants in the digital transformation of older ages—digital immigrants, those who were born before the arrival and rapid dissemination of digital technologies [31]. Prensky argues that digital natives have an innate knowledge of digital technologies. Are more tech-savvy and better at multitasking than older generations. They are accustomed to sudden changes in the speed of information perception, to the interactivity of gadgets, to their own activity in social networks, to the speed in the world of video games [32]. Although this concept has both many opponents and supporters and some researches call into question “the superpowers” of digital natives [33], the very fact of referring to the idea that what for some is a digital transition, transformation and requires certain efforts, for others is an innate reality, is noteworthy.

4 Conclusion Thus, as a result of the analysis of definitions and digital educational discourse, the concept of digital transformation appears as a complex multifaceted concept with a number of characteristics: a process, the final point of which is the achievement of the digital maturity of a university/educational organization, characterized by a systematic, comprehensive nature of changes. The main goal of digitalization is to increase the efficiency

The Concept of Digital Transformation in Educational Discourse

171

of the organization of the system in which it is implemented, the individualization of education, and the formation of personalized educational trajectories. In addition, digitalization is not confined to just converting content into the digital format, it is a change in the entire educational paradigm. Digitalization is an essential condition for building an ecosystem model of education. At the same time, it is emphasized that the level of readiness of universities for digital transformation is different and the pace of its implementation depends on the capabilities of a particular educational institution. There are also alarming trends associated with digital transformation in education and the urgent need to take into account all the positive aspects that exist in traditional education. The concept of digital transformation is inextricably linked with a number of other concepts that form its conceptual field, such as the digital footprint, digital twin, digital natives/immigrants, as well as such global concept as lifelong education.

References 1. Educational ecosystems: emerging practices for the future of education. https://www.sko lkovo.ru/researches/obrazovatelnye-ekosistemy-voznikayushaya-praktika-dlya-budushegoobrazovaniya. Last accessed 5 Aug 2022 2. Suleymankadieva, A.E., Petrov, M.A., Aleksandrov, I.N.: Digital educational ecosystem: genesis and prospects for the development of online education. Issues Innov. Econ. 11(3), 1273–1288 (2021). https://doi.org/10.18334/vinec.11.3.113470 3. Neborsky, E.V.: Digital ecosystem as an instrument of digital transformation of the university. World of Science. Pedagog. Psychol. 4(9), (2021). https://mir-nauki.com/PDF/02PDMN421. pdf. https://doi.org/10.15862/02PDMN421 (in Russian) 4. Arutyunova, N.D.: Discourse. In: Linguistic Encyclopedic Dictionary, p. 137. Soviet Encyclopedia, Moscow (1990) 5. Gee, J.P., Green, J.L.: Discourse analysis, learning, and social practice: a methodological study. Rev. Res. Educ. 23, 119–169 (1998). https://doi.org/10.3102/0091732x023001119 6. Big Encyclopedic Dictionary. Soviet Encyclopedia, Moscow (1998) 7. Alefirenko, N.F.: Discursive-cognitive origins of secondary sign formation. In: Cognitive Semantics/Materials of Scientific Conference, pp. 148–150. Tambov (2002) 8. Stepanov, Y.S.: Language and Method. Towards Modern Theory of Language, p. 676. Moscow (1998) 9. Demyankov, V.Z.: English-Russian terms in applied linguistics and automatic text processing. Methods of text analysis. In: All-Union Translation Center. Notebooks of New Terms, vol. 39, p. 7 (1982) 10. Rakhilina, E.V.: Cognitive Analysis of Subject Names: Semantics and Compatibility, p. 378. Rus. Dictionaries, Moscow (2000) 11. Plotnikiva, E.V., Efremova, M.O.: Diagnostics of trends in digitalization in the higher education system on the example of leading Russian universities. Kant 2(31), (2019) 12. What is digitalization? https://www.e-xecutive.ru/management/itforbusiness/1989667-chtotakoe-tsifrovizatsiya. Last accessed 5 Aug 2022 13. Digital transformation. https://ru.wikipedia.org/wiki/cifpova_tpancfopmaci. Last accessed 5 Aug 2022 14. Digital business transformation. https://talenttech.ru/blog/digital-transformation. Last accessed 5 Aug 2022 15. Iriskhanova, O.K.: On the theory of conceptual integration. Izv. AN. Ser. Lit. Lang. 60(3), 44–49 (2001)

172

I. Guseva and E. Pliva

16. Digital transformation: what it is and why it matters. https://www.sas.com/ru_ru/insights/ data-management/digital-transformation.html. Last accessed 5 Aug 2022 17. Saénz, M.J., Revilla, E., Borrella, I.: Digital transformation is changing supply chain relationships. https://hbr.org/2022/07/digital-transformation-is-changing-supply-chain-relations hips. Last accessed 7 July 2022 18. Gee, J.P.: An Introduction to Discourse Analysis: Theory and Method, pp. 40–42. Routledge, London and New York (1999) 19. Guseva, I.G.: Cognitive-discursive analysis of intersectoral ecological terminology in the field of fisheries (on the material of the English language). Dis. for the degree of candidate of philological sciences. K-d (2004) 20. Lanju, Q.: Research of digital transformation of Russian regional universities under modern conditions. Pedagog. Educ. Russ. (3), 59–66 (2020) 21. Minina, V.N.: Digitalization of higher education and its social results. Bull. St. Petersburg Univ. Sociol. 13(1), 84–101 (2020). https://doi.org/10.21638/spbu12.2020.106 22. Bannykh, G.A., Kostina, S.N.: Conceptualization of the concept of digital maturity of a university in the context of digital transformation of higher education. Bull. Maikop State Technol. Univ. 1 (2022). https://cyberleninka.ru/article/n/kontseptualizatsiya-ponyatiya-tsifro voy-zrelosti-universiteta-v-kontekste-tsifrovoy-transformatsii-vysshego-obrazovaniya. Last accessed 6 Aug 2022 23. “Island” of freedom: digital exoskeletons of tourists and Robinsons. https://inscience.news/ ru/article/discussion/ostrov-svobody. Last accessed 6 Aug 2022 24. Forum.Digital Education 2021—online forum on digitalization of education. https://forum. digital/education. Last accessed 6 Aug 2022 25. Why the future of education is in ecosystems. https://trends.rbc.ru/trends/education/6027f5 6f9a794723de4d1b34. Last accessed 6 Aug 2022 26. The digital transformation of higher education teaching: four pedagogical prescriptions to move active learning pedagogy forward. https://www.frontiersin.org/articles/10.3389/feduc. 2021.784701/full. Last accessed 6 Aug 2022 27. Mantulenko, V.V.: Prospects of digital footprints use in the higher education. In: Teacher 21st Century, № 3-1 (2020) 28. “Digital footprint”: how online education is changing in Russia. https://tass.ru/obschestvo/ 13257967. Last accessed 6 Aug 2022 29. Digital models and twins: why they are the future and where they are taught to work with them. https://vc.ru/education/294400-cifrovye-modeli-i-dvoyniki-pochemu-za-nimi-budush chee-i-gde-uchat-s-nimi-rabotat. Last accessed 6 Aug 2022 30. “Digital” education: let no one be left behind. https://www.kommersant.ru/doc/4171063. Last accessed 7 Aug 2022 31. Prensky, M.: Our brains extended. https://www.ascd.org/el/articles/our-brains-extended. Last accessed 6 July 2022 32. Ignatova, N.Y.: Education in the Digital Age, p. 128. URFU, Nizhniy Tagil (2017) 33. Ershova, R.V.: Digital generation: between myth and reality. Russ. J. Philos. Sci. 62(2), 96–108 (2019) (in Russ.). https://doi.org/10.30727/0235-1188-2019-62-2-96-108

Risk Management in the System of Economic Security of the Enterprise Tatiana Tarasova1(B)

and Artem Krivtsov2

1 Samara State University of Economics, Samara, Russia

[email protected] 2 Moscow State Institute of International Relations, Moscow, Russia

Abstract. In the current conditions of instability of the domestic economy, Russian enterprises operate in conditions of risk and uncertainty, which can adversely affect their financial condition and performance and further lead to bankruptcy. In this regard, measures to identify and neutralize possible problems and risks, as well as the search for new ways to ensure the economic security of the enterprise, become relevant for business entities. After all, timely and high-quality diagnostics of problems of ensuring economic security, identification and systematization of risks and threats make it possible to avoid both material losses and prevent the bankruptcy of an enterprise. The study examines the risks in the context of each business process of the enterprise and reveals the risk assessment technology. Then the principles that must be observed in the settlement of economic security in the company are considered. Strict adherence to the basic principles will allow the company to conduct continuous activities to control and localize risks and threats to the economic security of the enterprise, as well as effectively eliminate possible negative consequences, ensuring the protection of the economic interests of the enterprise. One of the tools to prevent the consequences and minimize the occurrence of risks is the use of a risk map. However, using only a risk map is not enough to create a full-fledged system to counter possible threats to the economic security of an enterprise. Therefore, for the enterprise not to be exposed to threats, risk management measures were proposed as recommendations. Keywords: Economic security · Risk management system · Risk · Threat

1 Introduction The stable development of any enterprise depends on the efficiency of the economic security system, which provides protection against existing and potential threats. Large enterprises, while carrying out their activities, are constantly exposed to risks. Risks are often repeated, despite the “novelty” of their occurrence. Most likely, the risks that enterprises are exposed to have already right now arisen before, they just showed themselves in a new way. That is why in economic practice there are already certain ways to solve these problems. Of course, there are new unknown risks. They are considered the most dangerous, as they cannot be identified immediately. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 173–188, 2023. https://doi.org/10.1007/978-3-031-34329-2_18

174

T. Tarasova and A. Krivtsov

Unknown risks are dangerous because there is no clear algorithm of actions that would allow you to instantly respond to this situation. A manager who is trying to protect his company from the occurrence of negative factors should make sure that the company has an additional resource that will allow it to respond to incoming threats in a timely manner. It is also necessary to ensure that the company has specialists whose activities will be aimed at developing recommendations for risk neutralization and preparing measures to manage them. In today’s world, this problem is of relevance. The development of the system of economic security of the enterprise and the organization of the system of internal control and risk management are dealt with by scientists in all countries of the world. Among the significant works on which the authors of this study relied, the authors of the following scientific publications can be distinguished Altman [1], Baldwin [2], Baturina [3], Nanto [4], Novikov [6], Onogda and Yarkina [7], Orlov [8], Osipova [9], Zaripova and Kovalenko [10].

2 Method Over the past few decades, a number of works have been published on ensuring the economic security of enterprises designing facilities for the chemical, oil refining and petrochemical industries, but a detailed analysis of the assessment of economic risks and their impact on the economic security of an enterprise has not been carried out. Therefore, this research topic needs further study. The purpose of the study is to comprehensively consider theoretical issues and practical problems of minimizing economic risks while ensuring the economic security of Bashgiproneftekhim LLC. Achieving this goal led to the formulation and the need to solve the following tasks: – identify risks and their role in ensuring the economic security of an economic entity. – consider modern tools for managing risks and threats of an economic entity. – to analyze economic risks and threats to economic security of Bashgiproneftekhim LLC; – offer recommendations for improving the economic security system aimed at managing the risks of Bashgiproneftekhim LLC. The theoretical significance lies in the increment of scientific material on the study of diagnostics and assessment of economic risks that affect the economic security of the enterprise. The practical significance is characterized by the search for optimal ways to improve the efficiency of enterprises designing facilities for the chemical, oil refining and petrochemical industries. Research methods. To solve the tasks set, a set of interrelated research methods was used: generalization and systematization of data; analysis and synthesis; comparative and comparative research methods.

Risk Management in the System of Economic Security of the Enterprise

175

3 Discussion The study is conducted according to the data of Bashgiproneftekhim LLC, which occupies a leading position in the market in the field of designing facilities for the chemical, oil refining and petrochemical industries. This is a leading company in the field of engineering and design work, which successfully uses its many years of experience in achieving its goals. Bashgiproneftekhim LLC has been cooperating with Russian and foreign partners for many years, and constantly introduces advanced technologies from more developed countries into its work, which allows a comprehensive and professional approach to solving any design problems of its customers, using modern approaches. Initially, the institute was included in the category of state organizations and was called the State Unitary Enterprise “Bashkir State Institute for the Design of Oil Refining and Petrochemical Industry Enterprises” of the Republic of Bashkortostan. In 2017, there was a change in the form of ownership, as a result of which the name of the organization was changed to Bashgiproneftekhim LLC. Today, the company employs more than 400 highly qualified employees who daily contribute to the development of the enterprise. Main activities: • provision of well drilling services; • maintenance of machinery and equipment; • provision of design and engineering services for oil refining, gas processing and petrochemical industries; • control over the construction of oil refineries, as well as the implementation of field supervision over the construction of oil refining and petrochemical facilities; • development of design estimates and operational documentation. It is important to understand that the provision of these services must comply not only with the wishes of customers, but also with the requirements of technical regulations. But do not forget that the activity of the enterprise is fraught with risk, respectively, during the trial operation of engineering work, various difficulties may arise in the form of accidents and other force majeure circumstances, which, in turn, may adversely affect the company’s reputation. In order to timely identify the reasons that contribute to the infliction of material, moral damage at the enterprise, in 2019, Bashgiproneftekhim LLC created a department of economic security, which implements a whole range of measures aimed at the qualitative implementation of protecting the interests of the enterprise from external and internal threats. Ensuring the security of the enterprise LLC “Bashgipronefiekhim” is a continuous process of creating favorable conditions for activity, under which the interests of the subject are realized and the goals set by it are realized. The economic security system at Bashgipronefiekhim LLC was created based on the following principles: 1. The principle of legality. 2. The principle of regular risk assessment.

176

3. 4. 5. 6. 7.

T. Tarasova and A. Krivtsov

The principle of economic feasibility. The principle of due diligence. The principle of continuity of education. The principle of avoiding conflicts of interest. Monitoring and control.

Legality. Compliance with this principle indicates that the enterprise LLC “Bashgipronefiekhim” implements its activities in accordance with the legislation of the Russian Federation. In accordance with this, the company’s actions during its activities do not contradict the applicable law, in order to avoid reputational or financial losses. Regular risk assessment. The company annually prepares reports on risks arising in production processes. In this report, risk factors are prescribed, the probability of an event occurring is assessed, and a responsible executor is appointed. Economic expediency. To comply with this principle, Bashgiproneftekhim LLC pays special attention to high-probability risks with significant damage. Since the enterprise has such risks, it is more profitable for the organization’s management to create an economic security service at the enterprise than to accept or try to avoid these risks. Due diligence. The company cares about its reputation, which is why each counterparty or potential candidate is checked by the security service, after which the management of the company decides on further cooperation or, conversely, on termination of relations, if negative factors are identified. Compliance with this principle helps the company protect itself from unreliable counterparties and unscrupulous employees. Continuity of learning. This principle is observed at the enterprise in full measure, it can be seen from the fact that in the company “Bashgiproneftekhim” LLC, to maintain the high qualification of employees, more than 2 million rubles are annually spent on training. Prevention of conflict of interest. Bashgiproneftekhim LLC is trying to do everything possible so that employees within the enterprise, performing their duties, put the interests of the company above their personal interests. The employees of the enterprise are familiar with local regulatory documents in the field of conflict-of-interest settlement, and are also informed about the consequences, if any. Monitoring and control. Monitoring and control in the company is carried out using the internal control system. The application of this principle in the work allows you to protect the enterprise from corporate fraud. The Economic Security Service constantly monitors the expenses of the enterprise for economic feasibility. Particular attention is paid to high-risk spending transactions, such as entertainment expenses, sponsorship or charitable activities, marketing expenses, and others. The procedure for the formation and execution of payments is regulated by the relevant regulatory documents of Bashgiproneftekhim LLC. The stable functioning of the business is ensured by complying with the conditions set for the economic security service: – timely identification of possible threats and development of measures to minimize them; – ensuring the required level of safety for all business processes; – mandatory regulation of ongoing procedures by the department of economic security;

Risk Management in the System of Economic Security of the Enterprise

177

– Ensuring that the company complies with the principles and requirements of the accounting policy; – compliance with the legislation of the Russian Federation and other normative legal acts by the department of economic security, in the implementation of its activities; – monitoring and analysis of identified incidents by the economic security department. Summing up the above, it is worth noting that at the enterprise of Bashgiproneftekhim LLC, as a settlement of economic security, not only those principles that have proven their effectiveness in practice are applied, but also completely advanced methods that have proven themselves in foreign countries but have not yet been used on the territory of the Russian Federation. Bashgiproneftekhim LLC is interested in making the developed system of economic security as effective as possible for the enterprise. Thus, the application of these principles will allow the company to conduct continuous activities to control and localize risks and threats to the economic security of the enterprise, as well as effectively eliminate possible negative consequences, ensuring the protection of the economic interests of the enterprise. The activity of any commercial enterprise is accompanied by various risks and threats that manifest themselves at certain stages of doing business. It is for this purpose that large enterprises create an economic security department, the purpose of which is to promptly identify and prevent possible risks and threats that can have a significant impact on the financial performance of an enterprise. Risks can be presented as an economic category, which in its final form is characterized by the possibility of loss of income, a violation of the standard functioning of the enterprise, a decrease in the economic potential of the company because of changes in the natural nature and conditions of economic activity. It is impossible to avoid all risks, but it is possible to create a control method that will prevent the onset of negative consequences. The control of all risk factors is entrusted to the economic security department, which must organize its work in such a way that all potential risk factors are considered and, if possible, eliminated. It is unacceptable to ignore emerging risks, it is necessary to treat them most carefully, since even an insignificant risk can become a potential threat to the enterprise. The risks arising from the economic activities of the enterprise are most often manageable, but for this to be possible, it is necessary to determine the specific properties of the occurrence of the risk. In practice, special attention is paid to the four main properties of risk, namely: • • • •

probability of risk occurrence; strength of influence; manageability; interconnectedness.

