Landmarks for Spatial Development: Equality or Differentiation (Contributions to Regional Science) 3031373480, 9783031373480

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Contributions to Regional Science

Stanislav Martinat · Vikas Kumar · André Torre · Yuliya Lavrikova · Evgeny Kuzmin   Editors

Landmarks for Spatial Development Equality or Differentiation

Contributions to Regional Science

This book series offers an outlet for cutting-edge research on all areas of regional science. Contributions to Regional Science (CIR) welcomes theoretically sound and empirically robust monographs, edited volumes and handbooks from various disciplines and approaches on topics such as urban and regional economics, spatial statistics, spatial econometrics, geographical information systems, migration analysis, land use and urban development, urban and regional policy analysis, interindustry analysis, environmental and ecological analysis, and related fields. All books published in this series are peer-reviewed.

Stanislav Martinat · Vikas Kumar · André Torre · Yuliya Lavrikova · Evgeny Kuzmin Editors

Landmarks for Spatial Development Equality or Differentiation

Editors Stanislav Martinat Department of Social, Economic and Geographical Sciences James Hutton Institute Aberdeen, UK

Vikas Kumar Faculty of Business, Law and Social Sciences Birmingham City University Birmingham, UK

André Torre Department of Economics University of Paris-Saclay Paris, France

Yuliya Lavrikova Institute of Economics of the Urals Branch of the Russian Academy of Sciences Ekaterinburg, Russia

Department of Economics INRAE (National Research Institute for Agriculture, Food and the Environment) Paris, France Evgeny Kuzmin Department of Regional Industrial Policy and Economic Security Institute of Economics of the Ural Branch of the Russian Academy of Sciences Ekaterinburg, Russia

Contributions to Regional Science ISBN 978-3-031-37348-0 ISBN 978-3-031-37349-7 (eBook) https://doi.org/10.1007/978-3-031-37349-7 © 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

About This Book

In the context of increasing inter-regional differentiation, many researchers note the growing threat of space distortion, fragmentation and “falling out” from economic processes of various territories categorized as economic outsiders (exacerbating existing social problems). At the same time, the advanced development of individual territorial elements can positively transform large-scale systems, in turn contributing to an increase in the performance of lagging territories. Due to such a two-way approach to understanding the essence of inter-regional inequality, the choice of parameters for the optimal spatial organization can be considered as a controversial process, bringing up the issue of determining the guidelines for sustainable development of regions and cities. The book includes 8 chapters on the topic of uneven spatial distribution of territory resources. The authors analyze the features of the localization of assets, paying attention to both the manifested factors and conditions determining the specificity of the current spatial organization. Based on the multivariate analysis, gravity models, clustering and index method, well as the assessment of concentration parameters, researchers propose various approaches to the systematization of territorial units, paying special attention to the peculiarities of their economic structure, resource diffusion barriers and specifics of their development. The obtained results indicate the need for a differentiated approach to the choice of guidelines for the transformation of the socio-economic space, allowing the researchers to propose various transformation models for differing regions. Thus, the book presents spatial organization models for different regional economies, describes a differentiated approach to the formation of local models providing special conditions for investors in order to achieve the sustainable development goals and reduce inequality.

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About This Book

The research results and authors’ conclusions confirm the lack of an unambiguous answer to the question of the optimal balance between the advantages of polarized development and the need to avoid significant inter-regional disparities. At the same time, the book offers various solutions to differentiate territories, distinguishing different space elements, determine the most suitable transformation options, and reform regional and clustering policies. The obtained results may be of interest to both researchers and experts in the field of territorial development management.

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arina Suvorova

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Interregional Migration: Reexamination of Population Redistribution in Russia at the Late Soviet Period . . . . . . . . . . . . . . . . . . . . Kazuhiro Kumo

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Clustering of Small and Medium-Sized Cities in Russia Based on the Assessment of Knowledge Spillovers Localization . . . . . . . . . . . . . . Tatyana Melnikova

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Hidden Single-Industry Towns in Transition . . . . . . . . . . . . . . . . . . . . . . . . . Irina Turgel, Aksanat Panzabekova, and Irina Antonova

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The Level of Urbanization of the Regions of Kazakhstan: Assessment by the Index Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aksana Panzabekova, Lidiya Bekenova, and Aksaule Zhanbozova

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Differential Approach to Shaping Models of Priority Socio-Economic Development Territories . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gulia Galiullina

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Models of Spatial Organization of Regional Economies . . . . . . . . . . . . . . . 113 Yulia Lavrikova and Arina Suvorova Overcoming Interregional Economic Disparities in Russia Through Implementation of Resource Projects . . . . . . . . . . . . . . . . . . . . . . . 135 Valery Kryukov, Nikita Suslov, and Yakov Kryukov Industrial Districts and Industrial Clusters. Conceptual Approaches from Italian and Eurasian Experiences . . . . . . . . . . . . . . . . . . . 169 David Celetti, Larissa Bozhko, and Raf Avetisyan

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About the Editors

Stanislav Martinat is a Researcher (Academic), a human geographer, currently working at the Department of Social, Economic and Geographical Sciences of the James Hutton Institute, Aberdeen, UK. Formerly COFUND (MCSA) Research Fellow (Cardiff University) and a Fulbright Fellow (Arizona State University). His focal points of research interests are in rural geography with an emphasis on implications of new spatial phenomena (like brownfields, sustainable bioenergies) affecting various types of contemporary rural space. Stanislav Martinat particularly interested in social and environmental dynamics that have been recently occurring in the East-Central Europe. Dr. Vikas Kumar is an Associate Dean for Research, Innovation and Enterprise, Faculty of Business, Law and Social Sciences, Birmingham City University, Birmingham, UK. He serves on the editorial board of around six international journals including International Journal of Supply Chain and Operations Resilience, International Journal of Service, Economics, and Management and International Journal of Manufacturing Systems. Prof. Kumar’s current research focus is on sustainable supply chain management and Supply Chain 4.0. His other research interests include supply chain improvement, short food supply chains, green supply chain, process modelling, innovation in SMEs, operations strategy, and service supply chains. André Torre is Research Director at France’s National Institute for Agricultural Research (INRA), works at AgroParisTech and heads the Paris-Saclay Humanities and Social Sciences Center (MSH Paris-Saclay). By virtue of his research in the social sciences, poised at the intersection of industrial and spatial economics, he has made a name for himself as an expert on territorial development from the standpoint of proximity and territorial conflicts. Since 1993, he has published ten books and more than 100 articles on the subject, the most often cited being “Proximity and Localisation”, co-authored with Alain Rallet. As director of research programs “for and about regional development”, André Torre coordinates 450 researchers working on 33 multidisciplinary projects involving ten regional councils. His other job is

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About the Editors

at Paris-Saclay, where he has directed the Humanities and Social Sciences Center (MSH Paris-Saclay) since 2018. Dr. Yuliya Lavrikova is Director of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences. She is a member of the international Eurasian business and economic community (USA, Netherlands, Turkey), the Association of Russian geographers and social scientists. Dr. Lavrikova has over 150 scientific publications, including 31 monographs. Research interests: theoretical and methodological aspects of the strategy of balanced territorial and sectoral development, strategic directions for the development of sectoral complexes, institutions of spatial development, sustainable and safe socio-economic development of regions. Evgeny Kuzmin is Researcher (Academic) of the Department of Regional Economic Policy and Economic Security of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences. He is a reviewer of high impact international journals including Journal of Cleaner Production (Elsevier), Entrepreneurship and Sustainability Issues, etc. He has over 150 published scientific papers. Mr. Kuzmin has participated in the implementation of more than 10 research projects supported by grants from the Russian Foundation for Basic Research, the Russian Humanitarian Science Foundation, the Russian Science Foundation and the Ministry of Education and Science of Russia. His research interests are risk, uncertainty, economic crises, sustainability, public–private partnerships, investments, business planning, industrialization, industrial policy, industry markets, modeling, economic growth and development, entrepreneurship, and business activity.

Introduction Arina Suvorova

Abstract This chapter presents the motivation for the research demonstrated in this book and provides an overview of the contributions of each chapter. Keywords Regional economy · Spatial equality · Spatial differentiation

In the context of increasing inter-regional differentiation, many researchers note that the growing threat of space distortion, fragmentation (Apostolopoulou, 2021; Senoret et al., 2022) and “dropping out” from economic processes of various territories categorised as economic outsiders (Banski et al., 2020; Huang et al., 2020). On the other hand, the advanced development of individual territorial entities can positively transform large-scale systems, in turn contributing to an increase in the performance of lagging territories (Xu & Zhang, 2021). Due to such a two-way approach to understanding the essence of inter-regional inequality, the selection of parameters for an optimal spatial organisation can be considered as a controversial process; thus, the authors of the book are interested in examining the issue of uneven spatial distribution of regional resources and determining its consequences. While modern researchers quite thoroughly assess how the localisation of assets affects socio-economic development, the majority of such works focus on measuring the extent of differentiation (Chen et al., 2020; Furkova, 2021; Wang et al., 2021) and identifying the reasons and consequences of imbalances caused by the advanced development of individual territorial elements (Cellmer et al., 2021; Liu & Zhang, 2021; Sun et al., 2021). The issues of spatial development are also considered from the perspective of inter-regional inequality; however, most researchers pay attention to the advantages brought by the formation and development of growth poles. The prospects of polarised development, presented in the works of Perroux (1988), Boudeville (1966), Pottier (1963), were highlighted in the agglomeration effects studies of Romer (1986), Fujita et al. (1999), cluster theory of Porter (2008), leading A. Suvorova (B) Institute of Economics of the Ural Branch of the Russian Academy of Sciences, 29 Moskovskaya St., Ekaterinburg 620014, Russian Federation e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_1

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to the appearance of publications considering spatial economic imbalances as a condition for ensuring the economic growth of the system (Gordon & Kourtit, 2020; Li et al., 2019; Yu & Liu, 2021). It is, however, obvious that the concept of polarised development has its limitations. Modern scientific literature (Batabyal & Nijkamp, 2019; Manduca, 2019; Mikhaylova & Gorochnaya, 2020) pays attention to the difficulties in narrowing the development gaps between economic leaders and outsiders, as well as to socio-economic problems caused by imbalances in the context of the advanced development of individual territorial elements. Thus, it would be wrong to conclude that the advanced development of individual territories (regions, agglomerations, municipalities) is effective. It is better to focus on identifying the optimal balance between the equality and differentiation aspects of spatial development. To this end, the authors analyse the features of the localisation of assets, paying attention to both the manifested factors and conditions determining the specificity of the current spatial organisation. Based on the multivariate analysis, gravity models, clustering and index method, as well as the assessment of concentration parameters, researchers propose various approaches to the systematisation of territorial units, paying special attention to the peculiarities of their economic structure, resource diffusion barriers and quality of life parameters. In this chapter reveals the distribution of migration flows across the country: changes in the parameters of population localisation (that can be seen as both the labour force and the consumer of the results of economic activity) significantly affect the spatial organisation of regions. Chap. 2 describes barriers preventing the free movement of assets (special attention is paid to knowledge dissemination barriers) as well as presents a new approach to the classification of territorial units (based on the obstacles to the reorganisation of economic space rather than on development features). Chapter 3 considers single industry towns as a specific territorial unit, determining the features of their localisation in the country and demonstrating its influence on the complex territorial system. Chapter 4 assesses the impact of the urbanisation factor on the development of the region. The obtained results indicate the need for a differentiated approach to the choice of guidelines for the transformation of the socio-economic space, allowing the researchers to propose various transformation models for differing territories. Chapter 5 describes a differentiated approach to the formation of local models providing special conditions for investors. Chapter 6 presents the spatial organisation models for different regional economies (depending on the available resources) and suggests strategic directions for their spatial development. Chapter 7 demonstrates the possibility of using large-scale resource projects for increasing inter-regional connectivity and reducing imbalances. Chapter 8 describes the possibilities and features of using the cluster approach to address the issues of optimal spatial organisation. The research results and authors’ conclusions confirm the lack of an unambiguous answer to the question of the optimal balance between the advantages of polarised development and the need to avoid significant inter-regional disparities. At

Introduction

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the same time, the book offers various solutions to differentiate territories, distinguish different space elements, determine the most suitable transformation options and reform regional and clustering policies.

References Apostolopoulou, E. (2021). Tracing the links between infrastructure-led development, urban transformation, and inequality in China’s belt and road initiative. Antipode, 53(3), 831–858. https:// doi.org/10.1111/anti.12699 Banski, J., Wesolowska, M., & Loboda, K. (2020). Disappearing villages–Identification and analysis of selected socioeconomic features. Przeglad Geograficzny, 92(2), 175–189. https://doi.org/10. 7163/PrzG.2020.2.1 Batabyal, A., & Nijkamp, P. (2019). The magnification of a lagging region’s initial economic disadvantages on the balanced growth path. Asia-Pacific Journal of Regional Science, 3(3), 719–730. https://doi.org/10.1007/s41685-019-00118-7 Boudeville, J. (1966). Problems of regional economic planning. The University Press. Cellmer, R., Cichulska, A., & Belej, M. (2021). The regional spatial diversity of housing prices and market activity—evidence from Poland. Acta Scientiarum Polonorum, Administratio Locorum, 20(1), 5–18. https://doi.org/10.31648/ASPAL.6111 Chen, Q., Du, M., Cheng, Q., & Jing, C. (2020). Quantitative evaluation of spatial differentiation for public open spaces in urban built-up areas by assessing SDG 11.7: A case of deqing county. ISPRS International Journal of Geo-Information, 9(10). https://doi.org/10.3390/ijgi9100575 Fujita, M., Krugman, P., & Venables, A. J. (1999). The Spatial Economy: Cities, Regions, and International Trade. The MIT Press. Furkova, A. (2021). Simultaneous consideration of spatial heterogeneity and spatial autocorrelation in European innovation: A spatial econometric approach based on the MGWR-SAR estimation. Review of Regional Research, 41(2), 157–184. https://doi.org/10.1007/s10037-021-00160-z Gordon, P., & Kourtit, K. (2020). Agglomeration and clusters near and far for regional development: A critical assessment. Regional Science Policy and Practice, 12. https://doi.org/10.1111/rsp3. 12264 Huang, X., Huang, X., Liu, M., Wang, B., & Zhao, Y. (2020). Spatial-temporal dynamics and driving forces of land development intensity in the Western China from 2000 to 2015. Chinese Geographical Science, 30(1), 16–29. https://doi.org/10.1007/s11769-020-1095-2 Li, Z., Ding, Ch., Niu, Y. (2019). Industrial structure and urban agglomeration: Evidence from Chinese Cities. The Annals of Regional Science, 63(1), 191–218. https://doi.org/10.1007/s00 168-019-00932-z Liu, L., Zhang, M. (2021). The impacts of high-speed rail on regional accessibility and spatial development-updated evidence from China’s mid-Yangtze river city-cluster region. Sustainability (Switzerland), 13(82). https://doi.org/10.3390/su13084227 Manduca, R. A. (2019). The contribution of national income inequality to regional economic divergence. Social Forces, 98(2), 622–648. https://doi.org/10.1093/sf/soz013. Mikhaylova, A., Gorochnaya, V. (2020). Social effects of agglomeration. An assessment of intraregional disparities in the south of Russia. Journal of Settlements and Spatial Planning, 11(2), 113–126. https://doi.org/10.24193/JSSP.2020.2.05 Perroux, F. (1988). The pole of development’s new place in a general theory of economic activity. In: B. Higgins, & D. Savoie (Eds.), Regional economic development: Essays in honour of François Perroux. Unwin Hyman. Porter, M. (2008). On competition. Harvard Business Review Press. Pottier, P. (1963). Axes de Communication et Développement Economique. Revue Économique, 14, 58–132.

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Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037. Senoret, A., Ramirez, M. I., & Rehner, J. (2022). Employment and sustainability: The relation between precarious work and spatial inequality in the neoliberal city. World Development, 153. https://doi.org/10.1016/j.worlddev.2022.105840 Sun, Y., Chang, Y., Liu, J., Ge, X., Liu, G.-J., & Chen, F. (2021). Spatial differentiation of nongrain production on cultivated land and its driving factors in coastal China. Sustainability (Switzerland), 13(23). https://doi.org/10.3390/su132313064 Wang, R., Xia, B., Dong, S., Li, Y., Li, Z., Ba, D., & Zhang, W. (2021). Research on the spatial differentiation and driving forces of eco-efficiency of regional tourism in China. Sustainability (switzerland), 13(1), 1–231. https://doi.org/10.3390/su13010280 Xu M, Zhang Z (2021) Spatial differentiation characteristics and driving mechanism of ruralindustrial Land transition: a case study of Beijing-Tianjin-Hebei region, China. Land Use Policy, 102. https://doi.org/10.1016/j.landusepol.2020.105239 Yu, Z., & Liu, X. (2021). Urban agglomeration economies and their relationships to built environment and socio-demographic characteristics in Hong Kong. Habitat International, 117. https:// doi.org/10.1016/j.habitatint.2021.102417

Interregional Migration: Reexamination of Population Redistribution in Russia at the Late Soviet Period Kazuhiro Kumo

Abstract Discourses over interregional migration at the time of the Soviet era have shown that, in the late Soviet era, the effects of incentive mechanisms including national investment became limited. However, the population influx was continuously seen in Far East or Extreme North regions even at the very end of the Soviet period, suggesting the possibility of effective governmental management on geographical redistribution of population. This chapter confirmed the effectiveness of the governmental control on population migration in the late Soviet era, using newly available data of migration matrix which identifies origin and destination of population flows. Region-based panel data analyses revealed that the analytical unit utilized in previous studies may involve problems so that the effect of various factors could not be accurately grasped. This shows the necessity of further verification of the results that have been obtained during the Soviet era. Additionally, the limitations and possibilities of governmental control on population re-distribution in a country are suggested. Keywords Migration · Soviet · Russia · Origin-to-destination matrix

1 Introduction The aim of this study is twofold. First, a survey of studies on the interregional population migration during the Soviet Union era was conducted, focusing migration management systems as well. Second, analysis of the factors affecting migration patterns was attempted by using newly obtained data. Considering the critical importance of regional labor allocation in implementing the centrally planned economy, it was obvious that the idea of optimum production reallocation was emphasized in the former Soviet Union. However, it is undeniable that only a limited number of analysis on migration factors were performed during the Soviet era, even though numerous K. Kumo (B) Institute of Economic Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8603, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_2

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arguments were made on the normative aspects of planning regional developments (Kumo, 2003). In the former Soviet Union under the socialist regime, studies on population migration were mostly conducted based on descriptive statistics and quantitative method was rarely used. One of the main reasons behind this was the fact that even for domestic researchers the access to detailed quantitative data was limited during the Soviet era.1 Another point was that most of the studies conducted by Soviet researchers were dominated by policy reviews or normative assertion. Some western researchers, however, utilized population census data and they offered quantitative analytical results in some aspects (Mitchneck, 1991). Population migration studies in the Soviet Union were heavily inclined to the normative description to realize socalled optimum population redistribution, rather than to examine the factors determining migration patterns. Various political approaches were taken to implement population distribution patterns in accordance with the governmental aims. On the evaluation of the effectiveness of such measures, however, there are both arguments for and against on the issues. This chapter, using newly obtained closed materials of the Soviet era, re-examines the factors affecting population migration under Soviet regime and shows the points which follow the arguments in previous studies, as well as those which deny the results of researches conducted during the Soviet period. Focus is given to the points whether or not (1) one could see the population redistribution patterns in accordance with the Soviet government development priority and (2) political incentives implemented during the Soviet era worked effectively. The main task of this chapter is to re-evaluate the effectiveness of migration control during the Soviet era, which became possible for the first time with the help of the access to internal materials of the Russian statistical office. Additionally, the analysis to be presented in this chapter may shed light on the limitations and the possibilities of governmental controlling power on population re-distribution in a country. The chapter is organized as follows. The next section argues population migration control systems of the Soviet Union, and the discussion on the effectiveness of governmental migration management presented in previous studies is examined. In Sect. 3, the data this study obtained and the approaches taken will be explained, followed by the analytical results and their interpretation. The final section concludes and the tasks ahead will be noted. Interregional population migration has been clearly one of the main issues in the fields of regional science and geographic research, and enormous analyses have been made on developing countries or western countries (Greenwood, 2019). On the contrary, it must be said that only a very limited number of researches were made in the Soviet Union. This fact was surprising, given that the Soviet Union had emphasized the importance of optimal regional resource allocation in order to implement the planned economy.

1

Russian Government Archive of Economics RGAE website, < http://rgae.ru/arkhiv-rgaeistoriyaarkhiva.shtml > (“The history of RGAE”), accessed on June 18, 2018.

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The limited access to the data has resulted in the limited number of previous studies. The majority of studies on population migration during the Soviet era were occupied by normative ones which discussed about optimal labor distribution among regions. Researches on causes and effects using statistical methods were limited and most of them were conducted by researchers in Western countries based on population census data. Main issues examined in such researches were, for example, effectiveness of implemented population re-allocation policy and the evaluation of the effectiveness of development priority policies led by the central government. The points which were discussed during the Soviet period through limited information can be verified by newly obtainable data in some cases. Such verification has been, however, rarely conducted on the issue of interregional migration in Russia: hence, this chapter tries to fill the gap in the field. Before that, a short review of migration control system in the Soviet Union and the discussion made by previous studies follows.

2 Population Redistribution in the Soviet Union 2.1 Population Migration Management as a System Migration between different regions in the Soviet Union was managed and recorded using domestic passports and the residence permit (Propiska) system (Matthews, 1993). The domestic passport system was introduced in 1932, approximately 10 years after the establishment of the Soviet Union in 1922.2 Passports, which were required for domestic movement, were distributed to urban residents. Domestic passports served as domestic personal identification cards, and presented the date of birth, place of birth, familial relationships (spouse and children), place of residence, work record, military service record, etc. of the holder. The residence permit (Propiska) system was introduced for the purpose of restricting residence in cities. At the earliest stage, it was introduced mainly in large cities such as Moscow, Leningrad (name at the time), Kiev, and Minsk, but later the residence permit system was expanded to cover almost every city.3 People in the Soviet Union needed to carry a domestic passport to move into a city, and they also had to obtain a residence permit in their destination. When moving to a rural village, a residence permit was sometimes not required, and residence registration based on a residence permit was an essential condition for obtaining livelihood benefits such as pension benefits, medical services, and, depending on 2

“Establishment of unified passports for the Soviet Union and obligation to obtain a residence permit,” decision dated December 27, 1932 by the Central Executive Committee and the Council of People’s Commissars of the Soviet Union. 3 Krechetnikov, A., Propiska: neperevodima i neistrebima, BBC Moscow Website, December 11, 2013. < https://www.bbc.com/russian/russia/2013/12/130304_russia_registration_history.shtml>, accessed on June 30, 2018 (in Russian).

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the situation, rations. The system allowed the government to gauge interregional population migration. However, it should be noted that in the Soviet Union, there was no law providing for penalties for failure to complete residence registration. That being said, failure to register resulted in numerous disadvantages in terms of receiving services for residents, pension benefits, medical services, and so on. So it can be said that there was a strong incentive for people to register (Matthews, 1993). Attention needs to be paid to the fact that it was not until 1974 that rural residents were issued with passports,4 and that until them it was generally not permitted for rural residents to move to cities. And until that time, it is likely that the government was not adequately aware of the extent of “rural area to rural area” migration and “city to rural area” migration.5 In other words, it seems that data specifying both the origin and destination were limited to that pertaining to migration between different cities. It was therefore difficult to gauge the situation, and this significantly restricted possibilities for research. In fact, apart from one or two exceptions, no quantitative analysis of interregional population migration within the Soviet Union that was based on records for each year was performed at the time the Soviet Union existed. And even this analysis only dealt with intercity migration or with data that broke down the entire Soviet territory into 19 regions (Mitchneck, 1991). The bulk of the analysis was based on statistics at the level of the republics that comprised the Soviet Union. In other words, the Russian Republic, which covered an area more than 45 times that of Japan, was treated as a single region, and it has to be said that this was woefully inadequate as data for analyzing the actual situation. Despite facing such limitations, researchers at the time explored the potential for analysis using data such as lifetime migration data from population censuses or data on net migration data for each region that did not specify origins and destinations. However, such studies were almost completely limited to Western countries. As stated earlier, in the Soviet Union most of the studies comprised normative discourse or constituted policy reviews. In the next subsection, the author will provide an overview of previous research on interregional population migration in the Soviet Union that was conducted in the Soviet Union itself and in Western countries.

2.2 Insights from Previous Research The population redistribution policy that was advocated at the beginning of the Soviet era and was driven by policy objectives had a major impact on the geographical distribution of population, something that has been discussed heavily. It left its mark 4

“Rules and approvals concerning the passport system in the Soviet Union,” Decision No.677, dated August 28, 1974, by the Council of Ministers of the Soviet Union. 5 The author examined interregional population migration matrixes (paper documents) from the 1950s to the 1960s at the Russian State Archive of the Economy, and found that there were only documents on migration between cities. There were no statistics at all recording origins and destinations for other forms of migration.

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most visibly in Siberia and the Far East, and especially in the “Far North.6 ” For example, Perevedentsev (1966) describes how numerous cities were constructed in the Far North immediately after the establishment of the Soviet Union until the end of the Second World War. With regard to this, explanations have been seen stating that the cause was the high wages set by the government in the region during the Soviet era, but these explanations are inadequate. As Kalemeneva (2019) has detailed, we in the post-Soviet era are aware that the major underlying factor was city construction by prisoners from the gulags. The impact of the Second World War on the change in population distribution from before the war until after the war cannot be overlooked. The effect is widely known, and as Rodgers (1974) pointed out, during the war, which was partially fought in European Russia, numerous factories and workers relocated to other regions centered on the Urals. Furthermore, the massive loss of population that occurred during the war also left a big mark on the regional distribution of the Soviet population. This can be seen as follows: Fig. 1a shows that the sharp decline in the industrial output of the Northwest (including Leningrad [name at the time]) that occurred in conjunction with the start of the war between Germany and the Soviet Union failed to recover even after the war and that the Urals, which had rapidly increased their share of industrial output during the war maintained a much higher share of industrial output than they had had prior to the war, though it did decline. Figure 1b, meanwhile, illustrates that the number of workers in the Urals increased more or less continuously from the middle of the war and that the North was severely affected by the war. Development policy for remote regions in the Soviet Union involved the simultaneous tackling of two tasks: evening out the level of economic development of different regions, and building a core infrastructure to satisfy the need for national defense resulting from the clash with the United States, which was a neighboring country for the Far North. As a result, investment in the East was conducted on a large scale relative to the population, and Mitchneck (1991) pointed out that this seemed to be followed by an observable increase in the populations of the Far East and Siberia. In fact, Nykanen (2018) described how this led to a decline in the degree of centralization of industrial output when viewed at the level of the republics comprising the Soviet Union. Regarding this situation, Rodgers (1974) argued that the government was dominant in determining the direction of interregional population migration in the Soviet Union, and that it occurred in an organized fashion. In addition to the fact that population migration data were difficult to obtain, if population migration patterns were determined based on policy, it can be said to be hardly surprising that little interest developed in analyzing the factors behind interregional population migration. With the death of Stalin in 1953 and criticism of Stalin being voiced by Khrushchev in 1956, the scale of regional development carried out by gulag laborers declined 6

Regions located above in the Arctic and regions with similarly harsh living conditions. These regions received favorable treatment in the distribution of goods and wage conditions. See “Rules concerning benefits for persons working in the far north of the Russian republic,” decision dated January 1, 1932 by the All-Russian Central Executive Committee of the Russian Soviet Federative Socialist Republic Council of People’s Commissars.

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a

Far East

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

East Siberia West Siberia Urals North Caucasus Volga-Vyatka Central Black Earth Volga-Vyatka Central North-West North

b 12000000

Far East East Siberia

10000000

West Siberia Urals

8000000

North Caucasus Volga-Vyatka

6000000

Central Black Earth 4000000

Volga-Vyatka

2000000

North-West

Central

North 19 40 19 42 19 43 19 44 19 45 19 46 19 47 19 48 19 49 19 50

38 39 19

19

19

37

0

Fig. 1 a Percentage share of gross regional industrial Products before and after the World War II by Region in Russia (%). b The Number of Workers before and after the World War II by Region in Russia. (person). Sources 1937–1948: RGAE (rossiiskii gosudarstvennii arkhiv ekonomiki), Fond 1562, Opisi 329, Ed.Khr. 2903; 1949–1950: RGAE, Fond 1562, Opisi 329, Ed.Khr. 4145

sharply.7 Measures that were instituted aggressively to take the place of forced labor included offering high wages in remote regions and allocating a certain proportion of jobs to fresh university graduates. Kumo (2003) pointed out that the incentive provided by the relatively high wages contributed to attracting the workforce needed to implement the development policy for remote regions, while Samorodov (1991) 7

According to official Soviet documents, total gulag labor was predicted to peak at more than 2.5 million people in 1950. The figure remained higher than 1.32 million people in 1954, but had declined by more than a million people compared with 1953 (GARF, F-R9414, Op.1, D.1319, L.1-1ob., 4-4ob., 7-7ob., 10-10ob., 18-18ob., 21-21ob).