Probability of occurrence. This property reminds us that a risk is an event whose occurrence cannot be accurately measured. The likelihood of a risk occurring can range from extremely unlikely to highly likely. Using this indicator, the enterprise develops a plan of necessary measures to minimize the risk, depending on the likelihood of its occurrence.

178

T. Tarasova and A. Krivtsov

The strength of the influence of risk is a property that must be considered when making strategic decisions. The consequences, depending on the degree of influence, can be either insignificant for the organization or have catastrophic consequences that can lead to the suspension of the enterprise, and in the future—to liquidation. Management of risks. As a rule, each risk that arises in the enterprise must go through the stages of analysis. For a risk to be managed, it must first be identified, the likelihood of its occurrence, the magnitude of the risk and the degree of its impact on and the financial condition of the company, and only then develop risk management measures. It is important to understand that there are risks that cannot be managed by the organization, as a rule, they are classified as force majeure. The risks of the enterprise that can be managed are handled by the risk manager. The main task of the risk management unit is to identify the causes of a possible risk, as well as the subsequent development of procedures and methods for its neutralization. Interconnectedness. The property of the interconnectedness of risks is due to the domino effect. As a result of this effect, the initial risk is more likely to be the cause of another, future risk. Thus, a chain of risks is formed, which is accompanied by serious consequences because of its randomness. Let’s consider the risks of Bashgiproneftekhim LLC identified in the design process. The risks were considered in the context of each business process of the enterprise, since each process has an individual set of risks, which is characteristic only for it. That is why it is necessary to analyze risks for each business process separately. 1. Process “Management of documented information”. With this business process, there is a risk in the form of a delay in the receipt of the necessary documentation. This risk refers to the production and organizational. The consequences of this risk may lead to the fact that non-updated information will be used during project activities, there is a risk of receiving comments from state and non-state examination bodies, and this risk may also lead to an increase in the cost of design. 2. Process “Participation in the tender”. In this business process, there is a risk of a situation where the procurement participant cannot take part in the procurement procedure since there is no registration at a particular site or payment is not made in accordance with the established tariffs. 3. When implementing this business process, another production risk may arise—this is the loss of tender products. This risk option is possible if the company claims an overestimated cost of its work. Negative consequences for the company will manifest themselves in the form of a lack of new contracts and a reduction in jobs due to a decrease in the volume of work performed. 4. Process “Development of project documentation”. The process of developing project documentation is quite laborious, because of this, many risks can arise. Risks can be completely different, ranging from changes in customer requirements to the absence of regulatory documents regulating the scope of changes at all stages of design. The appearance of these risks is since the customer, in the design process, increases the amount of work, therefore, there is a change in contractual terms, and the cost

Risk Management in the System of Economic Security of the Enterprise

179

of design increases. The consequences of these risks are long-term coordination of changes and schedule adjustments. After analyzing the risks that may arise because of the economic activity of Bashgiproneftekhim LLC, we can conclude about the possible consequences for the enterprise in the event of these risks. For example, the risk of failure to meet the deadlines for the performance of work may lead to a violation by the enterprise of its obligations. This will affect the increase in the cost of design, which will negatively affect the financial position of the enterprise. The above risks may affect the key performance indicators of the company, which in the future will provoke a decrease in the company’s rating, loss of reputation and competitiveness. A long absence of orders for design work can lead the company to a critical financial condition. Enterprise risks must be assessed in accordance with the national standard of the Russian Federation GOST R 58771–2019 “Risk management. Risk Assessment Technologies” [5]. The standard prescribes methods that can be used in risk assessment. This document details the ways in which risks can be sorted into certain categories, thereby identifying risks. Identified risks are subject to further assessment. Each risk is assessed on a five-point scale depending on the likelihood of the risk and the significance of the consequences, presented in Tables 1 and 2. Table 1. Point assessment of the degree of probability of risk events Risk score Probability of occurrence

Characteristic

1

The potential for risk is extremely low This event can occur in case of emergency

2

Low chance of occurrence

The probability of occurrence of risk events is extremely small

3

The risk cannot be ruled out

There is a possibility of a risk event, it is necessary to think in advance about ways to neutralize the risk

4

Quite possibly

There is a high probability of occurrence of risk, it is necessary to take measures

5

The onset of risk is inevitable

The risk is inevitable, it is necessary to prepare a work plan aimed at minimizing the negative consequences

After the risk is identified and analyzed, the next step is risk assessment, the essence of which is to determine how significant this risk is for the effective operation of the enterprise. After that, certain conclusions are formed, after which it will be clear in what way it is necessary to handle risks to avoid consequences, or to focus on carrying out activities that will be aimed at reducing a certain risk.

180

T. Tarasova and A. Krivtsov

The risk assessment technology at the enterprise takes place in several stages: 1. 2. 3. 4. 5. 6. 7. 8.

Risk identification. Identification of factors influencing the occurrence of risk. Choice of methods of risk management (conducting studies of selected methods). Assessment of the probability of risk occurrence. Assessment of the significance of the risk. Analysis of possible consequences in the event of a risk. Development of measures to minimize risks. Monitoring.

Table 2. Scoring the significance of consequences Significance of consequences Description

Characteristic

1

Insignificant This risk does not threaten the company’s activities at all

2

Permissible

When a risk occurs, there may be minor consequences that can be quickly prevented

3

Meaningful

The risk is accompanied by negative consequences in the context of the business process in which it was identified. But at the same time, the consequences of such a risk will slightly affect the financial condition of the company, since the elimination of such risks does not involve serious costs

4

Critical

The consequences of this risk can significantly affect the stable operation of the company, which can ultimately lead to serious damage to the enterprise. Significant funding will be required to address the consequences of such a risk

5

Catastrophic The occurrence of such a risk can cause catastrophic damage to any enterprise, ranging from huge material costs to eliminate the results of the risk, ending with the failure to fulfill the strategic goals set by the enterprise and the inability to conduct further business activities

During its activities, Bashgiproneftekhim LLC, an enterprise, faces both quite “natural” risks for this industry, and completely new, unexplored risks. The simplicity of “natural” risks is that the risk is understood. In this case, the risk assessment technology is greatly simplified. There is no need to determine the factors that influenced the occurrence of this risk, it is not necessary to choose risk management methods, and it is also not necessary to analyze the possible consequences (in the event of this risk occurring). Such risks usually do not pose a serious threat to the enterprise since

Risk Management in the System of Economic Security of the Enterprise

181

they have been investigated earlier. As a rule, such risks are specified in the accounting Risk Management Policy. Internal audit in the company is carried out in accordance with the quality management system. In Bashgiproneftekhim LLC since 2002, the quality management system has been effectively functioning, which is confirmed by annual supervisory audits for certification of the quality management system (QMS). At the enterprise LLC Bashgiproneftekhim, the Risk Management Policy contains information on the main risks to which the enterprise is exposed, as well as technologies and methods for assessing risks. When a company during its professional activities faces previously unexplored risks, it becomes difficult to assess this risk. In this case, it is best to stick to standard technology, consistently following all steps of the risk assessment process. Risk identification. It is the process of identifying potential risks for the enterprise. It is carried out with the help of a detailed analysis of all business processes of the enterprise. Once the risk has been identified and recognized, you can proceed to the second stage. Identification of factors influencing the occurrence of risk. To determine the possible factors that could provoke the emergence of a particular risk, it is necessary to analyze the external and internal environment of the enterprise. Compliance with this stage will allow the company to track the efficiency of work within the enterprise, as well as consider adverse environmental factors that have a direct impact on the financial condition of the organization. Choice of risk management methods. Each company individually develops risk management methods, depending on the type and nature of the risks. The risk management policy is considered one of the most effective methods to prevent possible threats and their consequences. As a rule, such a Policy is developed based on the company’s development strategy, in compliance with the law. Assessment of the probability of risk occurrence. When determining the likelihood of a negative event for the enterprise, acting as a risk, a team consisting of experts must be familiar with the problems existing in the enterprise. One expert can also assess the probability of a risk occurring, based on professional judgment, but for this he needs to provide a list of project risks already available at the enterprise. The risk assessment methodology is chosen by the enterprise individually. Assessment of the significance of the risk. To determine how much the occurrence of a particular risk will affect the financial condition of the company, each risk is subjected to an assessment procedure. The assessment is carried out as follows: each identified risk is assigned a point or verbal assessment, the risk is assessed in accordance with the scale. Next, the risks are ranked, i.e., risks are ranked in order from minor risks to major risks. Analysis of possible consequences in the event of a risk. If a risk with a high probability of its occurrence is identified, it is necessary to analyze the possible consequences if it occurs. During this stage, it is necessary to consider the preliminary results of the risk assessment and possible indirect consequences. This procedure is subject to all types of consequences that were identified in the previous stages of risk assessment technology.

182

T. Tarasova and A. Krivtsov

Development of measures to minimize risks. As a rule, when the risk is considered at this stage, it becomes clear to the company’s management what the probability of occurrence of this risk is, what consequences the identified risk will have for the enterprise, etc. Accordingly, it is already possible to analyze whether the enterprise has the technical and material capabilities to eliminate the risk, whether it is possible to reduce this risk, or whether it is necessary to transfer the risk to outsourcing. But in practice there are cases when enterprises try to keep the risk. This only happens when the risk is controlled. Saving the risk is possible only when the risk is insignificant for the enterprise, therefore it is sufficient to carry out periodic monitoring without affecting the risk. The development of measures is the preparation of specialized procedures aimed at eliminating or minimizing risks, depending on the type of risk and the nature of the consequences. The procedure for developing measures to minimize risks consists of the following steps: – a work plan for the quality management system is being prepared, in which measures are prescribed to minimize risks; – responsible executors are appointed; – set the timing of events; – generated reports are signed by the company’s management; – the approved plan is transferred to employees for execution. Monitoring. It acts as the final stage of the risk assessment technology, which involves monitoring the implementation of the proposed measures aimed at preventing risks and threats. With the help of monitoring, it is possible to track the effectiveness of the measures taken, and in the absence of the proper result, it will be possible to promptly correct the ineffective measures. Thus, it is very important to pay due attention to potential risks, because the initial judgment about the insignificance of a particular risk can be false, and as a result, the risk can pose a huge danger to the enterprise. That is why it is very important to track the nature of the occurrence, the cause of occurrence and the degree of influence of each risk on the financial condition of the enterprise. The mechanism of operation of the enterprise should be built in such a way that the security of the enterprise remains at a stable level. The level of economic security of Bashgiproneftekhim LLC is assessed using a methodology based on a comprehensive assessment of economic risks. This technique has several advantages and disadvantages but is the most universal way to assess the level of economic security of an enterprise.

4 Conclusion For the enterprise of Bashgiproneftekhim LLC, the greatest threat is posed by production and organizational risks, which may lead to non-fulfillment of planned work, and in the future, may have a negative impact on the reputation of the organization. To manage risks and analyze the impact of risk on the target indicators of the enterprise, it is important to correctly assess the possible damage and the likelihood of a negative event. The introduction of a risk map at the enterprise of Bashgiprneftekhim LLC (Table 3) will make it possible to clearly demonstrate not only the probability of occurrence of

Risk Management in the System of Economic Security of the Enterprise

183

risk events, but also the criticality of their impact on various aspects of the enterprise’s activities. Risks that have a high probability of occurrence or a significant impact on the financial stability of the enterprise are referred to as the “red zone”. The “yellow zone” includes risks that can lead to significant changes in the economic condition of the company or are characterized by a relative probability of occurrence. The “Green Zone” consists of risks that are not capable of seriously affecting the financial and economic activities of the enterprise, or risks with a low probability of occurrence. The risk map is one of the tools to prevent the consequences and minimize the occurrence of risks, but not the only one. Using only a risk map is not enough to create a full-fledged system to counter possible threats to the economic security of an enterprise. For the enterprise not to be exposed to threats, it is necessary to carry out risk management measures in a timely manner. Table 3. Risk map of Bashgiprneftekhim LLC Description of the risk

Loss of tender products The occurrence of significant changes in the design process Lack of qualified specialists Breakdown of contractual terms with the main customer Premature equipment failure Unfair intra-industry competition Loss of goodwill

Probability of risk occurrence UnProbVery likely ably likely +

Degree of danger of losses Is not dangerous

Dangero us

+

+

+

+

+ +

+

+

+

+

+

+

Permissible

+

Below is Table 4, which reflects recommendations for neutralizing possible risks and threats. If the company has a goal—to achieve a certain effect, then it must be considered that any innovations can both positively affect the result and negatively. Before implementing these recommendations, it is necessary to evaluate the effectiveness and usefulness of this program of measures to neutralize threats to economic security. The recommendations discussed in Table 4 will become much more effective if each activity aimed at neutralizing the risks and threats of Bashgipronefiekhim LLC is held by a responsible person who will be responsible for monitoring the implementation of activities.

184

T. Tarasova and A. Krivtsov

Table 4. Recommendations for neutralizing risks and threats for Bashgiproneftekhim LLC Name of risk/threat

Measures applied

Loss of tender products

• Analyze the closest competitors in the region, monitor the declared cost of competitors for the performance of work • Determine on which electronic trading platform the main customers of the company participate and update prequalification data in a timely manner

The occurrence of significant changes in the design process

• Conclude an additional agreement to the contract (which will spell out the conditions in the event of a change in the original contract) • Monitor changes in regulatory documents and legislative acts

Lack of qualified specialists

• Work with specialized universities • Invite senior students to the enterprise (for practical training) • Once every two years, carry out staff appraisal

Breakdown of contractual terms with the main customer

• It is necessary to track intermediate results during the design process • Control the performance of the work of the subcontractor

Premature equipment failure

• Apply a preventive maintenance strategy • Regularly update the regulatory documents used to calculate the repair schedule

Unfair intra-industry competition

• Conduct an analysis of new market participants providing similar services • Apply a SWOT-analysis that will identify strengths and weaknesses to determine the organization’s prospects aimed at increasing its competitiveness

Loss of goodwill

• Regularly assess the most likely reputational risks to determine the degree of impact on the company • Create a public relations department (PR department) at the enterprise

Let us consider how expedient the application of these measures is in relation to each risk and threat that arises at the LLC Bashgiproneftekhim enterprise. Loss of tender products. In the Republic of Bashkortostan, seven companies with OKVED Code 71.1224, engaged in engineering and technical design, were found. The companies JSC Sibkom, LLC Synergy and LLC NIPI NG PETON may be potential

Risk Management in the System of Economic Security of the Enterprise

185

competitors for the LLC Bashgiproneftekhim enterprise, as they have proven themselves in the market of services provided. Enterprises: Statusstroyproekt LLC, OINSI LLC, Geolocom LLC, INFORMSVYAZ ENGINEERING LLC, in my opinion, are not potential competitors, as they were registered less than 5 years ago, have a small number of employees (no more than three people) and minimum share capital. These companies do not have the opportunity to implement large-scale projects at the present time, as this requires expensive equipment and a large staff of professionals. After analyzing the competitive environment of Bashgiproneftekhim LLC, we can conclude that this event is simply necessary. Studying the market, analyzing the capabilities of competitors and determining the trading platform where the main customers are located are the main recommendations that will help the company minimize the risk of losing tender products. The occurrence of significant changes in the design process. This threat is especially typical for enterprises engaged in engineering and technical design. Most of the customers of Bashgiproneftekhim LLC are large Russian companies and factories. Each order is individual; therefore, it is very difficult to calculate the deadline for the completion of work since the contractor or customer may need to change the deadlines for the completion of work during the execution of the project. To eliminate the risk of significant changes in the scope and timing of work, it is necessary to conclude an additional agreement between the organization Bashgiproneftekhim LLC and the customer company. In the absence of this agreement, the executing company may violate its contractual obligations, resulting in both financial and reputational losses. Lack of qualified specialists. The enterprise Bashgiproneftekhim LLC can avoid this risk if it conducts an annual certification of the qualifications of employees, and, if necessary, improves the qualifications of personnel by sending employees of the enterprise for vocational training. As an additional way to neutralize this risk, the company’s cooperation with a specialized university can become. The Ufa State Petroleum Technological University can act as a partner university, which will help improve the skills of existing employees of the enterprise, as well as provide an opportunity for students to have an internship at the LLC Bashgiproneftekhim enterprise. These measures will help the company to replenish the staff of young professionals. Subsequently, if the employer has vacancies, priority in employment will be given to trainees. Failure of contractual terms with the main customer. As business practice shows, failure to meet contractual deadlines may arise due to unfair actions of contractors, shortcomings that arise during project activities, as well as due to circumstances that prevent the start or progress of work. At the enterprise LLC “Bashgiproneftekhim” special attention is paid to this risk, due to the specifics of the activity. The occurrence of such an event can lead to serious consequences, in the form of penalties or termination of the contract. As measures that can minimize the onset of such a risk, it is proposed to introduce a system of intermediate control of work performed. The control will allow to track the intermediate results of the design work, as well as to check the quality of the work performed by the subcontractor. This method should be used not only by the economic security service, but also by employees of the enterprise.