Interregional Migration: Reexamination of Population Redistribution …

11

demonstrated that the allocation of jobs to university graduates had the same effect. Furthermore, Mitchneck (1991) describe how targeted investment as an incentive for labor mobility in remote regions occurred, and that in conjunction with this population inflow occurred in the regions that were subject to this investment. The same writers also explained that, in contrast, comparatively well-developed regions such as Russia and Ukraine experienced population outflows. These writers contend that even without coercion, it was possible to control population flows to some degrees using economic incentives. However, Orttung et al. (2020) pointed out there are limits to the management of interregional population migration. He described the volatility of population inflows and outflows in the Far North and argued that it was difficult to ensure a stable labor force. Vorob’yev (1977) theorized that the following factors also affected population migration in the Soviet Union, and this was evident in the later years of the Soviet Union: people move based on differences in factors such as climate conditions and living standards, cities attract people, and there are other factors, such as the level of transportation infrastructure and the living environment, that typically influence interregional population migration. Ball and Demko (1978) claimed that the fact that the Russian Republic, which was relatively well-developed, experienced a population outflow during the 1960s provided corroboration for it experiencing a continuous population inflow from the 1970s onwards, for Central Asia experiencing a population inflow despite having once been a backward region in the 1960s, and for all Central Asian regions experiencing a population outflow in the second half of the 1970s. However, researchers such as Ball and Demko (1978) treated republics comprising the federation or vast regions called “economic regions8 ” as their units of analysis, so it cannot be denied that there was the problem of difficulty in identifying location characteristics. Amid these circumstances, Mitchneck (1991) became the first researcher at a Western organization to apply a gravity model to the analysis of interregional population migration in the Soviet Union. She investigated migration “between economic regions” in the Soviet Union in the late 1960s (1968–1969) using population census data and “intercity” population migration, which occurred between Soviet republican capitals and other major cities, using data for 1985 (Vestnik statistiki) from the Central Statistical Administration of the Soviet Union, and attempted to identify the factors behind it. What Mitchneck (1991) showed was that regional population size, which is typically used with gravity models, obtained a stable and powerfully significant coefficient. There is nothing unusual about this, but in 1968–1969, on the other hand, state investment had a greater effect on population migration than the distance variable, while in 1985 state investment had hardly any impact at all. These findings are worthy of attention. They mean that management of interregional population migration by the state remained effective at the end of the 1960s, but no 8

“Economic regions” was a regional classification established for the purpose of economic planning and management in the Soviet Union. The Russian Republic, which covered an area more than 45 times that of Japan’s, contained 11 economic regions. In addition, the Ukrainian Republic, which had a population of over 50 million and a land area 1.6 times that of Japan’s at the end of the Soviet era constituted a single economic region.

12

K. Kumo

longer had any impact at the tail end of the Soviet Union in the 1980s. The conclusion was also reached that even in 1968–1969 state investment had no influence in Siberia and the Far East, which seems counterintuitive. This is because, on the contrary, the impact of state-led development would be expected to be especially strong in remote regions like these. In addition, Cole and Filatotchev (1992) employed population census data to point out that the distance variable, which would normally play a decisive role, had limited influence, which is in tune with the findings of Mitchneck (1991) and may indicate that the Soviet Union was an unusual case. Furthermore, Cole (1990) used data from the Soviet Union’s final population census to examine the progress of urbanization in the Soviet Union, and claimed that the effectiveness of regulations concerning the inflow of population into large cities was limited. On the other hand, as was pointed out by Rowland (1989), attention needs to be paid to the possibility that the fact that there were population inflows into Siberia and the Far East until the end of the 1980s could be evidence that the management of population migration conducted in the Soviet Union was successful. Regarding the effectiveness of population migration management by the government and the impact of policy incentives, which we have looked at above through the examination of previous research on interregional population migration in the Soviet era, we find that it is claimed that while such factors played an extremely decisive role initially, toward the end of the Soviet era limitations to their effectiveness began to be observed. At the same time, because population inflows into regions with harsh living conditions, such as the Far East and the Far North, continued until the demise of the Soviet Union, it has been pointed out that population migration management maintained a great deal of influence even at the tail end of the Soviet era, so there have been mutually conflicting interpretations. Under the conditions at the time, when data were heavily restricted, it is likely to have been practically impossible to conduct any further investigations. It also cannot be denied that even after the collapse of the Soviet Union complete statistics could still not be obtained. In the next section, however, the author will employ newly obtained, usable data to attempt to identify the specific characteristics of interregional population migration in the Soviet era. The author’s attention will focus in particular on ascertaining whether, based on the insights gained from the previous research discussed in this section, regional socioeconomic circumstances did, after all, affect interregional population migration in the Soviet Union in a manner that would be intuitively expected, investigating whether the role of the distance variable was stable, and verifying the influence that state investment in the form of development incentives provided by the central government had on population redistribution.

Interregional Migration: Reexamination of Population Redistribution …

13

3 Analysis 3.1 Method Based on the previous research on interregional population migration in the Soviet Union that we looked at in Sect. 2, as well as insights gained from population migration analysis that has been performed in numerous countries (Greenwood, 2019), the author will identify the variables that should be employed. As predicted by the gravity model for population migration, the population of a region will obviously have a positive effect on the scale of population migration. Furthermore, the distance between regions should, intuitively speaking, have a negative impact on population migration between them, yet the analysis by Mitchneck (1991) did not yield stable results for the distance variable, so this will need to be verified. In addition, as mentioned earlier, Vorob’yev (1977) pointed out that the accumulation of descriptive statistics has revealed that factors such as climate conditions as well as the economic environment, wage level, and degree of infrastructure development in a region also have an effect, and it will need to be confirmed whether this also holds for the analysis of population migration in the Soviet era. And then, the task here will investigate whether the degree of concentration of investment in each region affected population flows between regions. In this chapter, the author will perform his analysis using an extended gravity model of the like widely used in previous research involving the analysis of population migration (Greenwood, 2019; Vakulenko, 2019). β

Mi j = g ∗

Pia P j Diδj



Yj ∗ Yi

γ

Here Mi j denotes the scale of population migration (number of people) from region i to region j, Pi denotes the population of region i, P j denotes the population of region j, and Di j denotes the distance between region i and region j. In addition, Yi denote characteristics of the origin region I, while Y j denote characteristics of the destination region j.

3.2 Data Economic statistics for the Soviet Union are extremely limited. Even so, usable statistics need to be extracted, and the author will rely on official statistics from the Central Statistical Administration of the Soviet Union for all of them. These are the same statistics that were used in the previous research discussed above. However, regarding interregional population migration data for the Russian Republic at the time, the author will use origin-to-destination tables, which are internal materials from the Russian Federal State Statistics Service (Rosstat) and only became available

14

K. Kumo

for use after the collapse of the Soviet Union. For the former, regional economic statistics published by the Central Statistical Administration of the Soviet Union, the author will use statistics that can be accessed by anybody, while for the latter, the origin-to-destination tables, the author will use data that he obtained from his own sources. The author will convert data on interregional population migration in the Russian republic to match the 83 regional divisions that existed as of 2016, and employ population migration matrixes that specify the origin and destination of migration. If talking about the smallest regional units from among the population migration matrixes published in the Soviet era, and one would find that they were either the “economic regions” which were discussed earlier or “cities.” The author has already pointed out that the difficulty imposed by the fact that “economic regions” were determined by dividing the vast Russian Republic into just 11 regions. Furthermore, the Soviet Union, which covered an area 60 times that of Japan’s, was split into only 19 regions, which included, for example, the “Central Asian economic region,” which contained all the Central Asian republics with the exception of what is now Kazakhstan. This was in no way adequate for analysis. In addition, the data did not even provide information on population migration, which would normally be the subject of analysis. For example, it sometimes only recorded lifetime migration.9 Regarding migration between “cities,” on the other hand, only records for migration between 53 regions in 1985 have been published, so the author was unable to surmount the data limitations when conducting his analysis.10 The data employed in this chapter is a matrix of population migration during the final 3 years of the Soviet Union (1989–1991), a period for which data could be obtained. It is a matrix of 83 × 83 regions in the Russian Republic (6,889 elements). However, the Chukot Autonomous Okrug, and the Jewish Autonomous Oblast were not independent administrative subjects at the time, so data for them are completely absent. There are also no data for the republics of Chechnya and Ingushetia, which were affected by social turmoil. Furthermore, a number of regions that are deemed to be “republics” as of 2019 are treated as part of another oblast.11 This means that the 9

The population censuses for 1926 and 1989 basically only recorded place of birth and current residence. Normally, population migration analysis covers movement between the previous residence and the current residence, and this other sort of migration, namely when the “place of birth” and the “current residence” differ, is called “lifetime migration.” Lifetime migration cannot be explained in terms of short-term factors, so is unsuitable as a target for the type of analysis performed in this paper. Note also that the 1979 population census did not include any questions about interregional migration. See Demograficheskaya entsiklopediya, Tkachenko, A. A. ed., Izdatel’stvo Entsiklopediya: Moscow, 2013 (in Russian). 10 This was used by Mitchneck (1991). 11 At the time, the Nenets Autonomous Okrug was part of the Arkhangelsk Oblast, while the Republic of Karachay-Cherkessia was an autonomous oblast and included in the Stavropol Krai. Furthermore, the Republic of Adygea was an autonomous oblast in the Krasnodar Krai, and the Khanty-Mansi Autonomous Okrug and Yamalo-Nenets Autonomous Okrug were part of the Tyumen Oblast. The Republic of Altai was the Mountainous Altai Oblast, which was part of the Altai region, and the Republic of Khakassia was an autonomous oblast in the Krasnoyarsk Krai. All these autonomous okrugs and republics, which are now independent administrative zones (federal

Interregional Migration: Reexamination of Population Redistribution …

15

number of observations for each year is less than 6,889. However, no more detailed statistics on population migration in the Russian Republic exist. The matrixes for 1990 and 1991 have been used by Oshchepkov (2007) and Kumo (2017), but the goal of Oshchepkov (2007) was not to identify the characteristics of population migration in Soviet Russia, and it is completely impossible to gauge differences between the situations prior to and after the collapse of the Soviet Union. Kumo (2017) used the same unified explanatory variables for both the Soviet Union and modern Russia, but variables for factors such as the unemployment rate and income can only be obtained for modern Russia, so it is fundamentally impossible to investigate the huge systematic and statistical changes that occurred between the two periods. Here, therefore, the primary aim will be to shed light on the characteristics of phenomena in the Soviet Union by performing an analysis that is focused on these phenomena, and then to compare these characteristics with the results of previous research. Furthermore, 1989 population migration matrix data will, as far as the author can judge, be used for the first time, as it does not seem to have been used either in the West or in Russia itself. What the author will do here is to investigate what the determinants of interregional population migration in the Soviet Union were. Naturally, regional population size will be included in the analysis. The author will also examine the effect of distance between regions, a factor that is always used with gravity models. The focus here will be on whether the distance effect is sufficiently stable. Mitchneck (1991) and Cole and Filatotchev (1992) pointed out that in most cases the distance variable is not significant, and that the distance variable is unrelated to the scale of population migration. The author will therefore investigate whether these claims are indeed justified. In addition, to explore the effectiveness of regional population redistribution policy in the Soviet era, the author will investigate whether it is possible to clarify the effect of state investment. And of course, the analysis will also employ variables used in previous research, such as regional socioeconomic conditions and the natural environment. It must also be borne in mind that for the Soviet era, data on incomes, inflation, unemployment, etc. either does not exist or no such statistics have been disclosed. It was mentioned in the previous section that the Soviet Union government suppressed urbanization, so the author will include urbanization in the variables as a means of confirming the consequences of this. Furthermore, given that age structure also affects population migration rates, the analysis will employ the proportion of people who have not yet reached working age. As an approximation variable for income level, average expenditure on charged services per capita will be used,12 and as measures of the level of infrastructure, the author will use the total length of paved roads per unit of land area and the number of buses per resident. Similarly, subjects) are treated as though they are part of each oblast and region, and even this data could not allow records of interregional population migration to be obtained. 12 “Expenditure on charged services” was an expenditure category that appeared frequently during the Soviet era. It refers to expenditure on transport, communication, education, travel, healthcare, cultural activities (museums, theatres, etc.).

16

K. Kumo

the number of doctors per resident as an indicator of social infrastructure will be employed. Consideration needs to be given to factors that were unique to the Soviet Union. Taking the impact of extreme climate conditions into account, the analysis will employ a dummy variable for administrative zones that were regarded as being in the Far North throughout the Soviet era. To serve a similar purpose, the average January temperature will be also used. The author will investigate whether population was allocated to the Far North, which contained numerous regions targeted for development, and whether this had any effect. Furthermore, as is the case with modern Russia, the Soviet Union was known as a producer of oil and gas, so to examine whether there are any differences between the Soviet Union and modern Russia in terms of population flows to resource-producing regions, a dummy variable to the top five regions for crude oil and natural gas output will be applied. Finally, the amount of state investment per capita, which is actually the most important variable, will be introduced into the analysis. Nothing beats it as an indicator of the central government’s commitment to regional development. Ball and Demko (1978) and Cole (1990) pointed out the limitations of management of population migration by the government, though Mitchneck (1991) actually argued that state investment was not significant. However, this is at odds with the phenomenon of a large-scale reversal of population migration flows, described by Kumo (2017), that occurred around the time of the collapse of the Soviet Union, when net population migration toward the Far East and Siberia was replaced by a flow toward European Russia, so it will need to be investigated using newly available data. By doing this, the author wishes to examine whether the interregional population redistribution carried out by the government during the Soviet era was effective. However, it must be mentioned that the figures for state investment are subject to major limitations. Figures relating to the years to which this study relates can only be obtained for 1990 and 1991 onwards. Furthermore, in 1992, the Soviet Union had already collapsed, so it would not be appropriate to use the figure for that year as a reference. In this chapter, therefore, the author will extrapolate figures for 1988 and 1989 from the figures for 1990 and 1991. Needless to say, this is a secondary approach, but looking at the correlation with per-capita state investment by region in 1980 and 1985, figures for which were obtained separately, reveals a correlation of at least 0.9 between the figures for 1990 and 1991 and those for both 1980 and 1985 (see Appendix Table 4). This means that the regional allocation of state investment until the end of the Soviet era can be regarded as having been stable, so given that data do not exist, the approach employed in this chapter is probably acceptable. Another major problem is that the period that this study covers, namely 1989– 1991, was right before the collapse of the Soviet Union, and it was also a time in which the macroeconomic conditions were unstable and the economic system was undergoing immense changes. The utmost care therefore needs to be taken when studying the final years the Soviet Union as opposed to a stable period like the 1960 and 1970s. And because it is naturally possible that changes in socioeconomic conditions resulted in real-time changes in interregional population migration patterns, the

Interregional Migration: Reexamination of Population Redistribution …

17

author will also try introducing year dummies, and keep the characteristics of the period in mind as one interprets the results. For the analysis, regarding quantitative variables, the analysis will compute the ratios between figures for origins and destinations, and then perform a logarithmic transformation of them. The author will also take logarithms of the size of population migration (numbers), the distance between regions, and the populations of the origins and destinations. Therefore, regional pairs between which no population migration occurred will not be included in the sample. In addition, intraregional migration, where the distance is zero, will also be excluded from the analysis. Regarding dummy variables, those for both origins and destinations will be used as is. Following Oshchepkov (2007) and Vakulenko (2019), the analysis assigned a 1-year lag to all the explanatory variables to avoid the problem of endogeneity. Definitions of, sources of, and descriptive statistics for all the variables are shown in Table 1.13

4 Results The results of the analysis are shown in Table 2. In Table 2I, the analysis has used all observations, while in Table 2II, III, IV, V the analysis has extracted regional pairs between which migration on a large scale occurred, accounting for 90–60% of the total flow, extracting regions in the order of the scale of migration, and analyzing each data set. As was the case with Kumo (2017), this is significant for the following reason: This chapter relies on macro variables to examine interregional population migration factors, but in the case of interregional migration on an extremely small scale, it would be appropriate to attribute this, depending on such factors, to the inability to identify this migration. For this reason, it is appropriate to extract and analyze the main migration patterns from all the migration data, though an issue is how to define “main patterns.” When extracting such main population flows in fields such as geography, it can be said to be typical to use such categories as “50% of all migration” or “migration on a scale of at least 0.5% of all migration” (Ishikawa, 2001). However, such approaches do not allow criticism that they are arbitrary to be avoided. The author will therefore combine a number of subsets, analyze each one, and endeavor to extract more robustly significant variables. By doing that, the analysis will focus on whether it will be possible to obtain stable results even from small subsets. Regarding the method of analysis, there are elements that do not change diachronically, such as the distance between two regions, Far North region dummies, and oil/gas-producing region dummies, and because elements such as distance and 13

The types and number of explanatory variables used probably appear somewhat limited. However, this is due to the limitations imposed on research on the Soviet economy. In fact, very few economic statistics were published during the Soviet era, which has proved a hindrance to analysis. For example, Mitchneck (1991) asserted that the only explanatory variables were population size, distance, state investment, and service expenditures. The analysis in this paper is exposed to the same limitations, and so it will only be possible to draw tentative conclusions.

18

K. Kumo

Table 1 Variables introduced, their sources, and descriptive statistics Variable

Number of observations

Average

Standard deviation

Min.

Max.

Sources and notes for data

The number of migrants

15,598

344.1

783.3

1

22,157

Material provided by Rosstat

Distance between regions (km)

16,773

2327.4

1899.8

18

7683

Federalnaya sluzhba geodezii i kartografii Rossii (1998), INGIT (2002)

Population 16,773 (in thousand)

19,50,992

15,11,321

54,500

89,70,000

TsSU, Narodnoe Khozyaystvo RSFSR (National Economy of the Russian Soviet Socialist Republic), various years

Percentage share of urban population

16,773

70.1

11.6

28.5

100

Sam as above

Percentage of the population below working age

16,773

25.5

3.79

19.7

37.4

Same as above. (under 15 y.o.)

Average expenditure on charged service per capita (rubles)

16,773

2.53

0.76

1.1

6.3

Same as population

Number of doctors per 10,000 people

16,773

43.8

11.2

30.7

105.9

Same as above

(continued)

Far North region dummies are vitally important for the analysis in this chapter, the chapter will focus on results from random effect models and pooled ordinary least squares. It can be confirmed that the distance variable yields strongly and significantly negative coefficients. This is intuitively obvious, but Mitchneck (1991) and Cole and Filatotchev (1992) claimed that in the Soviet Union, distance did not have a conspicuous impact, but the author wishes to emphasize that these sort of results were

Interregional Migration: Reexamination of Population Redistribution …

19

Table 1 (continued) Variable

Number of observations

Average

Standard deviation

Min.

Max.

Sources and notes for data

Kilometers of paved roads per square kilometre of land area (km/km2 )

16,773

87.1

67.8

0

306

Same as above. As for the data for Moscow City and St. Petersburg City, the figures for Moscow oblast and Leningrad oblast are substituted because of the lack of data. Figures for 1988 and 1989 are interpolated from figures of 1985 and 1990

Number of buses per 100,000 people

16,702

98.9

28.7

0

185.3

Same as above. Figures for 1988 and1989 are interpolated from figures of 1985 and 1990

Far north dummy

16,773

0.14

0.34

0

1

Unity for regions classified as ‘Far North’, zero for others. Goskomstat RF (2004), Ekonomichekie Pokazateli Raionov Krainego Severa I Priravnennykh k Nim Mestnostei za yanvar’-mart 2004 goda, Moskva, 2004 (continued)

20

K. Kumo

Table 1 (continued) Variable

Number of observations

Average

Standard deviation

Min.

Max.

Sources and notes for data

Oil/gas producer dummy

16,773

0.076

0.27

0

1

Same as population. If a region is one of the top five crude-oil producing regions or one of the top five natural-gas producing regions in each year (many regions are both), it is given a value of 1. Otherwise it is given a value of 0. 1990 data substituted for 1989 and 1988

Average January temperature (Celsius)

16,773

13.9

7.23

−0.5

−39

Sevruka (2006)

Government Investment per capita (rubles)

16,773

2.09

15.9

0.28

15.3

Same as population. Figures for 1988 and 1989 are extrapolated from figures of 1990 and 1991

Source Prepared by the author

obtained here. It can be said that even in the Soviet Union, increasing distance served to reduce the scale of population migration, and this was an extremely commonly observed phenomenon. However, previous research such as Mitchneck (1991) has performed analyses based on “economic regions,” which are far larger than states (called “federal subjects” after the collapse of the Soviet Union), so the reason may be that it was impossible to accurately grasp the effect of distance.14 The fact that 14

Here, whenever possible, it would be desirable to recompile the data in formats employed in Mitchneck (1991) and other previous research, such as “inter-economic-region migration,” “interrepublic migration,” or “intercity migration,” and then, by performing additional testing of the previous research, show how the impact of the distance variable changes in comparison. However, none of the “economic regions” in previous research are limited to Russia. They cover the entire Soviet Union, which had a land area that was 1.5 times and a total population that was almost twice

0.0054 0.022 0.014 0.014 0.0033 0.011 0.012 0.013 0.013 0.01 0.11

0.4

0.43

−0.076

-0.046

0.031

−0.004

0.057

−0.0089

0.19

0.19

−0.065

−0.22

0.0009

0.38

−8.5

Population (origin)

Population (destination)

Urban population

Under working age

Charged service

Doctors

Paved road

Bus

Far north (origin)

Far north (destination)

Oil/gas (origin)

Oil/gas (destination)

Jan temperature

Government investment

Constant

0.005

0.012

0.024

**

**

**

**

**

**

**

**

*

+

**

**

**

0.28

0.27

(Omitted)

(Omitted)

(Omitted)

(Omitted)

(Omitted)

−0.026

0.12

0.49

0.0079

0.042

0.029

0.043

0.01

−0.0058 0.36

0.093

0.062

0.025

0.025

SD

0.049

−0.63

0.12

0.0059

(Omitted)

0.0079

−0.44

Distance

**

β

0.0054

Fixed Effect

Coefficient β t-value

Standard Deviation

Pooled OLS

I. All the Samples

Table 2 Results

**

**

**

**

t

−7.54

0.29

0.0042

-0.16

−0.044

0.19

0.17

0.0054

0.049

0.038

0.17

0.0074

0.0079

0.022

0.021

0.019

0.019

0.017

0.0048

0.019

0.0096

0.035

−0.068 −0.0069

0.031

0.0084

0.0084

0.013

SD

−0.091

0.39

0.37

−0.43

β

Random Effect

(continued)

**

**

**

*

**

**

**

*

+

**

**

**

**

t

Interregional Migration: Reexamination of Population Redistribution … 21

0.0068 0.027 0.032 0.018 0.016 0.0037 0.013

0.16

0.2

−0.057

−0.064

0.017

−0.019

0.035

−0.015

Population (origin)

Population (destination)

Urban population

Under working age

Charged service

Doctors

Paved road

Bus

0.0068

0.0085

−0.38

**

*

*

**

**

**

t

−0.089

0.12

0.038

0.028

0.044

0.014

−0.023 0.41

0.081

0.05

0.025

0.026

SD

0.22

−0.059

0.2

0.065

(Omitted)

β

SD

β

Fixed Effect

Pooled OLS

Distance

SD

t

*

**

**

+

**

**

*

t

−0.024

0.042

0.071

−0.029

0.022

−0.083

0.21

0.16

−0.37

β

SD

0.018

0.0049

0.019

0.012

0.039

0.031

0.0095

0.0096

0.013

SD

Observation: 14,952 Samples: 5,111 Wald chi2(68) = 7902.53 Prob > chi2 = 0.00 R-square Within = 0.087 Between = 0.59 Overall = 0.56

β

Random Effect

Random Effect

Observation: 14,952 Samples: 5,111 F(9, 9832) = 138.99 Prob > F = 0.00 R-square: Within = 0.11 Between = 0.07 Overall = 0.073 Sargan Test statistic = 520.16; P-value = 0.00

β

Observation: 14,952 F(15, 14,936) = 1307.51 Prob > F = 0.00 Adjusted R-square: 0.57

Fixed Effect t-value

Coefficient β Standard Deviation

Pooled OLS

II. 90% of Total Migration: Region pairs with more than 147 migrants

I. All the Samples

Table 2 (continued)

(continued)

**

**

*

**

**

**

**

t

t

22 K. Kumo

0.013 0.015 0.012 0.14

−0.029

−0.13

0.0001

0.25

−1.66

Oil/gas (origin)

Oil/gas (destination)

Jan temperature

Government investment

Constant

**

**

**

*

**

**

0.0081 0.032 0.041

0.089

0.14

−0.055

−0.073

Population (origin)

Population (destination)

Urban population

Under working age

0.0083

0.0099

−0.35

+

+

**

**

**

t

0.2

−0.048

0.19

0.069

(Omitted)

β

SD

β

FE

Distance

0.49

0.0076

SD

**

**

t

0.09

0.059

0.032

0.031

SD

*

**

*

t

Observation: 7,287 Samples: 2,842 F(9, 4436) = 147.30 Prob > F = 0.00 R-square: Within = 0.23 Between = 0.04 Overall = 0.044 Sargan Test statistic = 120.28; P-value = 0.00

−1.43

0.25

(Omitted)

(Omitted)

(Omitted)

(Omitted)

(Omitted)

Pooled OLS

III. 80% of total migrants: Region pairs with more than 250 migrants

Observation: 7,287 F(15, 14,936) = 208.43 Prob > F = 0.00 Adj. R-square: 0.30

0.0057

0.016

0.14

Far north (destination)

0.015

0.13

Far north (origin)

Fixed Effect β

t

β SD

Pooled OLS

II. 90% of Total Migration: Region pairs with more than 147 migrants

Table 2 (continued) Random Effect

0.19

0.007

0.0078

0.021

0.02

0.023

0.023

SD

0.044

−0.066

0.16

0.097

−0.35

β

RE

0.048

0.037

0.011

0.011

0.015

SD

Observation: 7,287 Samples: 2842 Wald chi2(15) = 2472.2 Prob > chi2 = 0.00 R-square Within = 0.21 Between = 0.31 Overall = 0.30

−1.75

0.25

0.004

−0.12

−0.044

0.16

0.13

β

(continued)

+

**

**

**

z

**

**

**

*

**

**

t

Interregional Migration: Reexamination of Population Redistribution … 23

0.0044 0.016

0.015 0.017 0.0066

0.03

−0.014

0.049

0.12

0.011

−0.091

−0.001

0.23

0.31

Paved road

Bus

Far north (origin)

Far north (destination)

Oil/gas (origin)

Oil/gas (destination)

Jan temperature

Government investment

Constant

Observation: 4,674 F(15, 4658) = 102.31 Prob > F = 0.00 Adjusted R-square: 0.25

0.16

0.014

0.019

0.018

0.019

−0.0072

Doctors

*

**

**

**

**

**

0.022

−0.0079

Charged service

0.61

−1.29 **

**

*

**

**

*

t

Observation: 4,674 Samples: 1,889 F(9, 2776) = 104.64 Prob > F = 0.00 R-square: Within = 0.25 Between = 0.03 Overall = 0.028 Sargan Test statistic = 57.31; P-value = 0.00

0.009

0.043

0.031

0.05

0.017

SD

0.25

(Omitted)

(Omitted)

(Omitted)

(Omitted)

(Omitted)

−0.1

0.091

0.3

−0.039

FE β

β t

SD

Pooled OLS

III. 80% of total migrants: Region pairs with more than 250 migrants

Table 2 (continued) RE

0.22

0.0081

0.0085

Observation: 4,674 Samples: 1,889 Wald chi2(15) = 1497.82 Prob > chi2 = 0.00 R-square Within = 0.24 Between = 0.24 Overall = 0.24

−0.1

0.24

0.002

0.024

0.023

−0.11

0.028

−0.002

0.026

0.021

0.0056

0.022

0.015

SD

0.14

0.055

−0.034

0.039

0.055

−0.043

β

(continued)

**

**

**

*

+

**

*

**

z

24 K. Kumo

0.0099 0.039 0.052 0.027 0.0051 0.019

0.017 0.02 0.0075

0.087

−0.043

−0.037

−1E–03

0.0029

0.026

−0.006

0.045

0.087

0.02

−0.071

−0.002

0.19

1.56

Population (destination)

Urban population

Under working age

Charged service

Doctors

Paved road

Bus

Far north (origin)

Far north (destination)

Oil/gas (origin)

Oil/gas (destination)

Jan temperature

Government investment

Constant

0.18

0.016

0.023

0.022

0.023

**

**

**

**

*

**

**

**

0.01 0.76

−2.1

0.049

0.036

0.056

0.019

0.1

0.071

0.041

0.037

SD

0.24

(Omitted)

(Omitted)

(Omitted)

(Omitted)

(Omitted)

-0.1

0.08

0.27

−0.027

0.16

−0.041

0.23

0.098

(Omitted)

0.06

Population (origin)

**

0.012

−0.31

Distance 0.0099

FE β

β t

SD

Pooled OLS

III. 70% of total migrants: region pairs with more than 380 migrants

Table 2 (continued)

**

**

*

*

**

**

**

t

RE

1.02

0.24

−0.002

−0.095

0.002

0.12

0.054

−0.032

0.036

0.05

−0.035

0.048

−0.053

0.11

0.071

−0.33

β

0.25

0.0093

0.0095

0.028

0.026

0.033

0.031

0.024

0.0064

0.024

0.017

0.059

0.044

0.013

0.013

0.017

SD

(continued)

**

**

**

**

+

**

*

*

**

**

**

z

Interregional Migration: Reexamination of Population Redistribution … 25

0.012 0.045 0.063

0.061

0.065

−0.067

−0.11

0.00037

0.0038

0.015

Population (origin)

Population (destination)

Urban population

Under working age

Charged service

Doctors

Paved road

0.0059

0.027

0.032 *

+

**

**

0.078

0.039

0.066

0.023

−0.0008 0.19

0.13

0.081

0.45

0.43

SD

0.16

−0.044

0.22

0.14

(Omitted)

0.014

−0.27

Distance

**

β

0.012

FE SD

β t

SD

t

*

**

**

**

t

Observation: 3,037 Samples: 1,237 F(9, 1791) = 81.12 Prob > F = 0.00 R-square: Within = 0.29 Between = 0.008 Overall = 0.0098 Sargan Test statistic = 48.75; P-value = 0.00

Pooled OLS

III. 60% of total migrants: region pairs with more than 561 migrants

Observation: 3,037 F(15, 3021) = 55.26 Prob > F = 0.00 Adjusted R-square: 0.21

FE β

t

β SD

Pooled OLS

III. 70% of total migrants: region pairs with more than 380 migrants

Table 2 (continued) RE SD

0.029

0.023

−0.009

−0.009

−0.068

0.085

0.079

−0.3

β

RE

0.0075

0.029

0.02

0.072

0.052

0.016

0.016

0.019

SD

Observation: 3,037 Samples: 1,237 Wald chi2(15) = 1012.08 Prob > chi2 = 0.00 R-square Within = 0.28 Between = 0.31 Overall = 0.21

β

(continued)

**

**

**

**

z

z

26 K. Kumo

0.0085

0.016

-0.058

−0.0029

0.17

1.88

Oil/gas (origin)

Oil/gas (destination)

Jan temperature

Government investment

Constant

Source Prepared by the author

0.024

0.058

Far north (destination)

Observation: 1,977 F(15, 1961) = 29.84 Prob > F = 0.00 Adjusted R-square: 0.18

0.2

0.019

0.019

0.028

0.025

0.04

Far north (origin)