186

T. Tarasova and A. Krivtsov

Premature equipment failure. Since Bashgiproneftekhim LLC uses computer equipment, high-tech 3D printers and multifunctional devices of various formats when performing design and engineering work, there is a high risk of premature equipment failure. To minimize this risk, it is recommended to introduce a system of preventive maintenance (PPR). This system excludes the possibility of operation of the equipment with its intensive wear and implies trouble-free operation and timely repair of the equipment used. The proposed measures will help eliminate the risk of premature equipment failure. Unfair intra-industry competition. Many enterprises face unfair competition, regardless of the type of activity. In the market of services provided, there are competitors who ignore the Federal Law “On Protection of Competition”, thereby neglecting the prohibition on unfair competition. In relation to the LLC “Bashgiproneftekhim” enterprise, unfair competition may manifest itself in the form of collusion of several market participants, or a significant reduction in prices from a competitor for similar services provided. To timely identify and promptly respond to such threats from competitors, it is necessary to constantly analyze both new and existing market participants that provide similar services. Loss of business reputation. The risk of loss of business reputation is quite serious since the image of the company depends on the reputation. Reputational risks always lead to financial losses, which negatively affects the financial stability of the enterprise. Bashgiproneftekhim LLC can neutralize the occurrence of such risks by creating a PR department at the enterprise. It is the PR department that is responsible for the whole range of tasks related to the development of the company’s image, the formation of public opinion, the management of relations with the public, the media and authorities. This division of the company will be able to provide the necessary level of control and security in the section of reputational risks. For effective development, a competent management strategy must be implemented at the enterprise. SWOT-analysis is one of the tools for creating a business management strategy. Let’s consider a SWOT analysis of the enterprise LLC “Bashgiproneftekhim”, in order to identify the weaknesses of the enterprise, which can be used by competing organizations in the struggle for a leading position in the market (Table 5). According to the data obtained, the company has enough opportunities and strengths with which it is possible to take a leading position in the market and maintain its financial stability. Despite the large number of positive qualities, the Bashgiproneftekhim LLC enterprise also has weaknesses that need to be paid special attention so as not to face serious threats in the future. As mentioned earlier, every risk that cannot be prevented in time can easily turn into a threat to the enterprise. Neutralizing the threat is much more difficult, longer and not always possible. It is much easier to try to prevent the occurrence of a risk at an earlier stage of its manifestation. Summing up, it can be noted that the recommended measures to neutralize risks and threats will help improve the system of economic security at the enterprise of

Risk Management in the System of Economic Security of the Enterprise

187

Table 5. SWOT-analysis of the enterprise LLC “Bashgiproneftekhim” Strengths

Weaknesses

– – – – – –

– Equipment downtime – There is no opportunity not to take on low-liquid projects if they were from the main customer LLC NK Rosneft – Decrease in liquidity indicators – High management costs

Convenient location of production Technical equipment is at a high level Modernized equipment Experienced staff High-quality resource base Availability of innovative potential

Opportunities

Threats

– Creation of a unique project that has no analogues in the Russian market (with the help of state support) – Creation of a PR department that would promote the services provided in international markets

– Market instability (crisis) – Sanctions against Russia – Increasing competition in the market of services provided – Making adjustments by customers – Change in currency regulation

Bashgiproneftekhim LLC, and the weaknesses and possible threats identified during the study will be prevented by carrying out the proposed measures aimed at improving work efficiency enterprises.

References 1. Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968) 2. Baldwin, D.: The concept of security. Rev. Int. Stud. 23, 5–26 (1997) 3. Baturina, N.A.: Analysis of the investment attractiveness of the organization’s current assets. Econ. Anal. 3, 31–35 (2018) 4. Nanto, D.: Economics and National Security: Issues and Implications for U.S. Policy. Congressional Research Service. https://www.fas.org/sgp/crs/natsec/R41589.pdf. Last accessed 06 Aug 2022 5. National standard of the Russian Federation GOST R 58771–2019 Risk management. Risk Assessment Technologies. https://base.garant.ru/73747568/. Last accessed 06 Aug 2022 6. Novikov, K.A.: Characteristics of the main threats to the economic security of the enterprise and their neutralization. Health Is Basis Hum. Potential: Probl. Ways Solve Them 15(3), 1361–1365 (2020) 7. Onogda, A.V., Yarkina, N.N.: Financial risks in the system of ensuring the economic security of the enterprise. Eurasian Sci. J. 3 (2016), https://cyberleninka.ru/article/n/finansovyeriskiv-sisteme-obespecheniya-ekonomicheskoybezopasnosti-predpriyatiya. Last accessed 06 Aug 2022 8. Orlov, A.A.: Mechanisms for neutralizing the financial risks of an enterprise. Univ. Bull. 2 (2020), https://cyberleninka.ru/article/n/mehanizmy-neytralizatsii-finansovyh-riskov predpriyatiya-1. Last accessed 06 Aug 2022

188

T. Tarasova and A. Krivtsov

9. Osipova, V.A.: Information security as an element of economic security. Vector of Economics of Rostov State Transport University (2020), https://www.elibrary.ru/download/elibrary_426 61995_82064492.pdf. Last accessed 06 Aug 2022 10. Zaripova A.I., Kovalenko, S.V.: Financial risks in ensuring the economic security of enterprises. Young Sci. 1(187), 61–63 (2018). https://moluch.ru/archive/187/47652/. Last accessed 06 Aug 2022

The Specifics of Determining the Value of Segments of Digital Ecosystems Vladislav Podtopelny(B)

and Alina Babaeva

Kaliningrad State Technical University, Kaliningrad, Russia [email protected]

Abstract. Sets of components of ecosystems, interconnections of components and subsystems are considered. The role of central control elements in the architecture of a digital system is determined and studied, their criticality is considered as a criterion for assessing the importance of an information resource. The specificity of determining the value of the resource value as critical for the health of the ecosystem is considered, taking into account the number of dependent components, the number of intercomponent links, and the value of the significance of links. The degree of influence of intercomponent links on the performance of the entire ecosystem is being studied. The features of communication disruption, transmission over its channel, the specifics of the influence of the direction of migration of control, service and user data on the value of the resource are considered. The definition of ecosystem components is formalized, taking into account the number and significance of links with other components. Types of links are considered: strong unidirectional links, weak links of autonomous components. It is determined that the central components that affect autonomous subsystems have strong connections, the number of connections is correlated with those threats that are realized through intercomponent and intersystem connections, the value of the criticality of digital eco-environment objects can change subject to a change in the number of dependent components. Measures are substantiated to maintain the efficiency of the digital ecosystem: it is recommended to use duplication of systems, which involves restoring the work of the central system-forming part until the need for its functionality arises. The expediency of using weak ties in the digital environment is determined. Keywords: Ecosystem · Digital environment · Resource value · System criticality · Subsystem · Threat · System error

1 Introduction When we talk about the digital ecosystem, we mean some client-centric system services. It is assumed that there are several groups of services that meet the different needs of consumers of digital services. At the same time, it is assumed that the provider of digital services is the same company, and all consumer data is aggregated in a single accounting system. It should be noted that the services of digital media services may be different. The advantage of such a system of services is obvious: a client receiving a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 189–196, 2023. https://doi.org/10.1007/978-3-031-34329-2_19

190

V. Podtopelny and A. Babaeva

digital service receives not only it, but also a whole range of other potentially important opportunities for the client. Thus, he gets a whole functioning environment called the digital ecosystem. On the one hand, this is beneficial to the client, but on the other hand, this implies working in a closed environment in which the client exists and at the same time, he cannot leave it as a recipient of the service. To receive a new set of services, the user must change the ecosystem. Obviously, the reason for the existence of digital ecosystems is that the client does not need to go beyond its boundaries. The structure and operation of any system is formed based on the features of the supporting system and business processes [1]. Thus, an array of system and business processes makes it possible to describe the functional model of a digital ecosystem, regardless of its purpose [2, 3].

2 Materials and Methods. Definition of Specifics If we consider the architecture of many different digital ecosystems being created, then all of them will contain typical sets of already known information systems, between which there is a close relationship. Such links are provided by a single system of accounting and aggregation (summation) of data. That is why in such ecosystems there is a single account for using all services (therefore, there is a single registration subsystem of services). This approach to building modern digital ecosystems leads to the understanding that the problems that arise in the field of security in an information system previously known and used as a separate digital service will be inherited by modern ecosystems [4]. Since a single system for accounting and data aggregation will link components, that is, information systems, together, the inherited security problems of one element will affect many others, based on their close relationship. Consider a functional model using the simplest architecture of a digital ecosystem as an example, designed to ensure the functioning of an organization in a digital environment. The ecosystem architecture assumes that there is a set of some central server components that parse and provide data services. However, there are several user services (they are usually standalone) that provide a discount for the following: 1. 2. 3. 4. 5. 6. 7. 8.

Postal message Subsystems of identification and authentication; Subsystems for managing and aggregating and broadcasting data; Instant electronic messages (messengers); Videoconferencing and training; Systems of electronic document management and joint work on documents; Food delivery systems; System for making an appointment with a doctor or other specialist, etc.

In most cases, all these services use the same data processing cycle, which is very convenient when aggregating typical data, saving them as standard templates to further take into account the wishes of the user, and also in order to save time when processing user request data online. The following stages of the processing cycle are assumed (here

The Specifics of Determining the Value of Segments …

191

we consider stages that are not tied to any service, that is, they can be used to consider any service): o o o o o o

data acquisition, verification; analysis and obtaining the result, verification; data entry, verification; processing by the data service (determining the handler by data type and processing); transferring the result to users and duplicating the result for other system services; saving the result for further processing, accounting, saving logs in the system. You can also consider several states of the components of digital ecosystems:

o o o o

refusal to register the request; an (in)correct decision was made to refuse processing the request; the request was (in)correctly executed; fixing the progress of the request in the log file.

Obviously, there are many reasons that cause certain states of ecosystem services. They may be the result of an attacker’s influence or be the result of some service error, indirectly or directly related to the processing of the request. Events generated by ecosystem system service processes are discrete. In this case, the processes are carried out continuously. It should be noted that all stages and subprocesses are interconnected, and some of the services that are included in one of the above states can be associated with all services due to the use of the principle of building digital ecosystems: close interconnections for subsequent data aggregation by central system services (see Fig. 1).

Fig. 1 Drawing of a system with many connections between different components of different ecosystems

In such digital ecosystems, there are many typical processes that use data designed to work with several services simultaneously, that is, service processes can transmit their results to several services at once. They act as aggregating or central elements in the architecture of the digital eco-environment, that is, they actually act as a traffic gateway within the ecosystem, which either processes data packets or redirects them. There are

192

V. Podtopelny and A. Babaeva

also processes that work individually with autonomous services of digital ecosystems, without implying the distribution of their data for other services. Using the theory given in published works [5, 6], we define the value of critical resources (S) as a value depending on the values of the following type: on the measure of the resistance of a critical component (W) (the value can be expressed in terms of effective operation time, or the amount income from the operation of the component), the magnitude of the decrease in resistance depending on the type of system components, the number of connections, quantitative indicators of labor costs to support stability/reliability.

3 Results. Determine the Importance Before considering formal definitions, it should be pointed out that the criticality of the central systems of ecosystems actually always determines the performance of the digital system as a whole. The central systems in this case include: 1. Subsystems of identification and authentication. 2. Subsystems of control, aggregation and translation of data. Thus, if the central subsystem of the digital ecosystem is compromised by one of the autonomous subsystems, then this compromise will negatively affect the performance of the centralizing subsystem and, in addition, the negative impact will be reflected in the work of previously unaffected, but connected autonomous subsystems. In this case, we will consider the value of S as a total indicator of the results of the influence of factors of a negative nature, taking into account all the elements of systems or other subsystems interconnected with the considered component. Indicators of a negative impact on the stability of systems include: o cases of attacks, the cost of intruders’ resources to compromise the object is taken into account (this value should be considered as time costs that are inversely proportional to the amount of resources spent); o the case of an error (such a value should be considered as a loss of time for efficient operation). Thus, the value S of some autonomous system or component, based on the formal description [6], can be determined as follows:   N  ar · Dri,j (t) , (1) Sm (t) = max Wi,j (t) − i∈I ,j∈J

r=1

where W i,j is the value of the resistance of the component received by the consumer at time t; t is the lifetime of the resource (here it is important to emphasize that the resource of the central aggregating system must always be operational, and if the measure of its stability tends to zero, then the value of t will also tend to 0); i, j—connections (the number of interacting objects), a measure of the distribution of influence; Dr(i,j) —impacts that reduce the stability of the critically important r-th resource; ar is the coefficient of

The Specifics of Determining the Value of Segments …

193

reduction of consumption of the r-th resource of the consumer to the units of measurement of final effects; N is the number of components, which is limited by the architecture of the subsystem of the digital environment (paths of influence of threats). When calculating the value of the system as a whole, one can take into account the total resistance of all components of which it consists [7]. In this case, the value of the ecosystem is determined by the formula:   N M   ar · Dri,j (t) (2) S(t) = max Wmi,j (t) − i∈I ,j∈J

n−1 r=1

However, due to the fact that there is a close relationship between the components of the system, and at the same time there is a dependence on the centralized system of autonomous subsystems (that is, the weight of the connection between the components of these systems and the components of other systems is large), it is advisable to consider the definition of the value of an ecosystem through its central (main) components. When considering the above case, it is obvious that the influence through the central component of autonomous systems on other autonomous subsystems will be less than the influence of the initially compromised central subsystem. This is due, first of all, to the peculiarities of data processing processes and the essence of the data itself, since the main information blocks are located in the central system. Then, using formula 2 as an example of the influence of the central subsystem on another of the same type, we can get some abstract idea of the value of the resource, taking into account the dependence on its performance of other resources or autonomous systems.   N  ar · Dri,j · Hri,j (t) (3) Sm (t) = max Wi,j (t) − i∈I ,j∈J

r=1

where H are the impacts that reduce the durability of the r-th resource from another critically important resource. At the same time, it should be noted that the situation is considered when data is transmitted from central subsystems to autonomous ones, that is, one-way communication is considered. If the value of a locally significant resource for an autonomous system and, accordingly, having a limited value is considered, then the value of its value will also be determined by formula 3, where the value of the subsystem stability will be used as the value of the system stability (accordingly, its value as an autonomous component will be determined). Autonomous parts of the digital eco-environment interact with each other through the central component. In the event of a security incident, they will constantly transmit the same error or the same result of the implementation of the threat (as a result of each iteration it is added to the total value of the decrease in resistance from the results of previous iterations), constantly increasing the amount of compromise, thereby reducing the value of the system as a whole. This situation is comparable to the cascading error propagation in the system, and the sum of security events tends to a maximum [7]. S(t) = max

i∈I ,j∈J

N  r=1

ar · Dri,j (t)

(4)

194

V. Podtopelny and A. Babaeva

In the same way, you can consider the information of the data subject, who transmits them to the digital ecosystem (in the future, these data are the source for many services). The critical importance of objects (their value) S(t) for the consumer of information can be reduced by introducing duplicate functional components into the system. Such methods are mainly used when building protection against DDoS attacks. The functionality of redundant systems involves the restoration of the functioning of the central system-forming part until the need for this functionality arises (the value is determined as a minimum of time spent on restoring the function [7]). However, this approach does not work well when considering the importance and value of user data. Decreasing their privacy, like stealing or exposing them, leads to the loss of their value and, at the same time, to the compromise of the system. Thus, duplicating information or duplicating functionality is an important component of only system services intended for translation of data processing.