0.023

0.0011

Bus

**

**

*

*

0.86

−2.53 **

**

**

t

Observation: 1,977 Samples: 797 F(9, 1171) = 67.07 Prob > F = 0.00 R-square: Within = 0.34 Between = 0.023 Overall = 0.055 Sargan Test statistic = 58.89; P-value = 0.00

0.012

0.054

SD

0.26

(Omitted)

(Omitted)

(Omitted)

(Omitted)

-0.15

FE β

t

β SD

Pooled OLS

III. 60% of total migrants: region pairs with more than 561 migrants

Table 2 (continued) RE

0.032

−0.11

0.29

0.011

Observation: 1,977 Samples: 797 Wald chi2(15) = 2472.2 Prob > chi2 = 0.00 R-square Within = 0.21 Between = 0.31 Overall = 0.30

1.34

0.25

0.011

0.029

-0.007

0.039

−0.015

0.036

0.028

SD

0.069

0.034

-0.054

β

**

**

**

+

z

Interregional Migration: Reexamination of Population Redistribution … 27

28

K. Kumo

origin and destination population had a significant impact on the scale of interregional migration can be said to have been an obvious finding. The coefficient for proportion of the population who live in cities was generally significant, and the fact that it was negative even when it was significant is a result that is unique to the Soviet Union, which deliberately attempted to limit the growth of the urban population. Cole (1990) pointed out that the rise in the urban population as a proportion of the total population is indicative of the limited effect of efforts to manage population migration in the Soviet Union, but the government did intend for the number of residents to increase to a certain extent, and it may just be that it had a powerful suppressive effect. Average expenditure on charged services per capita, which was used as substitute variable for income, and the number of doctors per resident, which was used as an indicator of social infrastructure development, sometimes yielded positive and significant coefficients, but it cannot be said that stable results were observed. At present, it is impossible to obtain statistics for income itself, so there are limitations with respect to the substitute variable used, expenditure on services (not total expenditure), but in the Soviet Union, where an urban residence permit would only be issued after the person concerned had secured a stable place of employment (Matthews, 1993), if interregional migration did not occur based on the individual’s wishes, there is nothing odd about obtaining these sorts of results. Regarding the density of paved roads and the number of buses per resident, which serve as indicators of the level of economic infrastructure, the latter was insignificant, but the former was stably positive and significant. This may not be indicative of personal preferences but instead could be interpreted as evidence of the government’s commitment to development. After all, it cannot be said that personal car ownership was typical in the Soviet Union at the time,15 so if paved roads are assumed to have been used basically for industrial purposes, such an interpretation can be said to be much more reasonable. The Far North dummy tended to be positive and significant for both origins and destinations, but it is clear that when the destination was in the Far North, it was more stably significant, and the absolute value of the coefficient was always higher for the destination. This means that in the Far North, outflows and inflows were both heavy, but inflows were greater than outflows. It could be said that this was underpinned by the frontier development policy of the Soviet Union at the time. A significant coefficient was not obtained for average January temperature, but it is that of Russia’s, so they are not suitable for additional testing. When desirable results were obtained in accordance with the authors’ claims, it can be said that the claims were reasonable, but on the other hand, when only unexpected results could be obtained, it is possible to cite the difference in the coverage of the analysis as a reason. The author therefore decided to wait until there is an opportunity to obtain relevant data for the Soviet Union as a whole, so for this paper the author abandoned this investigation. 15 In 1985, more than 60% of Japanese households owned a car, and there were 223 cars for every 1,000 people. In the same year in the Russian Republic (as it was in the Soviet era), however, there were fewer than 45 cars for every 1,000 people. This is lower than the number of cars per 1,000 people in Japan in 1969 (See Goskomstat Rossii, Pokazateli sotsial’nogo razvitiya Rossiyskoy federatsii i ee regionov, 1993, p. 367 [in Russian]).

Interregional Migration: Reexamination of Population Redistribution …

29

likely that the policy status given to Far North regions was more important than the physical factor of temperatures. On the other hand, the dummy for oil/gas producing regions showed that the inflow toward such regions was actually smaller. In modern Russia, the economy of which is mainly reliant on resource exports, the exact opposite results were obtained (Kumo, 2017). Furthermore, while the Soviet Union was the world’s largest oil-producing nation at the time,16 this was probably not of standout importance domestically. What needs to be stressed is that compared with the early twenty-first century, during which oil prices have basically remained at high levels, at the end of the 1980s, there was a time when the price of crude oil plummeted, and during this period the Soviet Union slashed the quantity of crude oil being produced. It can therefore be surmised that this resulted in a population outflow from oil/ gas-producing regions. Regarding per-capita state investment, the results were extremely stable. In other words, with all estimates, significantly positive coefficients were obtained. State investment in the Russian Federation following the collapse of the Soviet Union can be assumed to have played a compensatory role toward underdeveloped regions (Kumo, 2017), whereas during the Soviet era, it can be regarded as having spurred development (Mitchneck, 1991). To avoid identifying it as an inverse flow, whereby investments are made in regions that are attracting more people, the analysis has assigned a 1-year lag to the explanatory variables, as it was explained earlier, and here it will be shown that people flowed into areas that were targeted for state investment in the Soviet era. This means that it can probably also be assumed that during the Soviet era state investment functioned as an effective policy for attracting development. This state investment was referred to in the Russian language using a term meaning “basic investment” (osnovnoy capital) and caution needs to be exercised with regard to the fact that only investment that contributed to physical output was recorded under this heading. Social investment, such as investment in welfare, commerce, education, etc. was not included, so it can be concluded that the orientation was toward regional development. Mitchneck (1991) contended that state investment ceased to have an effect on population migration at the end of the 1980s, and the results she obtained from her analysis were actually insignificant in the case of every state investment specification. This may have been because there were problems with the analytical units of the data she used for this period. In fact, as it was mentioned earlier, the analysis of population migration at the end of the 1980s that was performed by Mitchneck (1991) only examined migration between cities. If cities had not been targets for development, it would have been unsuitable to make state investment an explanatory variable. In fact, if the government’s management of population migration had not been effective, it would be impossible to explain the fact that population flows, which had been toward the Far East and Siberia, were reversed toward European Russia following the Soviet collapse (Kumo, 2017). 16

BP Statistical Review of World Energy 2015, http://www.bp.com/genericsection.do?categoryId= 92&contentId=7005893, http://www.bp.com/en/global/corporate/energy-economics/statistical-rev iew-of-world-energy.html, accessed on July 1, 2018).

30

K. Kumo

Such results contrast with the general view that the Soviet planned economy had become dysfunctional (Iwasaki & Kumo, 2020; Iwasaki & Suzuki, 2020). However, at least with regard to interregional population migration and the management of population distribution, results that are similar to the view obtained from this analysis can be seen in Kumo (2017). This is shown clearly in the population-census-based origin-to-destination table presented in Appendix Table 5. In 1989, during the Soviet era, 1.2 million people who were living in the Siberian and Far East federal districts had been born in what is now, as of 2018, the Central Federal District. On the contrary, just over 760,000 people who were living in the Central Federal District had been born the Siberian or Far East federal districts. In 2002, however, over a decade after the collapse of the Soviet Union, the number of people who were living in the Siberian and Far East federal districts and had been born in the Central Federal District had shrunk to just over 600,000, while the number living in the Central Federal District who had been born in the Siberian or Far East federal districts had increased to over one million. In other words, people born in Siberia and the Far East had begun flowing into European Russia, and it can be surmised that the bulk of people who had been born in central Russia and had moved to Siberia or the Far East had returned to central Russia.17 In contrast, under the aforementioned domestic passport system and the residence permit system, population was allocated to relatively undeveloped regions such as Siberia and the Far East, and the results show that this situation had still been maintained toward the end of the Soviet era. It is a fact that at the end of the Soviet era, economic circumstances began changing dramatically. However, all the year dummies employed to investigate these changes on a year-by-year basis were not significant.18 To shed further light on this, the author performed estimates using least squares regression for each of the years, and as Table 3 shows, it is fair to say that the results were qualitatively identical. This finding, namely that extremely stable positive and significant coefficients were obtained for state investment presents a clear contrast with the analysis for the period after 1992 described in Kumo (2017). Furthermore, the analysis in Kumo (2017) shows that negative coefficients are obtained for interregional population migration in the case of state investment from 1992 onwards. This series of findings suggests that the collapse of the Soviet Union constituted a major turning point for patterns of interregional population migration. As repeated, governmental control on population migration in the Soviet Union was effective, which was suggested in the analysis. In this regard, it is possible to see that governmental policy can manage interregional population migration patterns in some way or so. This may be suggestive for other countries in introducing government-let regional development priority. At the same time, however, one should take into account the possible costs associated with the policy conducted. Conclusions like this, which point to the effectiveness of population migration management toward the end of the Soviet era, may appear odd in light of the social 17

This trend continued after that, with the 2010 population census revealing that flows toward the Central Federal District had become even more pronounced in relative terms (Kumo, 2017). 18 The results are omitted here.

Interregional Migration: Reexamination of Population Redistribution …

31

Table 3 Results of the analyses conducted by each year I. All the Samples 1989

1990

1991

β

SD

t

β

SD

t

β

SD

z

Distance

−0.43

0.015

**

−0.44

0.016

**

−0.45

0.016

**

Population (origin)

0.42

0.009

**

0.41

0.009

**

0.39

0.009

**

Population (destination)

0.41

0.009

**

0.42

0.009

**

0.43

0.009

**

Urban population

−0.04

0.039

−0.051

0.037

−0.056

0.038

Under working age

−0.11

0.039

*

−0.084

0.041

*

0.075

0.044

Charged service

0.051

0.029

+

0.062

0.028

*

−0.016

0.022

Doctors

−0.064

0.024

*

−0.041

0.023

+

−0.062

0.022

*

Paved road

0.045

0.0062

**

0.048

0.006

**

0.074

0.0063

**

Bus

−0.002

0.019

−0.019

0.019

−0.062

0.019

*

Far north (origin)

0.18

0.025

**

0.21

0.025

**

0.19

0.026

**

Far north (destination)

0.17

0.023

**

0.18

0.023

**

0.21

0.023

**

Oil/gas (origin)

−0.05

0.021

+

−0.065

0.021

*

−0.1

0.022

**

Oil/gas (destination)

−0.18

0.023

**

−0.19

0.022

**

−0.24

0.023

**

Jan temperature

0.002

0.009

−0.005

0.009

−0.027

0.009

*

Government investment

0.29

0.017

**

0.33

0.018

**

0.47

0.022

**

Constant

−8.4

0.19

**

−8.36

0.19

**

−8.4

0.019

**

Observation: 4,898 F(15, 4882) = 389.63 Prob > F = 0.00 Adjusted R-square: 0.58

+

Observation: 4,960 F(15, 4944) = 402.6 Prob > F = 0.00 Adjusted R-square: 0.57

Observation: 5,386 F(15, 4882) = 442.83 Prob > F = 0.00 Adjusted R-square: 0.58

1989 (region pairs with more than 315 migrants)

1990 (region pairs with more than 289 migrants)

1991 (region pairs with more than 239 migrants)

II. 90% of all the migration OLS

β

SD

t

β

SD

t

β

SD

z

Distance

−0.34

0.019

**

−0.34

0.019

**

−0.37

0.019

**

Population (origin)

0.08

0.015

**

0.087

0.015

**

0.082

0.014

**

Population (destination)

0.13

0.015

**

0.13

0.015

**

0.13

0.016

**

(continued)

32

K. Kumo

Table 3 (continued) II. 90% of all the migration OLS 1989 (region pairs with more than 315 migrants)

1990 (region pairs with more than 289 migrants)

1991 (region pairs with more than 239 migrants)

β

SD

β

SD

β

SD

Urban population

−0.057

0.059

−0.016

0.057

−0.044

0.062

Under working age

−0.048

0.065

−0.037

0.071

−0.044

0.08

Charged service

0.041

0.042

−0.029

0.041

−0.035

0.036

Doctors

−0.012

0.034

0.013

0.034

−0.004

0.032

Paved road

0.027

0.008

0.029

0.008

0.043

0.008

Bus

0.0001

0.029

−0.002

0.029

−0.032

0.029

Far north (origin)

0.031

0.032

0.043

0.033

0.036

0.03

Far north (destination)

0.099

0.036

0.097

0.035

0.15

0.034

Oil/gas (origin)

0.018

0.028

0.011

0.029

−0.018

0.029

Oil/gas (destination)

−0.071

0.031

−0.08

0.031

−0.12

0.034

Jan temperature

0.011

0.012

−0.009

0.012

−0.016

0.012

Government investment

0.2

0.025

**

0.24

0.026

**

0.31

0.034

**

Constant

0.69

0.29

*

0.56

0.29

+

0.65

0.29

*

t

**

+

*

Observation: 1,393 F(15, 1377) = 24.23 Prob > F = 0.00 Adjusted R-square: 0.24

t

**

+

*

Observation: 1,404 F(15, 1388) = 24.07 Prob > F = 0.00 Adjusted R-square: 0.24

z

**

**

**

Observation: 1,525 F(15, 1509) = 25.98 Prob > F = 0.00 Adjusted R-square: 0.25

Source Prepared by the author

turmoil that was occurring at the time. That being said, this could be understood as follows: Day-to-day economic activity is dependent on short-term decision-making, but the fundamental norms are not shaken. It is just a strategy for surviving each day, and various forms of subtle unlawful conduct, of a degree that would not be subject to criminal punishment, can be expected to occur. However, interregional migration that covers distances of several hundred or several thousand kilometers is more the result of the underlying system, change at this degree does not occur without the change in the official power structure, and change cannot be expected to happen overnight. In fact, during the 1990s, when post-Soviet-collapse Russia was experiencing a transition to a new economic system, one can be said to have observed a system that behaved according to the law of inertia (Iwasaki & Kumo,

Interregional Migration: Reexamination of Population Redistribution …

33

2020). If that is the case, the results obtained in this chapter, namely the view that the management of interregional population migration was also effective toward the end of the Soviet era, can probably be accepted. As it was mentioned earlier, Ball and Demko (1978) asserted that the fact that at the end of 1960s the Russian Republic switched from being a population-outflow region to a population-inflow region showed that there were limits to the effectiveness of population migration management, but here again the fact that the analytical units were “federal republics” could be a problem. At the beginning of the 1960s, high priority was placed on the development of central Asia, but it can be assumed that from the end of the 1960s onwards priority was given to the Far North and Far East regions, the entire territories of which were located within the Russian Republic (Perevedentsev, 1966). So as the analysis in this chapter has shown, the final days of the Soviet era should probably be regarded as a period in which management of interregional population redistribution remained effective to a certain extent.