4 Discussion It is obvious that among the many components of ecosystems there is a sufficiently large number of elements, on the activity of which the functioning of other components and/or subsystems depends. Accordingly, the value of such resources is multiplied if we consider the criticality (degree of performance) of data processing objects as a criterion for assessing the importance of an information resource. From this point of view, the importance of an object is determined not only by the degree of value of the results of the process, a certain component of the system for subsequent processes of the system, that is, the impact on the performance of a separate subsystem, but also by the number of links that the component under consideration has (the number of links may indicate importance (criticality) object) [8, 9]. In this case, the number of links correlates with the number of threats or errors, which increases in accordance with the number of dependent components (there is an effect of cascading error propagation). Accepting data for processing, the attacked or damaged resource compromises the information and the resource that transmitted it (see Fig. 2).

Fig. 2 Propagation of a compromise from a peripheral system

The Specifics of Determining the Value of Segments …

195

Therefore, when calculating the importance (value) of a resource, it is necessary to take into account the direction of data transfer of service and user types when considering component relationships. The weight of the connection also affects the criticality (value) of the components of the digital ecosystem, it is determined by the importance of the related components. Thus, it is required to take into account the mutual influence of the components when determining their value. Obviously, with this approach, most of the structural components, taking into account the peculiarity of the ecosystem architecture (many functionally autonomous subsystems and central aggregating, distributing and storing information), can be divided into two groups: 1. A group of critically important objects for the entire system that have many connections and/or a high level of dependencies (central subsystems). 2. A group of critically important objects limited by the architecture of a subsystem that has a narrow functional profile (autonomous systems). The task of the analyst in determining the value of critical resources, first of all, is to determine the most significant relationships, and based on the results of considering these relationships, identify a set of central components [10]. It should be mentioned that the significance of the relationship is also determined by the degree of dependence of one component on another. Maximum dependence means that one component (system) can perform its functions only if it supports them, provides them with a data flow, resources, another component (system). Considering the interaction of subsystems of digital ecosystems, strong weighty connections of a one-way type of central subsystems affecting autonomous highly specialized subsystems were noted. This is justified for the architecture of modern digital ecosystems. In the event that the autonomous components of digital environments will have strong ties with respect to the central subsystems, any error or threat that is being realized can immediately bring the digital environment into a state of inoperability. The reasons for this situation are as follows: the implementation of the threat, due to the presence of strong connections from the autonomous subsystem to the central one, creates events that compromise the central system, while the central system already transmits these results to other autonomous subsystems.

5 Conclusion In digital environments, there are many components of ecosystems, on the operation of which the functioning of other subsystems and components depends. Since they play the role of central control elements in the architecture of a digital system, their criticality was considered as a criterion for assessing the importance of an information resource. In the course of describing the pairing of arrays of system and business processes as a functional model of a digital ecosystem (regardless of its purpose), it was determined that the value of a resource as critical to the health of the ecosystem is determined using the following: o the number of dependent components; o number of intercomponent bonds;

196

V. Podtopelny and A. Babaeva

o the magnitude of the significance of connections (it is corrected by the degree of influence on the performance of all ecosystems in the event of a communication failure or the transmission of compromising data through its channel); o directions of migration of control, service and user data. When considering the components of ecosystems, taking into account the number and significance of connections with other components, it was determined that central components that affect autonomous subsystems have strong unidirectional connections. In this case, the number of connections is correlated with those threats that are realized through intercomponent and intersystem connections. This value can change if the number of dependent components changes (error cascading effect). To maintain the health of the digital ecosystem, it is advisable to use the functionality of backup systems, which involves restoring the operation of the central system-forming part until the need for its functionality arises. In addition, it is advisable to maintain weakened links from autonomous systems to central ones. This will avoid the cascading propagation of bugs and threats in the ecosystem.

References 1. GOST P 27.014 Reliability in technology. Reliability management. Guidance for setting system reliability requirements 2. Hentea M.: Intelligent System for Information Security Management: Architecture and Design Issues In: Issues in Informing Science and Information Technology 4: 15–20 (2007) 3. Brotby K.: Information security governance. A Practical Development and Implementation Approach, Hoboken: John Wiley & Sons, Inc.: 184–185 (2009) 4. Susanto, H., Yie, L., Setiana, D.A., Yani, Y., Riyanto, A., Slamet, S., Fadly: Digital ecosystem security issues for organizations and governments. Digital Ethics and Privacy (2021). DOI: https://doi.org/10.4018/978-1-7998-4570-6.ch010 5. Osipov V. Yu., Kondratyuk A. P.: Evaluation of information in the interests of reflexive management of competitors In: Software products and systems 2: 64–68 (2010) 6. Osipov V. Yu., Nosal I. A.: Substantiation of measures to ensure information security. In: Information and control systems 2(63): 48–53 (2013) 7. Nosal I. A.: Justification of information security measures of socially important objects: thesis of a candidate of technical sciences, Institute of Informatics and Automation of the Russian Academy of Sciences, St. Petersburg: 145–148 (2016) 8. Burgess, M., Canright, G., Engø-Monsen, K.: A graph-theoretical model of computer security. Int. J. Inf. Secur. 3(2), 70–85 (2004). https://doi.org/10.1007/s10207-004-0044-x 9. Dewri, R., Ray, I., Poolsappasit, N., Whitley, D.: Optimal security hardening on attack tree models of networks: a cost-benefit analysis. In: International Journal of Information Security 11: 167–188 (2012) 10. Sun, L., Srivastava, R.P., Mock, T.J.: An information systems security risk assessment model under Dempster–Shafer theory of belief functions. In: Journal of Management Information Systems 22(4): 109–142 (2006)

Russian Banks’ Monetary-Targeting and Investment Strategies in the Context of Elevated Uncertainty of Financial Markets Elena Gordeeva(B) Kaliningrad State Technical University, Kaliningrad, Russian Federation [email protected]

Abstract. Financial and economic sanctions as well as political situation in the world and in Russia particularly plays a tricky role in banks strategies and longterm planning. Moreover, the volatility of the ruble exchange rate has a negative impact on the financial and credit system of our country. In this regard, author brings up a problem of forming bad loan optimal strategy for managing debt of Russian banks. An economic-mathematical toolkit has been developed for strategic planning and management of the investment and credit activities of Russian banks, which makes it possible to obtain an effective strategy for the bad debt management in modern conditions. The study uses economic and mathematical models; when modeling the credit and investment processes of the bank, author suggests a fuzzy optimization problem, the solution of which makes it possible to form an optimal strategy for managing problem loans in fuzzy forecast economic conditions. The optimal strategy for loan debt managing is found from the solution of the developed optimization problem, provided that the maximum value of its economic efficiency indicator is reached. Keywords: Loan debt · Management strategy · Bad loan models · Economic efficiency

1 Introduction The effective functioning of commercial banks and their ability to lend money to enterprises in the real sector of the economy are largely determined by the availability of free investment and credit resources. Their value depends on the external and internal conditions of the bank’s lending activities. The first include the general macroeconomic situation and the state of the world market of international interbank borrowings; the second one depends on the quality of loan portfolios which are characterized by the share of overdue and bad loans. Since 2022 the situation in the Russian credit market has deteriorated significantly due to the ruble high volatility, low prices for the main export products and the lack of cheap long-term external borrowings (among other things due to sanctions imposed by the US, the EU and a number of other countries). The artificial external negative © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 197–205, 2023. https://doi.org/10.1007/978-3-031-34329-2_20

198

E. Gordeeva

background, characterized in particular by the downgrading of Russia’s investment ratings by international rating agencies, caused even greater restrictions on the availability of cheap external sources of investment and credit resources for domestic banks. This also affected the banks’ refinancing abilities of previously received loans on acceptable terms. The development strategy of the bank’s credit and investment resources is a certain set of general principles and rules, as well as models that support it, on the basis of which credit and investment decisions are made to achieve the specified goals in the long term. Therefore, one of the main directions for improving the credit and investment activities of banks is to ensure the optimal banking development strategy, taking into account the changing economic conditions. The bank’s objective credit and investment strategy is the effective use of the resources to achieve the main goal. In order to develop an optimal strategy banks should take into account the multidirectional processes that occur during the functioning of the bank as an economic system: its extensive and intensive development, its stability and economic growth, the consumption and accumulation of credit and investment resources of the bank, the use of own and borrowed funds for the development funds, etc. The mentioned multidirectional processes are characterized by a set of interdependent economic indicators (criteria), for which a change in one of them leads to an opposite change in the other: “own capital—borrowed capital”, “risk—income”; “dynamics of growth rates—sustainability of development”; “resource stock—resource costs” [1]. Thus, for sustainability of a strategy development, it is necessary to ensure a balance between interdependent indicators (criteria), i.e. to form a balanced banking credit and investment strategy based on the optimization models of the bank and optimization tasks. Quite a few optimization models of the bank have been developed for the implementation of balanced credit and investment strategies. Work [2] describes a systematization of the main economic and mathematical models that provide strategies in banking activities. The study notes that particular and generalized (complete) models have received the greatest distribution for describing the banking sector [3, 4]. Particular models generally describe a specific area of the bank’s activities, while generalized full models reflect the functioning of the bank as a whole. In the group of particular models, two directions can be distinguished [5], based on various hypotheses that describe the behavior of the bank in the money market and its ability to control the supply and demand processes in this market. Particular models are usually based on the hypothesis of low controllability of the deposit market, the flow of which depends on the overall economic and financial situation as a whole. These models consider financial agents operating in the loan market with which the bank is related [6, 7]. Optimal models for forming the structure of banking assets (portfolio of banking assets) are also analyzed, taking into account risk levels.

2 Materials and Methods The development of generalized models is based on the hypothesis that the demand for loans is determined by investment activity, as well as dependence on the deposit market and the factors that determine their attraction. The general theory of the firm and the costs theory are used to calculate the composition of banking costs [8, 9].

Russian Banks’ Monetary-Targeting and Investment Strategies

199

Unlike particular models that describe individual aspects of the bank’s activities, generalized models use complex methods. According to the research [10], the generalized model should include the interaction of the assets and liabilities of the bank and bank capital. To determine the optimal ratio of assets and liabilities of the bank in the generalized model presented in research [5], an objective function is formed that ensures the maximum profit of the bank. The study [4] proposed the so-called quasi-complete bank model, within which the following results were obtained: • bank’s decisions depend on costs, liquidity and risk; • the risk depends on the bank’s liquidity values and costs; • for an adequate description of decision-making processes, it is necessary to take into account resource costs and the change in the bank “portfolio” associated with the establishment of deposit rates. The study [11] proposes a model of a bank that satisfies the requirements for generalized models. This model is built taking into account the positive feedback between the current results of the bank’s activity and its own capital (resources) for the next time period. Nonlinear optimization models of a bank, taking into account changes in the external environment, describing various types of credit and investments and bank deposit resources, are considered [12]. The model accepts restrictions: the volume of placement of credit resources; deposit resources should not exceed the amount of savings of the bank’s customer base; the liquidity of the bank; the value of the difference between the value of assets and liabilities that are sensitive to changes in the interest rate. To ensure the banking strategy in the credit and investment sphere, a modernized optimization model is proposed based on the model developed [12, 13]. The presented model takes into account the impact of the introduced and possible financial sanctions against the Russian Federation. Within the framework of this model, we require the following restrictions to be satisfied: 1. The total supply of loans does not exceed the demand of the industry’s customer base (assuming the solvency of each customer) is described by the inequality: K 

xij ≤ Pj , xij ≥ 0, j = 1, K; i = 1, M ,

(1)

i

where: i j

type of credit investments: short-term, medium-term and long-term; type of industry that is the object of investment in accordance with OKVED (Russian Classification of Economic Activities); xij the volume of loans of type i to industry of type j (in value terms);

200

E. Gordeeva

Pj the total need of creditworthy enterprises of the jth industry in loans, confirmed by the relevant project and investment justifications. 2. In accordance with the requirements of paragraph 3.5 of the Instructions of the Bank of Russia [14], imposed on the long-term liquidity ratio of the bank, the following inequality must be fulfilled: N K   i

  xij ≤ 1, 2 BC + BL + 0.50∗ ,

(2)

j

where BC—volume of the bank capital; BL—obligations (liabilities) of the bank on loans and deposits received by the bank, with the exception of the amount of the subordinated loan (deposit) received by the bank in terms of the residual value included in the calculation of the bank’s own funds (capital), as well as on the debt circulating on the market bank’s debt obligations with a remaining maturity of more than 365 or 366 calendar days; O*—the value of the minimum total balance of funds on accounts with a maturity of up to 365 calendar days and demand accounts of individuals and legal entities (except for credit institutions) that are not included in the calculation of the BC indicator is determined in the manner established by clause 3.6 of this Instruction. A bank’s long-term liquidity ratio limits the risk of a bank losing liquidity as a result of placing funds in long-term assets. Inequality (2) shows that the maximum value of credit requirements of banks (with a period remaining until the maturity date is higher than 365–366 calendar days) cannot exceed by more than 20% the total value of 3 terms: bank capital, obligations under loans and deposits received by the bank (except for the amount of the subordinated loan in terms of the residual value included in the calculation of the credit institution’s own funds) and half of the minimum total balance of funds on the accounts of individuals and legal entities, except for credit institutions. The volume of distributed credit and investment resources of the bank must satisfy the inequality [15, 16]: N K   i

 xij ≤ (Dk + (1 − rb)O) (1 + ro)

(3)

j

ro

the aggregated reserve requirement ratio, which determines the cash allocations of reserves to the Bank of Russia; rb reserve ratio of funds to cover crisis credit losses (the reserve is created by the bank on its own initiative at the expense of its profit); O the bank’s obligations on attracted deposits of legal and physical persons; interbank credits; liabilities arising as a result of the issue of bills of exchange and securities (except for shares); DK cash portion of a bank’s equity. 4. In accordance with the regulations of the Central Bank of the Russian Federation [15, 16] and its internal credit policy at each point in time, the total amount of outstanding

Russian Banks’ Monetary-Targeting and Investment Strategies

201

funds on loans should not exceed the amount of the reserve determined by the bank and the central bank of the Russian Federation. N K     dij + dbij − Zij − ZSij xij ≥ 0 i

(4)

j

dij

amount of the reserve created by the bank in accordance with the regulatory legal acts of the Bank of Russia; dbij the amount of the reserve created by the bank in accordance with the internal credit policy (usually depends on the quality of loans); Z ij the actual (average industry) risk level of credit investments of type i in industry j (determined on the basis of statistical observations); ZS ij change in the level of risk of credit investments of type i in industry j due to the action of sanctions (determined on the basis of expert assessments). Seen as an outrage caused by the effect of the sanctions. In relation (4), the amount of reserves created by the bank in accordance with the regulatory legal acts of the Bank of Russia depends on the industry-average level of risk of credit investments. The change in the amount of the reserve created by the bank is carried out in response to changes in the level of risk of credit investments according to the bank’s estimates in specific economic conditions. Sanctions increase the level of risk of credit investments of type i in industry j by ZS ij . Accordingly, to fulfill inequality (4), it is necessary to increase the reserve d ij and dbij , created by the bank in accordance with the regulations of the Bank of Russia and the internal credit policy. The maximum average profitability of the entire volume of credit and investment investments is chosen as the target function: N K   i

rij xij → max,

(5)

j

where r ij is the average profitability per unit of investment of type i in industry j. The presented optimization problem (1)–(5) belongs to the class of linear programming problems with imposed restrictions, for the solution of which it is necessary to find the distribution of bank resources x ijopt , which deliver the maximum objective function (5) when the system of restrictions is satisfied (1)–(4). In practice, the formation (construction) of the above optimization model seems to be an elusive task due to the action of a number of factors that are difficult to predict in advance and take into account when setting parameters and criteria in the process of setting problem (1)–(5). For example parameters Pj , dbij , ZS ij and rij of this model are indefinite, which does not allow them to be described by certain values. For a formal description of such parameters, clear intervals, fuzzy intervals (numbers) or probability distributions are usually used. Clear intervals are used when only the boundaries of the intervals are precisely defined (known) and information (quantitative or qualitative) about the values of a given parameter within a given interval is unknown.