5 Conclusions Discourse concerning interregional population migration during the Soviet era has contended that management by the government was effective initially, as it was also easy, for example, to redistribute people over long distances, but that in the latter part of the era, the effectiveness of attracting people through state investment became limited. While this could certainly be accepted as something that could have happened, it also cannot be denied that it is at odds with what actually happened. While it has alluded to the possibility that distance had little effect, given that population flows from regions that are extremely far away from European Russia, such as the Far North and the Far East, were seen on a continuous basis, the fact that this continued until the end of the Soviet Union also demonstrated that the potential for management of population migration by the government had not been exhausted. The analysis conducted in this chapter showed that the impact of the Far East dummy and the impact of state investment were both strongly significant even at the tail end of the Soviet era. The fact that the distance variable was negatively significant but inflows to Far North regions continued until the end of the Soviet Union may mean, for example, that inflows not from European Russia, but from regions that were relatively closer, occurred. On the other hand, results supporting the effectiveness of state investment could indicate that there were problems with the samples used in previous research. Intercity migration, which has been analyzed in previous research, was subject to administrative control, so it was probably not an appropriate sample for judging the influence of state investment. However, the investigations at the time had to be performed under conditions in which no other data existed, and given the background to that period, it can be said that there was no other alternative. In that sense, the efforts the predecessors made amid these constraints are worthy of praise, and the author not criticizing such previous research. Even so, it can be said that

34

K. Kumo

with regard to the analysis of the Soviet era, there still remains scope to perform investigations using more detailed data. Issues like these also apply to the analysis conducted in this chapter. In fact, it is undeniable that most of the explanatory variables used are substitute variables or estimates. Aside from variables for which it can be judged that no major problems will occur as a result of using estimates, such as the density of paved roads and the number of physicians per resident, for which sudden changes cannot occur, the constraint of being unable to use variables for income and wages is extremely severe. Another major problem is that variables for state investment, which was an important issue in this chapter, were extrapolated from the figures for 1990 and 1991. This was because it was not until 1990 that figures for state investment in each region began to be published, but given that there are figures for interregional population migration, it is quite possible that information on state investment exists internally at Rosstat, and the archives there will need to continue to be pored over in the future. The author has described how research on interregional population migration during the Soviet era has progressed slowly due to the limited data, but the same also applies to the situation after the Soviet collapse, so numerous challenges exist. Acknowledgements This study was supported by Grant in Aid for Scientific Research B (19H01478) by the Ministry of Education, Science and Culture in Japan, and the Joint Usage and Research Center Program of Hitotsubashi IER in 2021.

Appendix See Tables 4 and 5. Table 4 Correlation coefficients of governmental investment per capita by year (1980, 1985, 1990, and 1991) 1980

1985

1990

1991

1980

–

0.970

0.974

0.913

1985

0.970

–

0.979

0.902

1990

0.974

0.979

–

0.985

1991

0.913

0.902

0.985

–

Sources Calculated by the author by Goskomstat Rossii, Pokazateli sotsial’nogo razvitiya Rossiyskoy federatsii i ee regionov, 1993, pp. 100–102, and Goskomstat Rossii, Rossiyskiy ststisticheskiy ezhegodnik 1994, 1994, pp. 721–723 (in Russian)

Interregional Migration: Reexamination of Population Redistribution …

35

Table 5 Distribution of place of birth and place of residence seen from popultion censuses in 1989 and 2002 (in thousand) Population census 1989

Place of residence Central

NorthWest

South

North Caucasus

Volga

Urls

Siberia

Far East

Central Federal district

31,623

1,565

769

161

978

555

686

492

North West FD

628

10,436

169

46

283

165

195

117

South FD

426

206

10,153

231

245

232

173

199

North Caucasus FD

154

80

306

6,258

82

123

68

71

Volga FD

1,473

759

635

146

27,447

1,872

943

493

Urals FD

266

158

171

49

443

9,180

365

162

Siberia FD

496

252

354

101

390

505

18,819

742

Far East FD

268

124

144

45

187

116

387

5,116

Population census 2002

Place of residence Central

NorthWest

South

North Caucasus

Volga

Urls

Siberia

Far East

Central Federal district

29,818

1,038

578

112

721

322

397

232

North West FD

662

9,768

163

43

249

102

123

64

South FD

431

166

9,930

192

208

130

116

93

North Caucasus FD

283

90

367

7,529

110

96

66

43

Volga FD

1,358

565

524

119

27,163

1,182

580

254

Urals FD

316

142

180

47

378

8,873

260

91

Siberia FD

620

241

346

95

369

363

16,707

480

Far East FD

384

133

183

45

199

98

316

4,758

Source Calculated by the author by TSSU SSSR, Itogi vsesoyuznoy perepisi naseleniya 1989 goda, tom 12, Moskva, TSSU SSSR, and Rosstat, Itogi Vserossiyskoy perepisi naseleniya 2002 goda, Tom.10, Prodolzhitel’nost’ prozhivavaniya naseleniya v meste postoyannogo zhitel’stva, Statistika Rossii, 2005 (in Russian)

36

K. Kumo

References Ball, B., & Demko, G. J. (1978). Internal migration in the soviet union. Economic Geography, 54(2), 95–114. Cole, J. P. (1990). Changes in the population of larger cities of the USSR. Soviet Geography, 31(3), 160–172. Cole, J. P., & Filatotchev, I. V. (1992). Some observations on migration within and from the former USSR in the 1990s. Post-Soviet Geography, 33(7), 432–453. Greenwood, M. J. (2019). The migration legacy of E. G. Ravenstein. Migration Studies, 2(2), 269–278. Ishikawa, Y. (2001). Studies on migration transition. University of Kyoto Press. Iwasaki, I., & K. Kumo (2020). Transformational recession and recovery: determinants of the Jcurved growth path. In I. Iwasaki (Ed.), The economics of transition: Developing and reforming emerging economies (pp. 67–118). Iwasaki, I., & T. Suzuki (2020). Transition strategy debate: Radicalism versus gradualism. In I. Iwasaki (Ed.), The economics of transition: Developing and reforming emerging economies (pp. 25–66). Kalemeneva, E. (2019). From new socialist cities to thaw experimentation in arctic townscapes: Leningrad architects attempt to modernise the Soviet North. Europe-Asia Studies, 71(3), 426– 449. Kumo, K. (2003). Migration and regional development in the Soviet Union and Russia: A geographical approach. Beck Publisher Russia. Kumo, K. (2017). Interregional migration: Analysis of origin-to-destination matrix. In T. Karabchuk, K. Kumo & E. Selezneva (Eds.), Demography of Russia: From the past to the present (pp. 261– 314) Palgrave Macmillan. Matthews, M. (1993). The passport society: Controlling movement in Russia and the USSR. Westview Press. Mitchneck, B. A. (1991). Geographical and economic determinants of interregional migration in the USSR, 1968–1985. Soviet Geography, 32(3), 168–189. Nykanen, N. (2018). Competing institutional logics in Soviet industrial location policy. Eurasian Geography and Economics, 59(3–4), 314–339. Orttung, R. W., Anisimov, O., Badina, S., Burns, C., Cho, L., DiNapoli, B., Jull, M., Shaiman, M., Shapovalova, K., Silinsky, L., Zhang, E., & Zhiltcova, Y. (2020). Measuring the sustainability of Russia’s arctic cities. Ambio. https://doi.org/10.1007/s13280-020-01395-9 Oshchepkov, A. Y. (2007). Mezhregionalnaya migratsiya v Rossii. Higher School of Economics (in Russian). Perevedentsev, V. (1966). Migratsiya naseleniya i trudovyye problemy Sibiri. Nauka. (in Russian). Rodgers, A. (1974). The location dynamics of Soviet industry. Annals of the Association of American Geographers, 64(2), 226–240. Rowland, R. H. (1989). National and regional population trends in the USSR, 1979–89: preliminary results from the 1989 census. Soviet Geography, 30(9), 635–669. Samorodov, A. (1991). Labor market problems and developments in the republics. In G. Standing (Ed.), The new Soviet labor market (pp. 145–163). International Labor Office. Vakulenko, E. S. (2019). Motivy vnutrenney migratsii naseleniya v Rossii: chto izmenilos v posledniye gody? Prikladnaya ekonometrika, 55, 113–138 (in Russian). Vorob’yev, V. V. (1977). Naseleniye vostochnoy Sibiri, Nauka: Nobosibirsk (in Russian).

Clustering of Small and Medium-Sized Cities in Russia Based on the Assessment of Knowledge Spillovers Localization Tatyana Melnikova

Abstract Small and medium-sized cities of Russia are one of the most sensitive topics in spatial research. Such cities are resilient to changing economic conditions, but are quite unaffordable in moving towards a higher level of development. In this paper, we set a goal to identify existing barriers of knowledge spillovers in small and medium-sized cities and their subsequent localization. We thus indicated the urban environment quality, the financial capital availability, the level of social capital, and transaction costs as potential barriers in localizing knowledge spillovers. On the basis of the k-means clustering method and data for the cities of the Sverdlovsk region, Oryol region and the Republic of Crimea, five clusters of cities were formed. The first cluster includes small cities with high barriers. The second cluster consists of a medium-sized city with low barriers. Small and medium-sized cities with moderate barriers of tacit knowledge spillovers form the third cluster. The fourth cluster includes mainly small cities with low barriers of tacit knowledge spillovers. And the fifth cluster is based on small and medium-sized cities with moderate agglomeration influence, lack of social capital and low financial barriers. The results obtained are useful for identifying reserves for reducing knowledge spillover barriers.

1 Introduction The agglomeration processes in the Russian Federation are being intensified, and scientific discourse regarding small and medium-sized cities has reopened. Such cities face a fairly large number of challenges, namely: obsolescence, a lower level of life and quality of services satisfaction, and a higher level of poverty. There has been a growing volume of research searching for resilience sources for small and medium-sized cities. Due to the greater dependence of “small” cities’ budgets on locally formed revenues, their sustainability is largely determined by specialization (the category of “small” cities here includes cities with a population of up to 250 T. Melnikova (B) Department of Management, Tourism and Hospitality, Plekhanov Russian University of Economics, Sevastopol Branch, 29 Vakulenchuka St., Sevastopol 299053, Russian Federation e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_3

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thousand people) (Limonov & Nesena, 2019). The resilience of demographic policy is also under the rule of efficient economic policy that promotes reindustrialization and postindustrial specialization, as well as geographic referencing to large centers. Attention is paid to the advantages of labor migration (Mkrtchyan & Florinskaya, 2019), and the prerequisites for educational migration (Karachurina & Florinskaya, 2019). The development of small and medium-sized cities is tied to the territory’s efficient use in relation to the process of forming budget revenues (Tagirov & Ignatova, 2020), and the transition to a green economy model (Kuzmitskaya, 2021). The research methodology includes the development of methods for assessing demographic stability (Fauser et al., 2021), a system of factor indicators for urban growth (Manaeva et al., 2020), and the peculiarities of periphery location (Modica et al., 2021). At the core of the difficulties of empirical measurement is the limited data on small and medium-sized cities, therefore, along with Rosstat data, the results of surveys of the population, business and local governments are used (Sekushina, 2021). Knowledge and technology as a resource for small and medium-sized cities growth are not given much attention, but in European research papers, it is a fairly widespread subject. For example, three dimensions of patterns of development in small and medium-sized cities are considered: residential, productive and creative knowledge (Hamdouch et al., 2017). Our paper develops the research area regarding knowledge as a source of small and medium-sized cities‘ resilience. The results presented in this study make it possible to obtain additional methodological tools for assessing the readiness of a small or medium-sized city to rely on knowledge in its development, which is especially important when improving strategies for the region’s spatial development. We expanded data sources and applied data analysis methods. The paper is organized as follows. Section 2 outlines the literature review; Sect. 3 describes the cluster methodology adopted with relevant data overview; Sect. 4 presents the empirical findings; Sect. 5 discusses the results; Sect. 6 concludes the work.

2 Literature Review Knowledge spillovers are regarded as one of the key elements of regional development as well as the presence or absence of costs for knowledge, ideas and technologies diffusion. There are several approaches of knowledge classification. Regarding the knowledge base, analytical, synthetic and creative ones are distinguished. Knowledge can be public, private and tacit (Capello & Nijkamp, 2019). The analytical knowledge base is associated with formalized knowledge formation and transfer processes, in particular through intra-firm research units and does not imply influence from tacit knowledge. Thus, the private knowledge is in focus. The synthetic knowledge base is

Clustering of Small and Medium-Sized Cities in Russia Based … Table 1 Barriers to localizing knowledge spillovers

39

Type of knowledge

Examples of barriers

Public (information, data)

Telecommunication accessibility level

Private (patents, technologies)

The level of institutions financial capital availability

Tacit (incorporated in human)

Geographic distance the social capital nature

primarily referring to a new combination of existing knowledge in industrial production through the results of interaction with partners and consumers. Thus, tacit knowledge already plays a big role. Creative (symbolic) knowledge base being a part of synthetic ones is significant for creative industries with a strong dependence on tacit knowledge (Eder, 2019). Territorial dynamics should be perceived as the main result of knowledge spillovers. Of course, changes will not occur in the knowledge not being localized. Localization of effects from knowledge transfer may refer to the formation of organizational and technological, as well as social and institutional territorial innovations. The former deals with inventions shaped by cooperation or competition. The latter is integrated into new projects and arises as a result of agreement or conflict with subsequent society approval or disapproval (Torre, 2018). The following barriers of knowledge spillovers in small and medium-sized cities can be formulated (Table 1). Tacit knowledge diffusion works best when geographic distance is reduced through passenger transport projects and the spatial proximity of companies. Localization of tacit knowledge, in particular, can be based on the activities of leading scientists. According to the results of studies carried out in Germany, 85% of knowledge transfer from scientists to the business sector (small forms) is locally linked. About 76% of leading scientists recruit a team of young researchers also at the local level. Likewise, 60% of scientific leaders prefer local scientist-to-scientist collaboration. However, the mentioned local level refers to the territory of work of a given scientist, that is, the location of a research or educational institution, with an influence zone of up to 100 km (Schiller & Diez, 2010). Institutional similarity or low transaction costs resulting from efficient institutions reduce private knowledge transfer barriers. The emergence of start-ups in small towns can be explained by a high concentration of educated people and high median household income, as well as a stronger impact of bonding (rather than bridging) social capital. These results are based on an analysis of 98 small towns in Iowa with a population of up to 10 thousand people (Artz et al., 2021). Within the framework of knowledge spillover theory of entrepreneurship, the entrepreneurial ecosystem development with an emphasis on the information and knowledge exchange is seen as a means of eliminating institutional voids (Bendickson et al., 2021).

40

T. Melnikova

Against the background of research results postulating the existence of a diversity limit for the purpose of innovative development (too many external partners can reduce innovation activity), the issue of the open innovation factors emergence in the territories characterized by weak economic and spatial concentration and low diversity is developing. Peripheral actors are expected to generate diversity through the use of external grids, better familiarity with local issues, or work with less advanced technological information. The study, based on the example of small towns in eastern Switzerland, outside the Zurich influence zone, found local companies to be the source of diversity in small and medium-sized cities. Interaction between actors (tacit knowledge transfer) occurs through the urban environment, but not the professional ones like in big cities. Public and private knowledge comes from the external environment, the network of subsidiaries, university staff and clients. Thus, the quality of urban space and services is important for knowledge spillovers in small towns, as well as the need for fast and reliable transport and telecommunications connections (Meili & Shearmur, 2019). Agriculture and extractive industries, being the basis of many small and mediumsized cities, are integrated into global networks and are subject to the influence of multi-level competition. This fact diversifies the sources of knowledge. However, the local community, when choosing priority technologies, is largely guided by local ownership, old technologies and the viability of the community (Ris, teiu et al., 2021). At the same time, competing knowledge at the local, regional and national levels forms a balanced approach to urban planning, linking the brand of a small or medium city to research and technology priorities (Vesalon & Cretan, 2019). The dependence of agglomeration processes on the degree of openness for the public and private knowledge transfer is considered, in particular, in the models of new economic geography proposed by F. Martin, J. Ottavino and R. Baldwin. Within the framework of the global spillovers model (for symmetric regions), private knowledge is immobile, public knowledge is freely distributed. The localized spillovers model modifies the initial conditions by adding a barrier to the spread of public knowledge (e.g. distance). For the latter, two conclusions are proposed. If integration between territories takes place only on the basis of reducing the costs of trade policy, then the “core-periphery” result for the territories is the most possible. If trade policy and knowledge transfer freeness are increased simultaneously, a dispersal force can be observed that will lead to a stable symmetric outcome (Baldwin et al., 2005).