202

E. Gordeeva

At the same time, the description of these parameters with the help of the probability distribution seems to be difficult due to the impossibility of obtaining statistical observations of their behavior. If there is quantitative or qualitative additional information about the values of the parameter within the interval, for example, that some elements of the interval are in a certain sense preferable to others, then the mathematical formalization of such uncertainty can be adequately implemented using fuzzy numbers described by their membership function [17–19]. Note that the nature of the uncertainty of the indicated parameters most fully meets the requirements of the uncertainty of fuzzy numbers, so it is advisable to model these parameters with fuzzy numbers. Figure 1 shows such fuzzy numbers that are often used in practice, described by their membership functions: pi-like, trapezoidal, triangular, sigmoid, Gaussian.

Fig. 1. Frequently used fuzzy number membership functions

3 Results and Discussion Based on the optimization problem (1)–(5) presented above, we will form a fuzzy prob˜ ij , ZS ˜ ij i r˜ij . At the same time, the solution to lem (6)–(10) with fuzzy parameters P˜ j , db this problem is the optimal distribution of bank resources x˜ ij will also belong to the type of fuzzy numbers. K 

x˜ ij ≤ P˜ j , xij ≥ 0, j = 1, K, i = 1, M ,

(6)

i N K   i

j

  x˜ ij ≤ 1, 2 k + BC + 0.50∗

(7)

Russian Banks’ Monetary-Targeting and Investment Strategies N K   i

 x˜ ij ≤ (DK + (1 − rb)O) (1 + ro)

203

(8)

j

N K      ij − Zij − ZS ij xij ≥ 0 dij + db i

(9)

j N K   i

r˜ij x˜ ij → max,

(10)

j

Note that in problem (6)–(10) all the algebraic operations are carried out according to the rules of the algebra of fuzzy sets [18, 29, 38, 53]. In practice, the initial parameters are usually set in the form of trapezoidal or triangular fuzzy numbers, which are a special case of the trapezoidal fuzzy number. Then the results of solving problem (6)–(10) are represented as trapezoidal fuzzy numbers. This raises the problem of mathematical formalization of the initial fuzzy parameters of the considered fuzzy optimization problem (6)–(10). These parameters will be modeled by fuzzy numbers, the membership functions of which are found using the method of paired comparisons using expert estimates of predominantly one element over another in relation to the known property of the fuzzy set. So, for example, to build the membership function of a fuzzy set—“the total need of creditworthy enterprises for loans is approximately equal to some value P” on the universal set of elements pi, i = 1,..,n, paired comparisons are represented by the following matrix: ⎡

p11 ⎢ p21 P=⎢ ⎣... p11

p12 p22 ... p12

⎤ . . . p1n . . . p2n ⎥ ⎥. ... ... ⎦ . . . p1n

(11)

Here, the value of pij is understood as the level of advantage of the element pi over the elementpij . Usually this level is determined by the nine-point Saaty scale [68, 69]: Membership degrees (membership function values) are taken equal to the corresponding coordinates of the eigenvector V, which is found from the following system of equations [20]:  PV = λmax V (12) v1 + v2 + · · · + Vn = 1 where λmax —matrix maximum eigenvalue P. Thus, the proposed procedure allows each fuzzy parameter of the considered fuzzy optimization problem to be modeled by membership degrees (membership function values) on a given universal set of elements pi , i = 1,..,n. For the convenience of further calculations, the results obtained can be approximated by known fuzzy numbers. The proposed fuzzy optimization model uses interrelated indicators (criteria), for which a change in one of them leads to an opposite change in the other, as a result,

204

E. Gordeeva

the solution of the fuzzy problem (6)–(10)—the distribution of credit and investment banking resources in the form of a fuzzy numbers x˜ ijopt , will meet the requirements of a balanced banking credit and investment strategy [21].

4 Conclusion The impact of artificial restrictions on the part of the global financial system on the Russian Federation is carried out as a negative impact on the financial and banking sector, as well as on enterprises and companies of the Russian Federation. The impossibility for banks and companies to refinance foreign currency loans with cheap money has a negative impact on the general economic situation in the Russian Federation. This situation leads to a decrease in credit and investment resources in the banking system, the number of creditworthy enterprises and an increase in unreliable borrowers. In response to the negative change in the general economic situation, in order to stabilize it, the government and the Central Bank of the Russian Federation are introducing retaliatory measures in the form of directive decisions in the financial and credit and investment areas [22, 23]. Note that under the conditions of sanctions, the parameters and criteria of the model (6)–(10), which provide the strategy of banks’ credit and investment resources, will change, and the solution of the optimization problem perturbed by the sanctions will be balanced in the new economic conditions. In this case, the difference between the old and new values x˜ ijopt will be an estimate of the problem loan debt of the bank. Thus, in order to choose a balanced strategy of credit and investment resources of banks in the context of sanctions in these economic conditions, it is necessary to take into account three options for the actual placement of loans of type i in the industry of the type j − x˜ ijf : 1. Inequality fulfillment: x˜ ijf < x˜ ijopt —shows that industry j is underinvested by the corresponding loans of the form i. 2. Condition fulfillment x˜ ijf = x˜ ijopt means that the allocation of loans volume x˜ ij , is balanced within the framework of the model (6)–(10). 3. Inequality fulfillment: x˜ ijf > x˜ ijopt —shows that under given economic conditions, industry j may have δ x˜ ij = x˜ ijf − x˜ ijopt of bad loans.

References 1. Zaitseva, I.G., Shapovalova, S.S.: Analysis of the macroprudential policy of the Central Bank of the Russian Federation in the conditions of different phases of business cycles. Econ. Entrep. 6(143), 93–96 (2022) 2. Egorova, N.E.: Mathematical methods of financial analysis of banking (on the example of a large savings bank). Audit. Financ. Anal. 2, 75–146 (1998) 3. Sealey, C.W., Linndley S.T.: Inputs, outputs, and theory of production and cost at depository financial institutions. J. Financ. 1251–1266 (1977) 4. Sealey, C.W.: Deposit rate-setting, risk aversion, and the theory of depository financial intermediates. J. Financ. 1139–1154 (1980)

Russian Banks’ Monetary-Targeting and Investment Strategies

205

5. Sinky, J. (Jr.): Financial Management in Commercial Banks: Catallaxy, p. 937 (1994) 6. Kane, Malkiel, B.G.: Bank portfolio allocation. deposit variability, and the availability docturine. Q. J. Econ. 113–134 (1965) 7. Pyle, D.H.: On the theory of financial intermeditation. J. Financ. 734–747 (1971) 8. Federal Law of February 25,1999 N 39-FZ On investment activities in the Russian Federation, carried out in the form of capital investments (with amendments and additions) https://base. garant.ru/12114699/. Last accessed 22 Mar 2023 9. Klin, M.A.A.: Theory of the banking firm. J. Money, Credit. Bank. 205–218 (1971) 10. Baltensperger, E.: Alternative approaches to the theory of the banking firm. J. Monet. Econ. 1–37 (1980) 11. Blažeková, O., Vojteková, M.: Bad debts as a global problem in banking sector. (2018) https://www.researchgate.net/publication/327578529_Bad_debts_as_a_global_ problem_in_banking_sector. Last accessed 25 Mar 2023 12. Egorova, N.E., Smulov, A.M., Poletaeva, V.M.: Methods for substantiating a balanced credit and investment strategy for the development of banks. Econ. Entrep. 5(22), 64–70 (2011) 13. Caballero, R., Farhi, E., Gourinchas, P.: An equilibrium model of ‘global imbalances’ and low interest rates. Am. Econ. Rev. 98(1), 358–393 (2008) 14. Instruction of the Bank of Russia dated December 3, 2012 N 139-I On mandatory bank ratios (with amendments and additions). http://www.cbr.ru/publ/Vestnik/ves121221074.pdf. Last accessed 25 Aug 2015 15. Regulation of the Bank of Russia No. 254-P dated March 26, 2004 “On the procedure for the formation of reserves by credit organizations for possible losses on loans, loan and equivalent debt” (as amended on November 14, 2016). Adopted by the Bank of Russia on 26 March 2004. Entered into force on August 1, 2004. (Registered with the Ministry of Justice of Russia 26.04.2004 N 5774) http://www.consultant.ru/document/cons_doc_LAW_47597/. Last accessed 22 Mar 2023 16. Bank of Russia Regulation No. 342-P, dated August 7, 2009, “On the Required Reserves of Credit Institutions” (registered with the Ministry of Justice of the Russian Federation 15.09.2009 № 14775) http://www.cbr.ru/snglav/getdocument.aspx?documentid=1381. Last accessed 12 Sept 2015 17. Shepherd, D., Shi, F.K.C.: Economic modelling with fuzzy logic. IFAC Proc. 31(16), 435–440 18. Diligensky, N.V., Dymova, L.G., Sevastyanov, P.V.: Fuzzy modeling and multi-criteria optimization of production systems under uncertainty: technology, economics, ecology. Mech. Eng. (2004) 19. Zade, L.A.: Outline of new approach to analyses of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3, 28–44 (1973) 20. Rotshtein, A.P., Shtovba, S.D.: Reliability estimation of algorithmic processes with fuzzy input data. Bull. VPI 2, 30–37 (1996) 21. Egorova, N.E., Tikhnenko, A.N.: Methods and algorithms for the formation of an investor’s strategy operating in the stock market. Econ. Entrep. 3/2(56–2), 450–453 (2015) 22. Lando, D.: Credit Risk Modeling: Theory and Applications. Princeton University Press, Princeton, NJ (2004) 23. Orlov, A.I.: About the development of mathematical methods of controlling. Polythematic Netw. Electron. Sci. J. Kuban State Agric. Univ. 120, 49–59 (2016)

The Global Practice of Implementation and Use of Digital Currencies of Central Banks Olga Kuzmina1(B)

, Maria Konovalova1

, and Tatyana Stepanova2

1 Samara State University of Economics, Samara 443091, Russia

[email protected] 2 Kaliningrad State Technical University, Kaliningrad 236022, Russia

Abstract. At present, central bank digital currencies (CBDC) are not unambiguously defined, they cannot be considered sufficiently studied, there are discussions about the expediency and necessity of their introduction into circulation and the characteristics that they should have. The purpose of the research is to analyze the peculiarities of the processes of introduction and use of central bank currencies in different countries of the world. The study of the global practice of central banks’ introduction of digital currencies into economic circulation includes a review of approaches to the definition of this category, identifying the main reasons and goals for the construction of such an instrument, and outlining its key characteristics. Particular attention is paid to the analysis of the provisions of specific projects of individual countries of the world, by the example of which one can judge the effectiveness and feasibility of the introduction of digital currencies of central banks in monetary circulation. The identification of approaches to the processes of functioning of the Russian money market in the realities of the digital transformation of the economy and the finding of effective ways of its development in the current circumstances constitute the practical significance of the results of the study. Keywords: Money · Digital currencies · Central banks

1 Introduction Over the last few years, interest in CSD has grown: according to the results of a survey by the Bank for International Settlements in 2021, 86% of central banks are actively exploring the potential of CSD, 60% are experimenting with the technology, and 14% are deploying pilot projects [1]. Such tendency, at first sight, can be explained by the following set of reasons. First, the spread of cryptocurrencies and their public acceptance led to multinational companies being able to enter the payment services market, for example, back in 2019, Facebook presented the stabelcoin Libra (later renamed Diem), which is presented in an official document as a global currency and financial infrastructure that empowers billions of people. The popularity of cryptocurrencies and the emergence of “private money” may result in a shift away from traditional instruments of payment, which in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 206–222, 2023. https://doi.org/10.1007/978-3-031-34329-2_21

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

207

the long run may threaten the sovereignty of national currencies and reduce the role of central banks. Secondly, in the emerging conditions of digitalization of economic relations, regulators risk falling behind the global trend and being influenced by the national payment system of another state, and therefore develop projects of their own tools that meet the new demands of users of payment services. Third, the introduction of a CSD can give regulators additional opportunities to control the movement of funds, give them new powers, increase the share of settlements in national currency, and increase the efficiency of payments [2, 3]. Fourth, the COVID-19 pandemic, during which users of payment systems gave more and more preference to online payments, increased the demand for fast and low-cost transactions, contributed to the growing interest of certain countries in digital national currencies. In this light, digital currencies could serve as a tool to reduce the operational risks of payment systems.

2 Methodology The causal method formed the basis for identifying the reasons for the interest of central banks in the issuance of retail digital currencies. The historical approach in the form of retrospective analysis showed the advantages and disadvantages of the process of implementation of pilot projects for the introduction of digital currencies of central banks in the monetary circulation of various countries, including the process of construction of the digital ruble. The assessment of the demand for digital currency of the central bank on the part of economic subjects was determined with the help of correlation and regression analysis tools. The opportunities and risks of implementing the digital ruble in Russia’s monetary circulation required a SWOT-analysis.

3 Results A central bank’s digital currency can be defined as a new form of national currency expression designed to make payments [4]. This definition corresponds to the retail architecture of digital currency. At the moment there is no unified classification of digital currencies, but the division into wholesale and retail is basic, as it is carried out according to their functional purpose. Wholesale digital currencies are the central bank’s payment system, which is available to a narrow range of persons, they can be financial institutions holding funds on the central bank’s accounts or professional participants of the financial market. Wholesale digital currencies can be compared to the Central Bank’s reserves accumulated in its correspondent accounts and deposit accounts of financial intermediaries. Retail VCBs are a new form of national currency or digital form of cash, which is available for use by individuals and legal entities. It is assumed that such currency will be issued in the ratio of 1:1 with fiat currency, conversion into other forms of money will be free, these financial instruments will receive the status of means of payment.

208

O. Kuzmina et al.

The main interest of central banks is aimed at the study, development and implementation of retail digital currencies −70% of regulators surveyed in 2021 were studying the retail model. Ecuador was the first to test its national digital currency. Since 2000, Ecuador abandoned its own currency, the sucre, and adopted the U.S. dollar as its official currency. Only 40% of the population had bank accounts as of 2014, indicating low availability of financial services. The launch of the digital sucre meant the return of the sovereign currency and could have helped develop the payment infrastructure and increase financial inclusion. But the project proved costly for the government and there was no demand from the population, so the digital sucre was never introduced. In 2017, a pilot project was launched to launch Uruguay’s digital currency, e-Peso. The regulator stated the purpose of the digital currency was to save on the issuance of banknotes and related security and transportation costs, as well as to increase the transparency of money flows in the country. The piloting was successful, but the e-Peso was withdrawn from circulation in 2018. At the moment, there is no information on further development of the project [5]. Digital currencies were first recognized as legal tender by central banks in October 2020 simultaneously in the Commonwealth of the Bahamas and Cambodia. Due to the geography of the Bahamas, there are serious limitations to access to financial services: some small islands are not served by banks at all, as a result of natural disasters in 2019 bank branches and cell towers were damaged and took a long time to recover, resulting in the lack of ability to make cashless payments. In such a situation, the introduction of the digital Sand Dollar was a productive solution. Until 2018, alternative payment services were also banned on the islands in accordance with national legislation, the sector was legalized simultaneously with the introduction of the digital currency, that is, the infrastructure was being prepared simultaneously. At the initial stage, the Central Bank of the Bahamas, in order to increase confidence in the new instrument, gave commercial banks the right to maintain e-wallets, but in the future plans to use a hybrid model. Through cooperation with Mastercard and Island Pay it was possible to ensure the acceptance of the sand dollar. Digital currency is accepted for payment in all malls in the country and any stores around the world that accept Mastercard, and is used to pay salaries and pay for services. Cambodia’s central bank launched Bakong, a digital currency that the regulator said would help prevent the spread of COVID-19 by simplifying electronic payments and eliminating cash and increasing access to financial services. Only a small number of Cambodian citizens have bank accounts, but almost everyone has a smartphone. Bakong’s digital currency supports transactions with Cambodian riel and the U.S. dollar, as a large portion of payments in Cambodia are made in dollars. In this context, the development of digital payment services was a natural way to improve the efficiency of the national payment system. The Eastern Caribbean Central Bank began issuing its national digital currency, DCash, in 2021. According to the regulator, the current payment systems within the Organization of Eastern Caribbean States (OECS) are too slow, and DCash will improve the availability, security and quality of payment services.

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

209

Also in 2021, Nigeria launched its own digital currency. Nigeria is the country with one of the highest levels of cryptocurrency ownership in the world, in 2020 was the second largest bitcoin market after the U.S. with a turnover of about $600 million. Therefore, it is likely that the launch of eNaira was done to strengthen the financial stability and sovereignty of the national currency, as well as to develop a payment system that will make payments more accessible and easy. Thus, quite a large number of states are now officially using national digital currencies. Many countries are at the stage of piloting their projects, for example: Sweden, South Korea, Kazakhstan, Japan, Turkey and Jamaica, the most discussed projects are e-CNY and digital euro. The People’s Bank of China indicated its interest in retail digital currencies back in 2014. The reasons for launching the digital yuan in the official document are to improve the quality of financial services, reduce the cost of maintaining the circulation of money, as well as to meet the demand of the population for digital money. The digital yuan is based on a hybrid model: commercial banks will open wallets of different levels to users, depending on the degree of authentication. Meanwhile, users will be able to remain anonymous for microtransactions, but transactions of large amounts will be monitored to prevent e-CNY from being used for tax evasion and criminal activities. According to amendments to the People’s Bank of China Law, e-CNY is a legal means of payment [6]. Piloting of e-CNY was launched in early 2021, with the participation of six leading Chinese banks and a large number of partners and financial institutions. Already at the end of June 2021, more than 24 million wallets were opened, with more than 70 million transactions worth about 34.5 billion yuan, which is about $5.4 billion. China is definitely the leader in the scale of piloting its digital currency. Also, the People’s Bank of China is exploring offline payments and has joined the Bank for International Settlements Innovation Center’s Multiple CBDC Bridge project, which aims to jointly explore the possibilities for CBDC in multi-currency cross-border payments. This project also involves Hong Kong, Thailand, UAE. However, there is no information about its further implementation in the official document e-CNY. The European Central Bank has launched a pilot project of digital euro in 2021 and plans to finish it in 2023, in case of successful piloting and the regulator’s decision on the need to introduce a new instrument, the digital currency will be launched in 2026. The main goal of the ECB is to preserve the sovereignty of the national currency, ensuring the competitiveness of the pan-European payment system in relation to international payment services operating in Europe. Thus, at present there is an active work on the practical study of the possibilities of digital currencies of central banks as a means of payment. Equally active are preparatory and research works. It is known that the possibilities of retail digital currencies are being studied by Norway, Brazil, USA, Denmark, Iceland, Tunisia, Morocco, Kenya, the Republic of Madagascar, the Republic of Chile. Canada, Hong Kong, the Republic of South Africa and others are exploring wholesale central bank digital currency [7]. Saudi Arabia and the United Arab Emirates are working together on a cross-border project. As for the digital dollar, in January of this year, the US Federal Reserve published a report for public discussion. According to this document, the digital dollar will be a

210

O. Kuzmina et al.

means of payment, free of credit and liquidity risk, will improve remittances, expand the availability of financial services, make cross-border payments more efficient, increase the role of the dollar. However, the Fed does not yet have a consensus on the feasibility of introducing its own CSD, as it could make cryptocurrencies and stabelcoins unclaimed, and could also be a competitor to commercial banks and existing payment systems. Based on the world practice of implementation and use of digital currencies of central banks, it follows that regulators mainly aim to ensure financial stability, the development of the payment system, the promotion of the financial system, increasing the availability of financial services [8, 9]. However, the approach to the implementation of digital currencies is determined taking into account the peculiarities of different national jurisdictions. The countries with a well-developed payment services sector and banking sector approach the introduction of CSD with caution, carefully analyzing the risks and opportunities, taking care to maintain financial stability and create proper regulation, taking into account the interests of the subjects of the banking and payment services sector. And countries with underdeveloped banking sector consider CSSC as a basis for creation of payment infrastructure, controlling functions in which will belong to the central banks [10]. Taking into account the goals of regulators and global experience in the implementation of projects for the introduction of digital currencies, we can highlight some characteristics of retail digital currencies. First, the structure of digital currency should provide high speed and convenience of transactions, allowing for cheap or free payments, only in this case its introduction is advisable. The global community sees the most attractive hybrid (mediated) model, in which commercial banks service retail payments, digital wallets can be opened at intermediaries or at the Central Bank, which maintains a register of transactions, serving the reserve technical infrastructure. Second, the technological foundation must ensure the flexibility, reliability and security of digital currency. A digital currency can be based on distributed ledger technology or on a centralized database. For obvious reasons, regulators do not consider decentralized open distributed registries as the basis for digital currency. Third, in order to preserve financial stability and the traditional financial system, digital currencies of central banks must complement fiat currencies, not replace them [11]. Fourth, it is necessary for CSSCs to be supported by the legislative framework and promote innovation in payment services and encourage fair competition between subjects of the payment market. Fifth, it seems untimely for the current stage of development of digital currencies, but necessary in the future—the international legalization of digital currencies and the full use of their capabilities for cross-border payments and foreign economic cooperation. In Russia, the implementation of the central bank’s digital currency is currently directly dependent on the implementation of the Digital Ruble project, which is at the stage of testing digital currency on a limited group of users. In December 2021, the Central Bank of Russia announced the completion of a prototype platform, in the testing of which 12 banks wanted to participate: Ak Bars Bank, Alfa-Bank, Bank Dom.RF, VTB, Gazprombank, Tinkoff, Promsvyazbank, Rosbank, Sberbank, SKB-Bank, Bank Soyuz

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

211

and Transcapitalbank. In February 2022, the first successful full cycle of digital ruble transfer operations was carried out using mobile applications of two banks—customers opened digital wallets on the digital ruble platform and exchanged non-cash rubles for digital ones from their accounts, after which they made digital currency transfer operations between themselves. The remaining banks are taking organizational and technical measures to connect to the digital ruble platform, are finalizing their software complexes, and are going to join the testing as the finalization of their IT systems is completed. The digital ruble is still the same Russian ruble, but it will be issued by the Bank of Russia in digital form in addition to existing forms of money. Its units of account will not only be open in the Central Bank of the Russian Federation with the regulator’s obligations stored in special electronic wallets, but will also be able to be integrated by credit institutions into their products and services. Therefore, the introduction of the digital ruble into circulation does not carry potential crisis phenomena of the domestic banking sector. Full-scale operation of the digital ruble is planned by 2030. According to the Central Bank of the Russian Federation, the introduction of the digital ruble will not only contribute to the digitalization of the domestic economy and open access to financial services for residents from remote regions, but also minimize the risk of redistribution of funds of Russian residents into digital currencies of central banks of other countries, as well as into global stabilecoins. Moreover, a possible potential scenario is the creation of a special zone of the digital ruble beyond the reach of countries that are disloyal to Russia—since the register of transactions is accessible only to the Bank of Russia, information about the movement of money flows (as well as the monetary units themselves) will be protected by the regulator from potential restrictive measures of opposition-minded countries. Thus, Russia will increase its “immunity” to international economic sanctions, and the assets of Russian residents will become inaccessible to the introduction of a package of restrictions against them by the United States or countries of the European Union. The introduction of the digital form of the ruble in circulation will give an impetus to the digitalization of the financial sector, and the CSD system can be used to enter into smart contracts, as evidenced by the functional characteristics of digital currency. The Bank of Russia also notes that each unit of the digital ruble will have a unique identification digital code, which will allow the regulator to monitor cash flows between users. This fact denotes the similarity of the accounting system of the digital ruble and cash, which is only an additional confirmation of the fact that the digital ruble has the properties of cash currency. In addition, we know some parameters of the technological infrastructure of the digital ruble, which has already been created and which a number of Russian banks have started testing: a hybrid architecture combining distributed registry technologies and centralized components. There is also a two-tier retail model of interaction between the Bank of Russia, which is the issuer of digital currency and the operator of the digital ruble platform of financial organizations and users. Despite the fact that operations with cryptocurrency take place outside any jurisdiction, and the digital ruble is not introduced into mass circulation, determining the potential demand for central bank digital currency on the part of economic subjects does not lose its relevance. Therefore, let us assess the demand for digital ruble through a

212

O. Kuzmina et al.

related and popular phenomenon—electronic money. The choice of this form of money is due to several reasons: – the principle of electronic money as an element of a centralized system is similar to the functioning of electronic banking systems (it was their legitimization made it possible to make instant payments by bank cards); – Electronic money is a tool of cashless transactions of the stage of transition from traditional economy to digital; – Electronic money is characterized by the instant speed of transactions and reflection in electronic accounts; – Fast verification of the authenticity of the financial asset, high degree of separability, low commission; – absence of time and territorial restrictions on the operation. E-money, like today’s digital financial assets, for quite a long period of time was a servicing mechanism of settlement and payment transactions outside the legal field and statistical accounting. Only over time, regulators appreciated their usefulness, and as a result, e-money became an element of the national payment system in many countries around the world, including Russia. Nevertheless, electronic money in the Russian economy is not popular enough among the population—there is no rating and register of operators and electronic money systems officially operating in the territory of the Russian Federation, there is no provision of information on bank payment agents, there is a very limited number of outlets that carry out settlements with electronic money, there is a low awareness of financial sector employees about this type of non-cash payment instruments. But despite this, the volume of the Russian e-money market is growing every year. The analysis of the dynamics of e-money transactions over the period 2012–2021 shows that, despite the downward trend since 2015, the number of e-money operators their share in the total number of money transfer operators is increasing (see Fig. 1). Thus, since 2015, the number of e-money operators decreased by 33% (104 units in 2015 vs. 69 units in 2021), while their share in the total number of money transfer operators increased by 5.56%. The figure shows that the latter indicator was characterized by systematic growth, and since 2012 it has increased almost fivefold. Russian credit institutions have the ability to attract payment agents (subagents) for the distribution and execution of e-money transactions. However, as noted above, it is difficult to find a freely available register of electronic money systems, operators and subagents, so it is possible to assess the role of the latter only by the dynamics of the indirect indicator of the number of accounts opened by payment agents in credit institutions (see Fig. 2). Analysis of this indicator allows us to conclude that their number decreases annually. Thus, over nine years the number of accounts decreased by more than 2 times, from 32,100 accounts in 2013 to 14,000 accounts in 2021. Similarly, the volume of transactions performed through subagents’ accounts decreased—in 2021 it is equal to 736.8 billion rubles against the peak value of 1,698.6 billion rubles, reached in 2016. The reduction in the number of intermediaries causes inconvenience on the part of users during the work with electronic money systems, but the opportunities provided are still attractive. Since there is no publicly available information about the number of

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

213

Fig. 1. Dynamics of change in the number of electronic money operators (left scale) and their share in the total number of money transfer operators (right scale). Source Compiled by the author on the basis of data from the Bank of Russia.

Fig. 2. Dynamics of the number of accounts opened in credit institutions to subagents (left scale), and the volume of transactions made through them (right scale). Source Compiled by the author on the basis of data from the Bank of Russia.

users of e-money systems, the demand for this tool seems possible to assess through the dynamics of electronic means of payment (ESP), which are used to carry out transactions with electronic money (EMT). Until 2020, the change in the number of ESPs had a positive trend, reaching a sudden peak of 534.8 million units in 2019 (see Fig. 3). In 2020, however, the trend reversed, and in 2021 the figure dropped to the 2013 thresholds, from a peak of 534.8 million units to 335.2 million units. This decline is directly related to the spread of the COVID-19 coronavirus pandemic, the consequences of which were restrictive state measures, the tariff policy of e-money system operators and competition

214

O. Kuzmina et al.

from the banking sector in the form of bank cards and remote banking systems. Nevertheless, the positive trend of the dynamics of the amount of transactions using electronic means of payment for electronic money transfers did not change, but sharply increased in 2021 by 45%, which in absolute terms amounted to 842.5 billion rubles.

Fig. 3. Dynamics of change in the number of ESPs used for EDM transfer transactions (left scale) and the volume of EDM transactions (right scale). Source Compiled by the author on the basis of data from the Bank of Russia.

This significant growth of the indicator indicates an increase in the volume of the Russian e-money market and is caused by the reduction in the cost of goods, works and services that are sold over the Internet, the provision of additional services when paying with electronic money and the uniform increase in Internet penetration. The increase in Internet penetration (87.46% in 2021) shows that users prefer virtual shopping and do not want to provide information about their bank accounts in the unprotected space of the Internet. In addition, e-money settlements not only have a lower risk of transaction delays and failures in remote banking systems, but they also avoid the long wait for SMS-messages from credit institutions to confirm transactions. Also, users have begun to use remote services more frequently (as opposed to face-toface contact), as evidenced by the growing number of settlement accounts with Internet access—over nine years, the share of this indicator in the total number of accounts opened by banking organizations has quadrupled since 2013 (6.29% in 2013 vs. 32.17% in 2021). The impact of increasing Internet penetration on the increase in the number of emoney transactions is also confirmed by the data of the correlation and regression analysis. The value of the coefficient of determination (R-square) was 0.82, which indicates a fairly high quality of the model built—the dependence between the studied parameters is explained by 82% and is high (the level of correlation is 0.9). In addition, the regression showed that the adoption of the Internet by an increasing number of Russians is a fundamental factor in the popularization of electronic money payments—thus, with an increase in Internet penetration by 1 point, the volume of e-money transactions will increase by 71.3 million units.

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

215

Since 2013, the number of transactions with electronic money has increased almost six-fold—from 594.7 million units to 3,276.5 million units in 2021 (see Fig. 4). The growth rate averages 3.6%, which is explained by the increased interest in electronic money on the part of economic entities. However, the high risk of this digital asset and the ongoing tariff policy (raising the threshold of the minimum amount of electronic account replenishment without commission) restrain the use of electronic money in non-cash settlements, which is reflected in the reduction of the EDM share in such transactions. The figure shows a downward trend, from the peak in 2014, the index decreased by 3.5%, reaching 5.25% in 2021.

Fig. 4. Dynamics of change in the number of EDM transactions (left scale) and their share in the total number of non-cash payments (right scale). Source Compiled by the author based on data from the Bank of Russia.

Nevertheless, the annual dynamics of changes in the number of e-money transactions throughout the entire analyzed period has a positive trend, which, at first glance, was beyond the control of external economic events. As the time series in the first approximation seems to be increasing, let us consider the movement of the index within the calendar year for the last 4 years. The quarterly dynamics of the number of e-money transactions has a more discontinuous character. Having identified the trend lines (linear and polynomial trend—the quality of the model in this case is the highest with the polynomial equal to 5), we obtained the base from which we can measure the rapid changes of the observed series in the near future. Table 1 shows a linear trend calculation based on retrospective data on the number of e-money transactions as of the end of quarters beginning in 2017. On the basis of the calculated trend indices and seasonality coefficient, a relative and approximate forecast of the dynamics of changes in the number of e-money transactions for the next three years (up to and including 2024) was made. When extrapolating the above index, the boundaries of the confidence intervals of the forecast conducted for each quarterly value were also determined (see Table 2). The upper and lower boundaries are designed to show the amplitude of fluctuations in the actual data of future periods from the projected ones.

216

O. Kuzmina et al.

Table 1. Linear trend and quarterly seasonality of the number of e-money transactions, 2017– 2021. The quarter

1Q2017 2Q2017 3Q2017

4Q2017

1Q2018

2Q2018

Number of EDS operations, mln. units

453.8

531.6

455.9

597.4

541.0

565.4

Trend

464.31

485.95

507.59

529.23

550.88

572.52

The quarter

3Q2018 4Q2018 1Q2019

2Q2019

3Q2019

4Q2019

701.2

657.7

772.8

Number of EDS 485.7 operations, mln. Units

583.1

657.5

Trend

594.16

615.80

637.45

659.09

680.73

702.37

The quarter

1Q2020 2Q2020 3Q2020

4Q2020

1Q2021

2Q2021

Number of EDS operations, mln. units

872.3

528.5

898.1

819.8

750.4

806.5

Trend

724.02

745.66

767.30

788.94

810.59

832.23

The quarter

3Q2021 4Q2021 Seasonality

Number of EDS operations, mln. units

791.9

927.6

1st quarter 2nd quarter 3rd quarter 4th quarter

Trend

853.87

875.51

0.2970

0.3275

0.2811

0.3524

Source compiled by the author on the basis of data from the Bank of Russia

The fluctuating nature of the dynamics of the number of transactions will continue— this is due to a decline in the number of transactions with e-money in the first quarter in three of the four years analyzed. Only in 2019, the first quarter was distinguished by a small, but nevertheless growth. The index increased by 12.7%, reaching 657.5 million units. As a result, serious “drawdowns” are expected in 2023 (the index will fall to 744.61 million units in three quarters). Despite this, the overall trend of the forecast will remain positive, as the average growth rate also fluctuates between 5 and 6%, and the value of the indicator in the last forecast quarter will be 1309.58 million units due to strong growth after the drawdown in 2023. According to the results of the forecast period, the number of e-money transactions may increase by 40%, which in absolute terms equals 381.98 million transactions. Such dynamics for the period of three years is a good prospect for the development of the electronic money market, as even the “black swans” of the economy could not have a strong impact on this sector of the money market. Thus, electronic money as a means of cashless payment in the Russian Federation is becoming increasingly popular. This vector of development will continue in case of further digitalization of the financial sector and the state policy in terms of modernization of means of payments and payments. The positive dynamics of the electronic money market against the background of restraining factors shows that users of remote services

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

217

Table 2. Forecast of the dynamics of changes in the number of EDS operations until the end of 2024, mln. Units. The quarter

Trend

Projected number of EDS operations, million units

Lower confidence limit

Upper confidence limit

1Q2022

897.16

607.74

449.40

766.07

2Q2022

918.80

729.10

570.77

887.44

3Q2022

940.44

640.01

481.67

798.34

4Q2022

962.08

857.95

699.61

1016.29

1Q2023

983.72

794.43

636.09

952.77

2Q2023

1005.37

848.52

690.18

1006.87

3Q2023

1027.01

744.61

586.26

902.95

4Q2023

1048.65

912.76

754.41

1071.12

1Q2024

1070.29

1050.47

892.10

1208.83

2Q2024

1091.94

1142.94

984.57

1301.31

3Q2024

1113.58

1093.28

934.90

1251.67

4Q2024

1135.22

1309.58

1151.18

1467.98

Source compiled by the author on the basis of data from the Bank of Russia

are ready to implement and use new generation digital financial instruments, including digital currency of the central bank.