3 Materials and Methods The research is based on the economic clustering method under the selected features. Thereafter, the purpose of the cluster analysis was to build groups of small and medium-sized cities that have similar patterns of knowledge transfer freeness.

Clustering of Small and Medium-Sized Cities in Russia Based …

41

3.1 Cluster Analysis Methodology Taking into account the intellectual background, the following features were selected to characterize the presence or absence of barriers to the knowledge spillovers in intra and inter-territorial spaces: urban environment, financial capital, transaction costs, social capital and geographic distance. The quality of urban environment stands for the contribution of the infrastructure and adjacent spaces to the tacit knowledge transfer thus including: • the presence of public and business districts, the level and variety of services provided in them; • development of urban centers of attraction, including sports and cultural facilities, and their safety. The localization of knowledge flows in small and medium-sized cities is highly dependent on the financial capabilities of local companies. The social capital peculiarity refers to self-organization of citizens, and transaction costs decrease contributes to the freeness of knowledge transfer within professional communities, commercial and non-commercial entities and start-ups. The geographic parameter, despite the great influence of the Internet, remains relevant, as it stimulates the movement of the built-in ideas and knowledge. Cluster analysis was carried out on the principles of civilizational diversity. Cities from three different regional development models were taken. The Sverdlovsk region is characterized by a diversified economy, a large number of cities, mountainous terrain, and landlocked. The Oryol region possesses an industrial and agricultural specialization, the absence of cities with a population of over one million people, the predominance of small towns, flat relief, landlocked. In the Republic of Crimea, the development driver is the tourist and recreational complex and sea access. The region is distinguished by an average number of cities with a maximum of no larger than half a million people. The Oryol region is distinguished by more efficient process economic integration, the Sverdlovsk region is faced with a passive process integration, and the Republic of Crimea—with excess one (Melnikova, 2019). This approach is very important, since it has repeatedly emphasized the need to develop unique approaches to the regions of Russia in view of their specific development. A recent study, in particular, compares the levels of inter-municipal differentiation in the Krasnodar Region, Chelyabinsk Region, Kemerovo Region and the Republic of Tatarstan, and draws attention to the fact that such differences are the smallest in the Krasnodar Region, and in the Chelyabinsk Region, for example, the polarization of the economic space is pronounced (Lavrikova & Suvorova, 2020). The methodological basis for cluster analysis was the iterative K-means method integrated into the SPSS Statistics software package. The choice of the optimal number of clusters was based on the contingency table of clusters, as well as on the following criteria: the group occupancy (each cluster should include at least 10% of the total number of objects), the group stability (groups should differ by feature),

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T. Melnikova

Table 2 Contingency table of clusters

Number of cluster objects

Three cluster model

Four cluster model

Five cluster model

1

14

1

20

2

5

4

1

3

30

29

15

4

–

15

9

5

–

–

4

as well as semantic interpretation. A comparative assessment of three, four and five cluster models was carried out (Table 2). Simultaneous fulfillment of all conditions was not observed. The number of clusters increased, and pronounced groups appeared. Thus, the choice was made in favor of the five cluster model, even despite the presence of a cluster consisting of one object.

3.2 Data First, the revealed features were interpreted through indicators (Table 3). For clustering purposes, the data should be comparable for the studied municipalities, therefore, the quality of urban infrastructure is assessed through the rating positions of cities in the Urban Environment Quality Index, formed by the MinConstruction of Russia. The infrastructural opportunities for knowledge exchange are based on the quality of public-business, social-leisure and citywide spaces. Today, it is the only complex data set on the urban environment of municipalities, which includes state Table 3 Features for cluster analysis Feature

Indicator

Unit of measurement

Urban environment

Quality of public-business, social-leisure and adjacent spaces, Points as well as citywide space

Financial capital

Average revenue of local enterprises or organizations

RUB mln

Social capital

The number of non-profit organizations

Ratio per 10 thousand people

Transaction costs

The number of legal entities and individual entrepreneurs in the field of creative industries

Ratio per 10 thousand people

Geography distance

Distance to the nearest big, large, or the largest city in the region

km

Clustering of Small and Medium-Sized Cities in Russia Based …

43

statistics, data from geographic information systems and remote sensing of territories. In the case of a unique city assessment, individual methods are applicable, which should include such important elements as benchmarking and weighting, and the division of the territory into the city itself and the adjacent zone of its agglomeration influence (Devitofrancesco et al., 2016). The presence and availability of financial resources allow to get an overall assessment of private knowledge potential for localization and is measured as the average revenue for all industries in a given city. The indicator is generated by the SPARK Interfax system. Knowledge spillovers in small towns, as noted, depend on the social capital development, in particular the bonding one, as well as on transaction costs. The first indicator can be described as public engagement through the non-profit organization’s ratio per 10 thousand people. The second indicator is best interpreted in terms of the level of entrepreneurial initiatives in creative industries, including software development, architecture, scientific, technical and creative activities. Employees of these companies and individual entrepreneurs can also be perceived as “new Argonauts” or intellectual entrepreneurs. Secondly, we selected the cities. There are 47 cities in the Sverdlovsk region, including 43 small and medium-sized ones. For the analysis, 35 cities were selected as having self-formed indicators of socio-economic development. There are 16 cities on the territory of the Republic of Crimea, including 13 small and medium-sized ones. One city was excluded from the analysis on a similar basis. All six small towns in the Oryol Region were included in the study. Using geographic feature is dual. Its inclusion in the model for cluster analysis, on the one hand, was important, however, on the other hand, it pulled over the entire pattern in the formation of groups. For this reason, the distance feature was excluded from iterations, but left for interpretation of the results. Before starting the iterative process of dividing into groups using the K-means method, we checked the data for normal distribution, due to the financial basis, 4 cities were identified as “outliers” (Yalta, Kachkanar, Irbit, Verkhnyaya Salda) and 49 small and medium-sized cities were left for cluster analysis.

4 Results According to the analysis of variance, the five cluster model clusters are well differentiated for each feature: for the parameters of non-profit organizations and average revenue, the significance levels are zero, for the quality of urban environment—0.016, for the creative industry—0.001. Figure 1 summarizes the results obtained. The first cluster—small cities with high barriers to knowledge transfer and weak agglomeration influence (Table 4). The cluster includes small cities from all given regions. Republic of Crimea: Shelkino, Stary Krym, Dzhankoy, Belogorsk, Bakhchisarai, Armyansk. Sverdlovsk region: Turinsk, Talitsa, Tavda, Nizhnyaya

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T. Melnikova

Fig. 1 The five cluster model

Table 4 The “portrait” of the first cluster

Feature

The final cluster centers

Urban environment

77

Financial capital

9

Social capital

40

Transaction costs

9

Tura, Nizhnyaya Salda, Lesnoy, Kushva, Krasnoufimsk, Karpinsk, Kamyshlov, Verkhoturye, Artemovsky. Oryol region: Dmitrovsk, Bolkhov. By all features, it is characterized by the highest barriers to the localization of knowledge spillovers. These cities are, on average, 129 km away from the nearest large settlements. At the same time, the best accessible large city owns the population from 250 to 499 thousand people. It should be noted that for the Ural cities, the average minimum distance was 163 km, for the Crimean and Orel cities 80 km and 77 km, respectively. For the cities of this cluster, public involvement is more significant than professional involvement. The fifth cluster—small and medium-sized cities with a lack of social capital, low financial barriers and moderate agglomeration influence. Two small and two middle Ural cities have formed a separate cluster: Severouralsk, Krasnouralsk, Revda and Polevskoy. Except for Severouralsk, for these cities the minimum distance to the nearest city with a population of over 100 thousand people, is not exceeding 70 km. The cluster has low financial barriers to knowledge transfer (average revenue of 93 million rubles—the highest score for all clusters). The low level of involvement in

Clustering of Small and Medium-Sized Cities in Russia Based … Table 5 The “portrait” of the third cluster

Feature

The final cluster centers

Urban environment

86

Financial capital

34

Social capital

42

Transaction costs

13

45

public communities and professional creative teams is interpreted as high transaction costs of knowledge diffusion at the local level (11 entities of the creative industry and 34 entities of the non-profit sphere per 10 thousand population, respectively). The third cluster—small and medium-sized cities with moderate barriers to the tacit knowledge spillovers and weak agglomeration influence (Table 5). Republic of Crimea: Krasnoperekopsk, Feodosia. Sverdlovsk region: Sukhoi Log, Rezh, Novaya Lyalya, Nevyansk, Kirovgrad, Ivdel, Bogdanovich, Serov, Novouralsk, Krasnoturinsk, Berezovsky. Oryol region: Mtsensk, Livny. The group of 15 cities has the greatest potential to increase the rate of knowledge spillovers. The cluster has a relatively high average revenue value, as well as low barriers in terms of urban environment and a relatively high social and professional involvement of the population. Considering the background of relatively greater remoteness from large settlements (average minimum distance of 122 km), we can assume the possibility of building interaction in cities in terms of knowledge transfer based on the principles of open innovation. The fourth cluster groups mainly small towns with low barriers to the tacit knowledge spillovers and a strong agglomeration influence (Table 6). Republic of Crimea: Sudak, Saki, Alushta. Sverdlovsk region: Sysert, Zarechny, Alapaevsk, Asbest. Oryol region: Novosil, Maloarkhangelsk. With one of the highest levels of social capital, cities can disseminate tacit knowledge; however, further transformation into private knowledge faces financial barriers. A vicious circle occurs: capital usually moves to cities where effects from the spread of knowledge are observed, in turn, such effects depend on the availability of capital (Matray, 2021). Not taking into account the second cluster, the cities of the fourth cluster have access to the highest quality of urban infrastructure. On the one hand, the advantage of such public spaces is the ability to form an urban community without the spatial exclusion of social classes, on the other hand, the environment has a stimulating effect on the ability to generate ideas (Landry, 2006). However, if an environment is Table 6 The “portrait” of the fourth cluster

Feature

The final cluster centers

Urban environment

87

Financial capital

16

Social capital

72

Transaction costs

16

46

T. Melnikova

needed to integrate virtual reality, then it is possible to achieve positive changes of the knowledge spillovers speed. Pairwise correlations of features revealed the patterns of mutual influence of the transaction costs (creative industry), the quality of the urban environment, and social capital (non-profit organizations) (Fig. 2). Furthermore, the minimum distance to larger cities affects the number of creative and non-profit organizations in the form of an inverse relationship: the shorter the distance, the greater the social and professional involvement. In Fig. 3, we observe a significant dispersion of values within clusters and an increase in the distances between clusters as the non-profit and creative organizations ratio per 10 thousand population grows. The emergence of a large number of interest groups can provoke the isolated interaction. Mediators then begin to play an important role. Much attention in small territorial entities is given to intermediaries between small groups. They lend resilience to network interactions by facilitating the information and knowledge transfer, in particular medium and low technology innovation. Mediators can expand access to knowledge based on their own inter-territorial movements, as well as higher levels of knowledge background. Such relationships

Fig. 2 Pairwise scatter diagrams in the context of each feature

Clustering of Small and Medium-Sized Cities in Russia Based …

47

Fig. 3 Distribution of clusters depending on barriers to social capital and transaction costs

work especially successfully among residents united within the framework of any community. Projects arise from meetings in citywide spaces or through community gatherings (Torre et al., 2018).

5 Discussion The cities of the Sverdlovsk region are mostly concentrated in the first and third clusters, the Republic of Crimea—in the first and fourth, and for the Oryol region, an equal distribution was revealed between the first, third and fourth clusters. For the Ural cities, thus, to a lesser extent, networking is typical for the purpose of social and creative interaction. The peculiarity of network structures in the industrial and tourist regions affects. Industrial cities can include different types of networks: local; producing homogeneous products; aimed at expanding spheres of influence (partnerships, alliances and supply chains and value creation) (Korovin, 2020). Such areas are more likely to have a high level of related variety, which contributes to better adaptation to external shocks and provides a more prominent basis for the territory resilience (Cainelli et al., 2019). For mentioned cities, it is important to reduce financial and infrastructural barriers to the dissemination of knowledge, for example, through projects of high-speed railways connecting less developed territories with more developed ones (Wang & Cai, 2020). Tourist cities (Republic of Crimea) are based on a different type of network structures. They adjust to the spatial structure of tourist flows and may not be tied to a large financial or industrial center. Such networks are based on the interaction of

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T. Melnikova

business and non-profit tourism organizations, including environmental management or marketing cooperation (Gan et al., 2021). Therefore, the bearer of tacit knowledge here can be a tourist, and not a representative of the local community. However, it is noted that tourist networks have a low density or, in other words, lower opportunities for communication and a weaker system of trust, which hinders the transfer of knowledge. At the same time, such networks are built on the basis of small network distances, which increases the speed of knowledge transfer in the network (Raisi et al., 2020). The cities of the Oryol region are little affected by the distance from the center. Their equal representation in the first, third and fourth groups may indicate high financial barriers to the transfer of private knowledge, as well as a strong attachment to historical local diversity. There are “invisible” barriers between the village and the city. A recent study draws attention to the hukou system as an institutional barrier to knowledge diffusion in China (Zhang et al., 2020). Unlike in China, where this tool is used to curb migration from village to city, in central Russia, agricultural specialization to a certain extent contributes to the emergence of isolated network interactions. In particular, creative industries are distinguished by the fact that only two cities have legal entities that develop software, the rest work in the format of individual entrepreneurs (the highest average among the analyzed cities). This situation is less typical for the Republic of Crimea and the Sverdlovsk region. Accordingly, in the cities of the Oryol region, there are most likely scattered network interactions: some are focused on the outside world, and others are involved locally. For such cities, a decrease in the inter-territorial costs of knowledge transfer and the development of bridging social capital is in demand. The obtained results correlate with some foreign studies. An assessment of the knowledge spillovers for 95 Austrian settlements showed that a decrease in the centrality of a city’s location entails a weakening of all types of knowledge bases. Settlements along transport axes or in the zone of agglomeration influence of large cities mainly possess both an analytical and a synthetic knowledge base, which is less common in semi-peripheral and peripheral cities. At the same time, the authors note that “the more dimensions by which the territory is considered peripheral, the more difficult it is to maintain a pronounced knowledge base” (Eder, 2019).

6 Conclusion There are not many studies devoted to a comparative analysis of the knowledge transfer freeness between regions and cities. One of the latter focused on the structural and technological proximity of regions using the example of the Novosibirsk region and discussed the hypothesis of the influence of similar industry specialization on knowledge transfer against the background of weak geographic proximity (Untura et al., 2020).