4 Discussion As noted earlier, the first successful full cycle of digital ruble transactions between users was conducted in early 2022, and the Bank of Russia will begin the second phase of testing in the fall, which involves government payments and transactions to pay for goods and services. Thus, the Digital Ruble project is at the stage of testing on a limited group of users of the created asset. This means that the introduction of the central bank’s digital currency (DCCB) into circulation will not happen in the distant future. The analysis has shown that the domestic money market is currently in a favorable environment for the implementation of the digital ruble. In addition, the emergence of the national digital currency is capable of modifying the structure of the financial system, which should correspond to the existing trends in the global economy. However, due to the insufficient level of research on the potential of digital currency and its possible benefits, it becomes problematic to quantify its impact. In this regard, it is necessary to conduct an analysis of the opportunities and risks of the introduction of digital ruble into circulation. For clarity, this study is proposed to construct a swot table, which will identify not only the strengths and weaknesses of the introduction of the digital ruble into circulation, but also take into account the risks and opportunities of such implementation.

218

O. Kuzmina et al.

First of all, it is worth highlighting the advantages and prospects of the project “Digital Ruble”, which will result in the introduction of a digital form of the national currency in the monetary circulation. First, the digital currency issued exclusively by the Central Bank will be based on distributed registry technologies, which brings a new level of settlement security, which is also supported by the storage of funds in the Bank of Russia. In addition, the transparency of transactions will also increase, as the movement of a unit of digital ruble (from issue to specific transactions and storage in users’ personal wallets) will be possible to track by means of a unique identification number/code. As a consequence, the transparency of digital money turnover and the increased level of control over the movement of funds by the state will lead to the exclusion of corruption schemes and the absence of misuse of budget funds [12]. The presence of a unique number of digital monetary unit and storage of a digital wallet in the Bank of Russia will contribute to the return of funds to the user, who became a victim of fraudsters. In addition, the storage of digital rubles in an account with the Bank of Russia is not limited in amount, which opens opportunities for those users (individuals) whose savings exceed the amount of 1.4 million rubles, guaranteed by the deposit insurance system. Second, the digital ruble is designed to simplify the financial and payment life of economic entities—this is aimed at such goals as increasing the speed of payments and simplifying transactions, as well as a gradual reduction in the cost of payment services and transfers. The implementation of the above features of the digital ruble may increase the speed of money turnover and business activity, which will not only have a favorable effect on the development of the financial market and the domestic economy, but will also accelerate this process. The separate advantages of the introduction of digital currency of the Bank of Russia in the monetary turnover can include: – Ensuring the independence of the domestic banking sector and economy from the decisions of foreign governments and their politicians through the control of monetary transactions of economic agents of Russia in the case of tightening of anti-Russian sanctions; – continued modernization of innovative processes in the field of money transfers and the payment system of the country, further digitalization of the economy; – increasing the quality of life of the population of the Russian Federation by increasing the level of accessibility and territorial coverage of financial services after the introduction of digital ruble; – The formation of a single digital space for the EAEU countries and the creation of either a common digital currency market, or a single national digital currency, which in the future by combining national advantages will increase the level of business activity in the Union and increase the competitiveness of market participants in the global arena;

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

219

– The de-dollarization will lead to the autonomy of the national economy (or the economies of the EAEU countries) and reduce its dependence on the conditions of the international economy. However, despite the strengths of the digital ruble and the noted favorable climate for the emergence of new digital assets, there are a number of issues not elaborated by the state. Below are the weaknesses of digital currency and the risks of its introduction into the monetary turnover of Russia. First, the introduction of the digital ruble will entail the transfer of funds from the correspondent accounts of commercial banks to the accounts of the Bank of Russia. As a consequence, the withdrawal of funds from the banking system may cause an increase in the cost of resources required by a credit institution, which will result in higher prices for loans issued by banks and increased fees for other banking operations. Second, the digital ruble is a new means of payment, the study and active implementation of which will require additional time. Since wallets will be opened by credit institutions, it will be the banks’ responsibility to train users to make payments with the new form of currency. In this case there is a risk of uncertainty—since there is virtually no commission for such settlements, the banks receive no benefit, and in the absence of material interest will they spend resources on quality training of users? Will they strive to make this process more convenient and more intensive, even according to the recommendations of the mega-regulator? The lack of elaboration of these issues can slow down the introduction of digital ruble in the country. In addition, a slowing factor in the development of digital technology is also the low level of financial literacy of the population, so when introducing the digital ruble in monetary circulation, a strong marketing campaign to popularize this digital asset should be conducted. Third, the low level of financial literacy is associated with disparities in the development of IT technology between the regions of the country. The digital divide is particularly noticeable outside of major Russian cities, where the population prefers to use cash. In this connection, there is a risk of low or no demand both for offline digital ruble payments and for the asset itself in principle. Fourth, the digital ruble has some of the characteristics of cryptocurrency, while Russia may become an uncontrolled market for digital currencies without further work on the bylaws: there are discrepancies in the draft law on CFA with the Federal Law “On the Securities Market” and the provisions of the Civil Code in terms of a number of concepts (digital financial assets, digital rights, crowdfunding platforms); he lack of a principled position of the Bank of Russia and the Ministry of Finance on such an instrument as digital cryptocurrency, the absence of provisions on cryptocurrency itself in the current version of the CFA draft, which should be subject to serious regulation; he absence of legislative acts (namely, a federal law) on the digital ruble specifying all points related to the issue of the ruble by the Bank of Russia, transactions in online and offline modes, provisions for the control and protection of transactions and digital wallets by the issuer, etc.;

220

O. Kuzmina et al.

he lack of elaboration of the issue of non-sanctioned emission and the possibility of double spending of the same amount, which in the future threatens the inoperability of the national monetary system. Fifth, there has been insufficient research into the security of the digital ruble platform in terms of the implementation of the privacy decision when using distributed registry technology. For example, the sharply emerged as a result of the anti-Russian sanctions by foreign IT-companies showed that there is an acute shortage of full-fledged domestic software and hardware (payment terminals, high-performance storage and processing systems of blockchain information) solutions, which confirms the lack of programs and platforms for the practical implementation of the offline mode of payments by the digital ruble. It is also worth noting that despite the declared transparency of operations with digital currency, the threat of insufficient openness and accessibility of information about it is relevant. This is evidenced, for example, by the scarce amount of statistical information on the e-money market and the limited amount of data on instruments and market participants, although e-money has been part of the country’s monetary circulation for not the first year. Nevertheless, the mentioned problematic issues are not assessed from a negative point of view, as they are characteristic of the stage of the digital ruble project where Russia is now. They are only points that require more careful thinking and high-quality implementation. If the government and the regulator carry out a well-designed series of measures in relation to the domestic CSX and implement this project, the Russian money market will open a number of such promising areas, which were mentioned above. In any case, it is difficult to assess the impact of the digital ruble, which is only at the project stage. The full scale of its impact on the financial sphere and the Russian economy will depend on the level of demand for the tool from citizens and businesses. The government has the power to influence the success of the project by eliminating the disadvantages of the digital ruble through the integration of new solutions, strengthening its advantages. Thus, the central bank’s digital currency as one of the results of the digitalization of the financial sector can give a good impetus not only to the improvement and development of the Russian money market and strengthen it on the world stage, but also to the financial stabilization of the country and improve its economic security in general.

5 Conclusion The formation of the market of digital financial assets is impossible to imagine without the process of money evolution—their transition from cash to electronic form (electronic wallet, bank account) without loss of direct impact on real economic processes [13, 14]. Further development of digital currencies will be the next stage in the evolution of money, which will require a different approach to control by society. For quite a long period of time the global economy has been in a state of uncertainty, so we should soon expect the transformation and revision of its foundations [15]. This

The Global Practice of Implementation and Use of Digital Currencies of Central Banks

221

will undoubtedly be facilitated by the emergence of new technology, which underlies the birth and emergence of the digital financial asset market. The study of domestic trends in the digital transformation of the financial market has shown that digital technology has a huge driving force of the financial sector and a vast field of application. Integration of foreign trends, analysis of the current financial market environment, and consideration of past mistakes will reveal the full potential of digital technologies. Digitalization is not something unfamiliar to Russia; on the contrary, every financial institution seeks to apply the latest trends in development and automate all aspects of internal and external business processes both to simplify its own work process and to meet customer needs. Despite this, the development of a number of trends is hampered by the unpreparedness of the legislative framework in this direction—one of the most important problems is still the issue of financial security in the transition to the digital economy. That is why it is more important than ever to develop comprehensive solutions that improve the current legislation. An analysis of trends in the development of the domestic market for digital financial assets has shown that Russia has quite a great potential to increase the volume of this sphere by stimulating private initiatives and implementing projects based on distributed registry technology. The introduction of the digital form of the ruble into circulation will give an impetus to the digitalization of the financial sector, and the CSD system can be used to enter into smart contracts, which is confirmed by the functional characteristics of digital currency coinciding with other forms of money.

References 1. Scharnowski, S.: Central bank speeches and digital currency competition. Financ. Res. Lett. 49, 103072 (2022) 2. Barrdear, J., Kumhof, M.: The macroeconomics of central bank digital currencies. J. Econ. Dyn. Control 142, 104148 (2022) 3. Mohammadreza, S.: Central bank digital currency and monetary policy. J. Econ. Dyn. Control 142, 104150 (2022) 4. Kumhof, M., Noone, C.: Central bank digital currencies—design principles for financial stability. Econ. Anal. Policy 71, 553–572 (2021) 5. Sarmiento, A.: Seven lessons from the e-Peso pilot plan: the possibility of a Central Bank Digital Currency. Lat. Am. J. Central Bank. 3(2), 100062 (2022) 6. Aysan, A.F., Kayani, F.N.: China’s transition to a digital currency does it threaten dollarization? Asia Global Econ. 2(1), 100023 (2022) 7. Balvers, R.J., McDonald, B.: Designing a global digital currency. J. Int. Money Financ. 111, 102317 (2021) 8. Fernández-Villaverde, J., Sanches, D., Schilling, L., Uhlig, H.: Central bank digital currency: central banking for all? Rev. Econ. Dyn. 41, 225–242 (2021) 9. Minesso, M.F., Mehl, A., Stracca, L.: Central bank digital currency in an open economy. J. Monet. Econ. 127, 54–68 (2022) 10. Chen, H., Siklos, P.L.: Central bank digital currency: a review and some macro-financial implications. J. Financ. Stab. 60, 100985 (2022) 11. Keister, T., Monnet, C.: Central bank digital currency: stability and information. J. Econ. Dyn. Control 142, 104501 (2022)

222

O. Kuzmina et al.

12. Zhang, T., Huang, Z.: Blockchain and central bank digital currency. ICT Express 8(2), 264– 270 (2022) 13. Li, Z., Yang, C., Huang, Z.: How does the fintech sector react to signals from central bank digital currencies? Financ. Res. Lett. 103308 (2022) 14. Bhaskar, R., Hunjra, A.I., Bansal, S., Pandey, D.K.: Central bank digital currencies: agendas for future research. Res. Int. Bus. Financ. 62, 101737 (2022) 15. Elsayed, A.H., Nasir, M.A.: Central bank digital currencies: an agenda for future research. Res. Int. Bus. Financ. 62, 101736 (2022)

Measures of Information Use Quality for Changing Activity Success in Agricultural Systems Alexander Geyda(B) St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia [email protected]

Abstract. The article presents a concept of information application for agricultural systems functioning. Literature regarding such application was analyzed. Concept model of information application for agricultural systems discussed. It explains role of information for selection and performing purposeful changes of agricultural systems functioning in changing environments. Diagrams to build models of information application for actions in systems suggested as a result. Diagrammatic models of information application for actions in systems were built using concepts of complex states and actions. These concepts use the structure of possible complex states and actions realization to fulfill complex cause-and-effect relations between substates of complex states during information application. Diagrammatic models of complex states and actions depict possible sequences of states generated by possible actions realizations in possible circumstances of agricultural system functioning, such as weather conditions, resources restrictions. Models created intended for generation of sets of possible sequences of states and actions realizations that can be realized through information application for actions in agricultural system. Keywords: Information use · Quality · Measures · Models · Method

1 Introduction Information use modelling is essential for solving a variety of problems across many areas. The number of concepts and methods suggested for estimating information use quality characteristics. Information use in agricultural systems was of particular interest to the United Nations Food and Agriculture Organization [1] and researchers alike worldwide with use of such digital means as 5G Networks, Cloud Computing [2], and Geographical Information Systems [3]. Cybernetic aspects of information use for agricultural systems are discussed in [4]. The methods and models used to research information use in agricultural systems are based mostly on expert opinions and heuristics, but not on mathematical models of systems, information, and cybernetic processes incurred when information is used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 223–232, 2023. https://doi.org/10.1007/978-3-031-34329-2_22

224

A. Geyda

To research using information in agricultural systems with use of cybernetic and system research methods, it is required to model dependencies between information use quality measures and variables, parameters of the decided problems of information use in agriculture systems.

Fig. 1. UN vision of the Role of ICT in agriculture.

Intrinsic Value of Information1 (IVI), for example—IVI of trajectories [5] is widely used characteristic to estimate results of information use. Value of Information (VOI) defined for location-based trajectory. It is stated that [6]: “quantifying trajectory with VOI helps measure how valuable, revealing, informative it is, how it is distinct, and surprising”. More specifically, the VOI defined as “the value which corresponds to how much the trajectory data helps reduce the uncertainty of estimating its owner’s locations in continuous time”. Authors use information gain (IG), defined because of branching on the decision trees [6] to quantify that reduction. Information Quality (IQ) were put into the center of the Philosophy of Information [7]. Understanding value of information5 (VoI), particularly—business value of information6 (BVI), business value of information technology [8, 9], expected value of perfect and without perfect information [10] (EVPI, EVwPI) became central part of decisions regarding information technology adoption and use in such research fields as Infonomics [11]. The central question which arises when problems solved, such that it demands various characteristics of quality of information estimation, is value of information for practice, i.e., for human action and its results—pragmatics of information. CobiT objectives and Val IT framework [12] heuristics can be used to estimate such results in practice.

Measures of Information Use Quality for Changing Activity …

225

Similar concept of Actionable Knowledge exists, which relates the knowledge obtained with practical outcome of activity. Unfortunately, there are not enough mathematical models, methods, and frameworks of needed quality to explain these measures of information quantity, quality, and information use in their relations to each other and to activity outcomes [13] quantities and quality, obtained due to information use. Among existing mathematical formalisms are Informational Structures (IS) [14] and Informational Fields (IF) [15, 16], which are declared as a novel way to explain information use and, to some extent, to explain the phenomena of conscience. These ideas were further developed [17] to suggest Integrated Information Theory [18], Artificial Intelligence of Things (AIoT) [19], actionable knowledge [20] and Digital Twins of Activity [21]. It is widely recognized that for modelling of information use it is required to put into the cornerstone21 formal aspects of human action relations with information use. Information measures [22] of pragmatic kind— i.e., ones, reflecting use of information by humans for their actions—shall be created in order to solve various kinds of practical problems related to automation, informatization and digital transformation. Similar need to put action and activity into the center of formal research of information use for practice appears in adjacent areas of human action recognition [23], of using information for flexible management [24] and of explainable [25], interpretable and actionable AI [26]. The same need appears in bold when applying Dynamic Capabilities Approach and Knowledge Based View [27] with mathematical modelling. Actionable knowledge and information value5 of various kinds8 are, as well, important concepts in today’s knowledge-based economy which experiencing digital transformation, where organizations and individuals are constantly seeking to gain a competitive advantage through the effective use of information and knowledge [17]. Within these concepts, by focusing on actionable knowledge and information value, organizations can better explain how to extract value from their information assets, and use mathematical models to drive decision-making, innovation, and growth. Unfortunately, the majority of information use concepts suggested does not formalize enough for explainable actionable mathematical models and methods to be used in the case of discrete and organizational systems, which constitute many of real-world cases. The set of models elaborated for the case of discrete and organizational dynamical systems, including complex and adaptive ones are traditionally limited to models of automata, Petri and Boolean nets [2], complexity, cellular automata, cognitive systems models [28, 29] to geometric models [13], models of discrete event simulation and system dynamics [30], models of chaotic movements [31]. The aim of the presented research is to suggest concepts for developing mathematical models of information use for system of technological and organizational nature34, such as production systems, organizational and sociotechnical systems. Such models, based on already suggested concepts, models, and methods [21] should allow closing the gap stated. They can be based on probabilistic activity models, networks of actions models, project models. Graph-theoretic models suggested allows to describe reactions of the system on information obtained and its resilience on environment changes due to information used.

226

A. Geyda

2 Modelling Method The method of modelling uses suggested concept of complex state and modelling of their transitions. Complex state refers to the complex of states of a system, including system measurable characteristics and the relations between those characteristics, as well as (complex) information about the system’s functioning and the functioning of its environment. This information includes details about the possible elementary states and substates of the system, as well as the cause-and-effect relationships between those states and any measures that apply to them. Information about the substates of a complex state can describe the past, present, and future of the system and its environment, as well as higher-level information about the effects of actions and other factors that may impact the outcomes of those actions. This information can be presented in the form of known cause-and-effect structures of possible states and known peculiarities of system and environment functioning. The various information substates play a role in creating the cause-and-effect relationships that govern the system, including those that are initiated by humans or devices in the material world. Cause-and-effect relationships within a complex state can be intentionally created by humans or with use of devices to act on the material world. When these relationships are realized between an information substate and other substates, including those realized by humans or actuators, the information is used to execute material effects. The substates of a complex state can include the state of the system, the interaction between the system and its environment, the state of an action (including information about the action), and various kinds of information substates. The structure of complex states allows for various sequences of actions, such as parallel or sequential, to be performed between the system and its environment(s). For example, two substates of a complex state can be used to initiate two actions in different sequences, with separate information substates to describe the appropriate action sequence. Each initial state must have a corresponding information substate to specify the sequence of actions. An information substate may include descriptions of the history, current state, future state, possible causal dependencies between states, as well as observations, predictions, prescriptions, and plans. These states may be obtained as a result of decision actions or information actions to obtain requirements from the environment or to measure correspondences of states to requirements. Changes in complex states, occurring as sequences of complex states, are associated with (possibly unknown) actions in the system or its environment. The action state can be the beginning or end state of an action, or the state during the fulfillment of an action. The initial complex state is the state given without the need to specify the actions that led to it. An action begins and ends with a possible complex state, and action realization consists of sequences of complex states from possible states that are realized due to certain cause-and-effect relationships and information substates. Each information substate leads to possible states and cause-and-effect relationships. Actions that differ in their information substates generally lead to different results. The results of an action can be modeled as a set of possible resulting complex states with a probability distribution defined on it, given the initial complex state and its distribution. Thus, (generally, probabilistic) mapping (S i ) → (S u ) of each possible complex state S i into the set of possible complex states S u , where s means probabilistic object, can 









Measures of Information Use Quality for Changing Activity …

227

be considered as a general model of action regarding information use. The result of an action realization is a pair of complex states: the complex state at the beginning of the action and the complex state at the end of the action. The event associated with such a pair is the event of action realization. The complex states at the beginning and end of an action can be purely informational, meaning that they pertain to the beginning or end of information processing. In this case, the action is an informational action. The use of information for system functioning takes the form of changing action realizations depending on the information substates, which allows humans or devices to realize certain cause-and-effect relationships, among others. The result of information processing is an information substate, which may take the form of prescriptions for further action or descriptions, such as diagnostic information. The beginning state of an action can be a possible state of the system or environment, as well as the end state of that same action. Complex beginning states of an action can include an information substate, which allows for changes in cause-and-effect relations depending on the information subset. The cause-and-effect relation is always associated with an action, whether it be an information action or another type of action. Actions, like states, can be complex, they can include various kinds of actions sequences of various structure (parallel, sequences) and kind (information, material). Structures of actions are formed by possible states and causal relations between them. Sequences of subsets of complex states can be synchronized in time or space. The synchronization of subsets of complex states as the beginnings and ends of various actions can cause the synchronization of action sequences. Sequences of complex states and actions form a complex tree structure. To represent possible complex states changes due to information application, and information actions fulfillment of various kinds, commutative diagrams notation was developed. The example of diagram, representing information action inP∗ to choose prescriptions for future action mode, depending on measured states of environment and system, shown in the Fig. 2. Modernization of information technology followed by its use to prepare application of action in agriculture system depicted in Fig. 3. Modernization of information technology followed by its use to prepare application of action in agriculture system following actual application of action prepared is depicted in Fig. 4. The material execution action in the schema above is “dummy” in that sense; information is not processed during the action, and action fulfillment is not monitored. To represent the monitoring of the effect execution action ai , appropriate arrows and objects shall be added (for example, below action ai arrow). Thus, the presented schemas can be further extended to represent various aspects of information use for actions in systems. This can be done by adding various directions of commutative diagrams, which represent various reflection chains fulfilled to change possible and chosen complex states introduced in the article. The introduced complex states can be further parsed into possible sequences of complex states, depending on variable information states and possible realizations of complex states as a result.

228

A. Geyda

Schemas suggested acting as probabilistic states computation categorical machine, depending “data” represented by information substates and probabilistic “computations” results.

Fig. 2. Complex states and actions related to choosing one of actions modes, based on conditions.

Such sequences of complex states obtained because of possible “computations” could allow the representation of possible scenarios of information use for actions in systems, computation of possibilities of complex states based on known probability distributions because of mappings of complex states. The sequences mentioned could include information operations and information use of various kinds, which correspond to bypasses of various parts of the suggested diagrams during “computation”. The obtained sequences of complex information and non-information states, as models of possible scenarios of information use, can be used to estimate information use quality characteristics. Furthermore, they allow describing dependencies between information used, information operations characteristics, information processing of various kinds, and results of system functioning in changed conditions. As a result, it should be possible to solve unsolved practical problems discussed in the introduction and to close the specified gap.

Measures of Information Use Quality for Changing Activity …

229

Fig. 3. Complex states and actions, related to determining new actions modes and choosing one of them, based on conditions.

Fig. 4. Schema of information preparation and fulfillment of further (prepared) effect execution action.

230

A. Geyda

3 Conclusion The article presented a concept model of information application, and diagrams to build models of information application for actions in systems. This concept model can be used for the future formalization of structures presented as algebraic systems and categories of information application for system functioning. Diagrammatic models of information application for actions in systems were built using concepts of complex states and actions. These concepts use the structure of possible complex states and actions realization to fulfill complex cause-and-effect relations between substates of complex states during information application. Diagrammatic models of complex states and actions depict possible sequences of states generated by possible actions realizations in possible circumstances. These models can be used to generate sets of possible sequences of states and actions realizations that can be realized through information application for actions in the system. The use of suggested diagrams may serve as a tool to explain other cases of information application and for the generation of possible chains of complex states and actions.

References 1. Food and Agriculture Organization of the United Nations: Information and Communication Technology (ICT) in Agriculture. FAO 2. Zhang, M., Zhang, Y., Weng, Z., Chen, Z.: Design and service effect evaluation of agricultural social service platform based on 5g and cloud computing. Wirel. Commun. Mob. Comput. 2022(22), 1–11 (2022). https://doi.org/10.1155/2022/4949242/ 3. Nishiguchi, O., Yamagata, N.: Cultivating agricultural information management system using gis technology. Hitachi Rev. 58(6), 265 (2009) 4. Strang, K.D., Che, F., Vajjhala, N.R.: Thematic analysis of agricultural government policy and operational problems. Agri. Res. 11(3), 549–556 (2022). https://doi.org/10.1007/s40003021-00588-2 5. Nguyen, K., Krumm, J., Shahabi, C.: Quantifying intrinsic value of information of trajectories. In: Meng, X., Wang, F., Lu, C.-T., Huang, Y., Shekhar, S., Xie, (eds.) Proceedings of the 29th International Conference on Advances in Geographic Information Systems, pp. 81–90. ACM, New York, NY, USA (11022021). https://doi.org/10.1145/3474717.3483912 6. Bennett, D., Bode, S., Brydevall, M., Warren, H., Murawski, C.: Intrinsic valuation of information in decision making under uncertainty. PLoS Comput. Biol. 12(7), 1005020 (2016) https://doi.org/10.1371/journal.pcbi.1005020 7. Floridi, L., Illari, P.: The Philosophy of Information Quality, vol. 358. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-07121-3 8. Tohonen, H., Itala, T., Mannisto, T., Kauppinen, M.: Towards systemic evaluation of the business value of it. In: Proceedings of the Fifth International Symposium on Business Modeling and Software Design, pp. 163–170. SCITEPRESS-Science and and Technology Publications. https://doi.org/10.5220/0005886601630170 9. Tohonen, H., Kauppinen, M., Mannisto, T.: Evaluating the business value of information technology: Case study on game management system. In: 2014 IEEE 22nd International Requirements Engineering Conference (RE), pp. 283–292. IEEE. https://doi.org/10.1109/ RE.2014.6912270 10. Bickel, J.E.: The relationship between perfect and imperfect information in a two-action risk-sensitive problem. Decis. Anal. 5(3), 116–128 (2008). https://doi.org/10.1287/deca.1080. 0118

Measures of Information Use Quality for Changing Activity …

231

11. Laney, D.B.: Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage. Routledge, USA (2017) 12. Dwivedi, Y.K., Wade, M., Schneberger, S.L.: Information Systems Theory: Explaining and Predicting Our Digital Society. Vol. 1. Integrated series in information systems, vol. 28. Springer, New York, London (2011). http://www.springer.com/gb/BLDSS 13. Russell, S.: Artificial Intelligence: A Modern Approach. Place of Publication Not Identified, Pearson Education Limited (2016) 14. Esteban, F.J., Galad´ı, J.A., Langa, J.A., Portillo, J.R., Soler-Toscano, F.: Informational structures: A dynamical system approach for integrated information. PLoS Comput Biol 14(9), 1006154 (2018). https://doi.org/10.1371/journal.pcbi.1006154 15. Wolfram Erlhagen, E.B.: Dynamic field theory (dft): applications in cognitive science and robotics. In: euCognition: The European Network for Advancement of 9 Artificial Cognitive Systems, pp. 1–10 (2009) 16. Sandamirskaya, Y., Schneegans, S., Schoner, G.: Dynamic field theory: conceptual foundations and applications to neuronally inspired cognitive and developmental robotics. In: 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 4–5. IEEE. https://doi.org/10.1109/DEVLRN.2014.6982943 17. Ma, X.: Methodology on Digital Transformation: Implementation Path and Data Platform. Management for Professionals. Springer, Singapore (2023) 18. Kalita, P., Langa, J.A., Soler-Toscano, F.: Informational structures and informational fields as a prototype for the description of postulates of the integrated information theory. Entropy (Basel, Switzerland) 21(5) (2019). https://doi.org/10.3390/e21050493 19. Ghosh, I.: Aiot: When artificial intelligence meets the internet of things: A connected future the internet of things. https://www.visualcapitalist.com/aiot-when-ai-meets-iot-technology/. Last accessed 10 Mar 2023 20. Wang, R.Y., Strong, D.M.: Beyond accuracy: What data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996). https://doi.org/10.1080/07421222.1996.11518099 21. Geyda, A., Fedorchenko, L.: Digital twins of activities: Role of information actions. In: 2022 32nd Conference of Open Innovations Association (FRUCT), pp. 102–111. IEEE. https://doi. org/10.23919/FRUCT56874.2022.9953805 22. Arndt, C.: Information Measures. Springer, Berlin (2001). https://doi.org/10.1007/978-3-64256669-1 23. De, S., Dutta, P.: Computational Intelligence for Human Action Recognition, Computational Intelligence and Its Applications, 1st edn. Chapman & Hall/CRC, Boca Raton (2020) 24. Vilela, M.J., Oluyemi, G.F.: The value of flexibility—real options. In: Vilela, M.J., Oluyemi, G.F. (eds.) Value of Information and Flexibility. Petroleum Engineering, vol. 12, pp. 229–250. Springer International Publishing, Cham (2022) 25. Saranti, A., Hudec, M., Min´arikov´a, E., Tak´aˇc, Z., Großschedl, U., Koch, C., Pfeifer, B., Angerschmid, A., Holzinger, A.: Actionable explainable AI (axai): A practical example with aggregation functions for adaptive classification and textual explanations for interpretable machine learning. Mach Learn Knowl Extr 4(4), 924–953 (2022) 26. Linkov, I., Galaitsi, S., Trump, B.D., Keisler, J.M., Kott, A.: Cybertrust: From explainable to actionable and interpretable artificial intelligence. Computer 53(9), 91–96 (2020). https:// doi.org/10.1109/MC.2020.2993623 27. Pettigrew, A., Thomas, H., Whittington, R.: Handbook of Strategy and Management. SAGE Publications Ltd., United Kingdom (2006). https://doi.org/10.4135/9781848608313 28. Gros, C.: Complex and Adaptive Dynamical Systems. Springer, Berlin (2011). https://doi. org/10.1007/978-3-642-04706-0 29. Easton, R.W.: Geometric Methods for Discrete Dynamical Systems. Oxford Engineering Science Series, vol. 50. Oxford University Press, New York (1998)

232

A. Geyda

30. Brailsford, S., Churilov, L., Dangerfield, B.: Discrete-Event Simulation and System Dynamics for Management Decision Making. Wiley series in operations research and management science. Wiley, Chichester, West Sussex, United Kingdom (2014) 31. Martelli, M.: Introduction to Discrete Dynamical Systems and Chaos. Wiley-Interscience Series in Discrete Mathematics and Optimization. Wiley, New York, Chichester (1999)

Author Index

A Akimova, Olga

45

B Babaeva, Alina 189 Bank, Sergey V. 9 Brizhak, Olga 60 C Chekhovskaya, Irina 73 D Demina, Irina 99 E Ezangina, Irina 73 F Fedorov, Andrei 25 Frolov, Daniil 45 Frolova, Veronika 16 G Galkin, Denis 112 Geyda, Alexander 223 Glazyeva, Sofia 133 Gordeeva, Elena 197 Gordova, Marina 99 Guseva, Irina 163 I Izzuka, Tatiana B. 9 K Kamolov, Sergey 133 Kharin, Alexander 33 Kharlamova, Ekaterina 73 Khashimi, Matiar Rakhman 25 Kirilichev, Kirill 25 Konovalova, Maria 206

Koshkin, Andrey 25 Kozhukhova, Margarita Krivtsov, Artem 173 Kuzina, Elena 83, 92 Kuzina, Marina 92 Kuzmina, Olga 206 L Limanchuk, Leonid

45

83

M Mayer, Natalya S. 9 Mikhalenok, Natalya O. 9 Mnatsakanyan, Albert 33 Muzalev, Sergei 99 Muzaleva, Tatiana 99 N Nadolinsky, Pavel 92 Nazarov, Evgeny 83 Nikiforova, Elena 125 Nikiforova, Natalia 16 O Ogiy, Oksana 140 Osipov, Vasiliy 140 P Pliva, Elena 163 Podtopelny, Vladislav 189 Polyakov, Ruslan 60, 125 Protasova, Sofia 25 Putikhin, Yuri 16 S Sazonov, Sergey 73 Schneider, Olga 3 Shlychkov, Dmitry 16 Shnaider, Viktor V. 9 Snytnikov, Alexey 154

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Polyakov (Ed.): EcoSystConfKlgtu 2023, LNNS 705, pp. 233–234, 2023. https://doi.org/10.1007/978-3-031-34329-2

234

Author Index

Solovey, Marina 154 Stepanova, Tatyana 3, 206

V Vasilenko, Marina

T Tagilceva, Julia 83, 92 Tarasova, Tatiana 173

Z Zelenina, Larisa

83, 92

154