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In our paper, we tried to identify similar models for localizing the knowledge spillovers in small and medium-sized cities in Russia through an assessment of existing barriers. We would like to note that the knowledge transfer certainly requires a longer planning horizon than the one to which many small and medium-sized Russian companies are accustomed, which are more spontaneous in their decisions, focused on completing work “on the last day” (Makkonen et al., 2018). In this regard, the knowledge infusion model as a process of “regional branching” (closer to industrial cities) is perceived as more sustainable, as new industries emerge as a result of a new combination of localized opportunities of separately related industrial sectors (Zhou et al., 2019). However, as has been shown for tourist cities, the development of such territories depends on built-in knowledge in the labor force, and its dissemination among people further increases the competitiveness of companies in such cities. Tourism industry professionals are more likely to share knowledge about enhancing innovations than radical ones, which is more common in other industries (Kim et al., 2021). The main advantage of creative innovations (within the framework of creative industries) is that most of them integrate a review of existing knowledge, being especially important for small towns, having no opportunities to attract scientific leaders. Accordingly, the formation of such innovations is tied to skills and personal connections. An important issue not been covered in the paper, but requires further research, is related to the speed of knowledge spread. Studies show it depends both on the industry and can differ between product and process innovations. In addition, a technological gap between firms and organizations is a prerequisite for spillovers, and imitative behavior is a prerequisite (Xu et al., 2019).

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Hidden Single-Industry Towns in Transition Irina Turgel , Aksanat Panzabekova , and Irina Antonova

Abstract The article is devoted to the territorial distribution of single-industry towns, which, on the one hand, are large centers for the placement of industry, on the other, concentrations of socioeconomic problems. The purpose of the study is to identify the spatial features of the distribution of single-industry towns, taking into account the “hidden” single-industry towns. The authors propose to consider “hidden” single-industry towns, those whose share of the single-industry is not visible in statistics, or whose city-forming enterprise is in bankruptcy. Based on the involvement of microdata in 2013–2017, the authors propose six groups of single-industry towns. We consider the Kemerovo region case study and conclude the key features of the development of Russian single-industry towns in comparison to international experience: the participation of Internet technologies in the formation of interaction networks, the prevalence of competition for federal funding over the construction of networks of intercity interaction, geographical factor, and the strong influence of the path dependence.

I. Turgel · I. Antonova (B) Department of Theory, Methodology and Legal Support of State and Municipal Administration, Ural Federal University, 19 Mira Av., 620002 Yekaterinburg, Russian Federation e-mail: [email protected] I. Turgel e-mail: [email protected] A. Panzabekova Institute of Economics Committee of Science of the Ministry of Education, 29 Kurmangazy, Almaty, Republic of Kazakhstan I. Antonova School of Engineering Entrepreneurship, Tomsk Polytechnic University, 31 Lenina Av., 634050 Tomsk, Russian Federation Institute of Economics and Management, Tomsk State University, 36 Lenina Av., 634050 Tomsk, Russian Federation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_4

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1 Introduction Monotown, single-industry town, company town, and resource town are a group of concepts that in modern economic literature can be considered in a single terminological field. Being an extremely relevant issue in Russian theory and especially in practice, it does not arouse similar interest in the foreign scientific community, since the issue of diversification and development of resource towns is considered to be solved 30–50 years ago. Nevertheless, the questions of the theory of single-industry towns’ development on the example of the Russian experience open up new prospects for the elaboration of the model of territorial development. Answering the question, how similar are the stages passed by monotowns of Russia with a half-century lag from the same experience before, there is an opportunity to substantiate the features of the model of territorial development (cyclic, spiral, dynamic, evolutionary, etc.) and the possibility of applying measures in similar conditions. This is where the authors see the relevance of this study. Digitalization is an undeniable factor and trend of modern urban development. At the same time, the need for the accumulation of information is replaced by the desire for network use of it, as well as the integration of Internet technologies with socioeconomic systems (Cherkasova, 2021; Costa et al., 2019). Digital technologies make it easier and more efficient to create network interactions at various levels, and they also modify the quality of productivity (Baldwin, 2018). From the perspective of the new institutional economy, the network is, along with the market and hierarchy, one of the three mechanisms of coordination in which partners align their functions and establish long-term relationships, but do not unify them (Walker, 2003). Thus, the purpose of this study is to identify the features of the modern transformation of single-industry towns as a new stage in the evolution of the model of resource towns’ development. However, the achievement of this goal is empirically limited by the presence of so-called “hidden” single-industry towns. The specifics of these towns are: (1) attribution of a town-forming enterprise to a branch; (2) bankruptcy of a town-forming enterprise; (3) the town-forming industry is not reflected in the financial statements of single-industry towns; (4) single-industry towns fall out of the general sample in case of comparison with other single-industry towns. In this regard, the article proposes a new, original method of “restoring” data on the share of revenue of the monoindustry in “hidden” single-industry towns. The other gap is in the theory of the term “single-industry town” in Russian and international theory that we offer to solve through literature review and concepts comparison. The logic of this article is presented in the following sections. The introduction describes the relevance and purpose of the study. The lithobore analyzes the main theories and models of single-industry towns’ development. The section Company Towns Versus Monotowns compares the approaches to the concept of monotowns in Russian and international practice. The methodology describes the research base and methods. The results present the empirical data obtained. In the discussions,

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there is a discussion of the conditions of the emergence of innovative search. The conclusions show the main results of the study.

2 Literature Review The problem of economic development of single-industry towns has become extremely relevant in the last decade in post-communism economies, including the Russian Federation and Eastern Europe (Risteiu et al., 2021; Vesalon et al., 2013). It is determined not only by their significant number among the settlements (319–323 settlements in the official list of company towns in 2014–2021 in Russia), but also by the specifics of their territorial location, namely, a greater concentration in certain regions. Early academic papers devoted to single-industry towns depicted cultural changes in mining towns (Harold, 1936; Landis, 1938) and formed the basis of town life-cycle theory, which defined the rise and declining stages of company towns (Antonova, 2018) and single-industry towns (Lucas, 1971). Booms and declines were vividly described in automobile company towns (Hill, 1987) and in railway towns (Drummond, 1995). The life-cycle theory was further developed as a resourcecommunity cycle (Lockie et al., 2009; Taylor et al., 2003). The declining stage of the mining industry inspired many works about mining towns (Bebbington et al., 2008; Forsey, 2015; He et al., 2017; Littlewood, 2014; Lockie et al., 2009; Rogerson, 2012; Shann, 2012; Skeard, 2015; Tonts et al., 2012). Case studies of mining towns, in particular, have allowed researchers to formulate the “paradox of poverty in the midst of resource abundance” (Bebbington et al., 2008; Tonts, et al., 2012). Among the variety of studies analyzing foreign experience, in our opinion, the most structured study is Zamyatina and Pilyasov (2015), which distinguishes two stages of restructuring of foreign cities: “superficial” and “innovative search.” Taking these stages as a basis, this article proposes to compare these stages with the arguments in favor of the network concept of single-industry town development. Considering the case study of the Rosia Montana gold-mining project, Vesalon & Creton (2013) say that, Mining galleries can also be used in a variety of small industries and manufacturing … the opening of cinemas in the old mines or using the galleries as warehouses for a variety of products needing the temperature and humidity parameters.

Practical experience of mine closures shows that water, explosive methane, and high radiation background accumulate in old mines. All this does not allow us to implement the proposed ideas of restructuring single-industry towns of the coal profile. At the first, superficial stage, there is inertial development of the single-industry town based on the existing specialization, the competitiveness of which is improved by adding new technologies to it. If the old industry is not completely destroyed, this attempt may yield some results, but only to save the city (Zamyatina & Pilyasov, 2015). This stage differs in the specific networks that are being built between the

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main actors. At this stage, communication is built mainly vertically (top-down) between federal, regional, and municipal authorities with large corporate structures— the owners of the town-forming enterprise. This stage tests the viability of the old industrial sector. The success of economic diversification in small cities, as international practice, depends on the degree of involvement of local and regional authorities during the formation of the institutional environment (Oborin et al., 2017). However, the old, once-effective network strengthens, and development is blocked (reversed) by changes in the current situation. The old network system is resistant to external influences. It is clearly seen that Russian single-industry towns are going through this very stage of development. Nevertheless, the question arises whether the conditions of passing this stage in foreign and Russian cities are the same or whether the halfcentury period is a critical period for the direct application of international experience, and also whether the conditions are similar. If the answer is “yes,” then a “circular” or “cyclical” model of development of single-industry towns is formed. If the answer to this question is “no,” then an evolutionary model of single-industry towns’ development is formed. In such a model, similar stages are distinguished, but they unfold with some transformation. The “spiral” model most closely describes this model. Taking into account the network interaction of the main actors of monotown development (academia, industry, and government) in this case, it is logical to quote the triple helix model (Cai & Etzkowitz, 2020). Another question is what exactly creates the resistance of the network system and how to overcome it. On the one hand, according to the theory of endogenous economic growth, the critical level of internal factors of growth should be accumulated (Aghion et al., 1998; Grossman & Helpman, 1991; Romer, 1994). Respectively, the territory of a single-industry town concentrates capital and investments in R&D and human capital. On the other hand, this is possible only if the level of wages is high. Thus, Storper (2014) classifies cities according to the level of income and specialization, with people in Storper’s concept going precisely for high wages. Taking into account the fact that specialization exceeds diversification by the rate of economic growth only at later stages of development with relatively high per capita income, which can be described by a U-shaped relationship (Cadot et al., 2011), we should conclude that the current level of specialization and low per capita income makes the current system of management of single-industry towns extremely inefficient. On the contrary, a number of studies have shown the particular importance of small cities in overcoming poverty and diversifying rural areas (Christiaensen & Todo, 2014; Ferré et al., 2012). In particular, an analysis of cross-cultural panel data, which included 51 developing countries, suggests that people find opportunities in small, nearby cities easier and faster than in remote, large cities (Christiaensen et al., 2013). Zamyatina and Pilyasov (2015) associate the transition to the second stage of the development of single-industry towns according to the international experience with the destruction of previous inter-firm networks. The authors conclude that to create new, effective networks, it is not enough to modernize the old ones. The resistance of the network system will resist. Thus, the previous networks of interaction should be broken, the system is necessary to become “flat” and switch to outsourcing and

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subcontracting. The practice of urban development in Russia has a positive experience in creating platforms for the mutually beneficial participation of various parties to contract agreements: Kazan, the subcontracting platform. At this stage, all single-industry towns can be divided into two groups with: (1) a large share of the old core industry, especially with a small size of the city with a city-forming enterprise—a branch of a higher level structure located outside the single-industry town; (2) a low share of the old core industry, with the former networks between the cityforming enterprise, other firms, and other institutions destroyed, especially in the event of the bankruptcy of such an enterprise. In the first group, the research assumes a high level of resistance. The second group has more opportunities to move to the second stage of development in accordance with international experience. In this case, new networks, provided from the initiative from above to the initiative from below, replace the previous ones, more effective, where the authorities and the remnants of the old industry play partnership relations. Such conditions are necessary for the emergence of innovative search in singleindustry towns (Zamyatina & Pilyasov, 2015). The authors of this study express a concern that, in the second stage, digitalization of the economy for single-industry towns may play a cruel joke. Cherkasova M. A. emphasizes the role of networking in both the digitalization of the economy and the construction of a new effective vertical of power (Cherkasova, 2021). In these conditions building the vertical of power may act as a factor of resistance to the old network system counteracting the innovative search in the territory. The theory of knowledge transfer shows that knowledge exchanges in the clusters within one industrial specialization: MAR-effects (Romer, 1994), and in the differentiated structure of industrial specialization according to Jacobs (1969). This transfer can support the development of the innovative search and can be applied to single-industry towns in both groups. The role of the geographical factor is a specific feature of single-industry towns in the Russian Federation. This issue is considered by followers of new economic geography, who investigate the influence of geographical proximity of cities on the exchange of codified and tacit knowledge (Leamer & Storper, 2001) and on the production of innovation (Tolbert & Zucker, 1996). Zamyatina and Pilyasov (2015) emphasize the role of the educational infrastructure of a city in the innovative search process, including universities, research institutes, and different expert groups. The role of the educational infrastructure is in the generation of ideas, which are often in short supply even if the state funding is abundant. In case of small-scaled city, a local school, community council, community, or municipal foundation can assume this role. These elements of educational infrastructure should be able to generate and accumulate innovative ideas without any limitations from the outside. In this regard, we offer to compare the existing educational infrastructure with the geographical location of single-industry towns in Russia. Taking into account the geographical factor, the theory of networks is transposed to a higher level and moves to the concept of “networkcity,” which is understood

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as an association of two or more independent cities for the purpose of cooperation in the field of infrastructure, innovative development or cultural exchange (Batten, 1995). Subsequent studies have looked at inter-regional and international networks of cities (Taylor et al., 2003). Networking between cities shapes information exchange, inter-firm cooperation, investment incentives, and tourist flows in cities (Campbell, 2013). Oborin et al. (2017) propose to apply the network mechanism to improve the efficiency of planning and development of not only single-industry towns, but also other small towns. The mentioned authors assume that the network mechanism will stimulate the following: infrastructure development; exchange of labor resources and competencies; exchange of best practices of regulation and municipal management; creation of inter-firm networks; coordination of interests in the economic and social sphere; implementation of joint projects. Thus, the study of single-industry towns’ development can shift the focus from the financial position of the town-forming enterprise to inter-city networks potential. The application of international experience in the development of single-industry towns is associated with the problem of divergence of the semantic field of various terms reflecting similar objects of research. There is no possibility to compare the results of empirical research on Russian single-industry towns with international experience while having no equal base for comparison. In this regard, we propose to systematize and compare the available terminological field of the concept of singleindustry towns in Russian and international literature.

3 Company Towns Versus Monotowns On the one hand, company towns are considered as a company asset (Green, 2012; Lucas & Tepperman, 2008; Littelwood, 2014). Abdel-Rahman (2000) specified that company towns are “formed by a developer and has a single firm.” On the other hand, company towns also are called “resource towns” and “mining towns” (Littlewood, 2014), rather than single-industry towns. Apart from high diversity and dependence on core industries, such as iron mining (Landis, 1938), automobiles (Hill, 1987), railways (Drummond, 1995), mining (Shann, 2012; Tonts et al., 2012), textiles and timber (Scott & Bennett, 2015), these towns often have the related problems of rising unemployment, increased emigration, tax base erosion, higher crime (Akpadock, 1996), environmental deterioration, lack of sustainable development (Lai & Lorne, 2006), and low corporate social responsibility (Littlewood, 2014). To differentiate between the emerging goals of company towns, Green (2012) defined two basic models of modern company towns: an “exploitation ville” and “socially benign.” In the U.S., Canada, and other capitalist countries, the emergence of company towns was associated with the concentration of private ownership as a natural evolutionary process. In Russia, by contrast, the emergence of company towns was associated with the artificial creation of a city by the government for industry maintenance, excluding private ownership of houses and infrastructure.

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High relevance upon the company town concept in Russia relates directly to the urgent problems facing the country over the past two decades. The first widescale research, conducted by the Russian Expert Institute (Lipsits, 2000), counted approximately 467 settlements, classified and analyzed their geographical expansion, and listed their core problems. Lubovniy & Kuznetsova (2004) detailed the typology of company towns and analyzed their socioeconomic development. Zubarevich et al. (2010) assessed the relationship between crises and company towns’ economies, focusing on special inequalities. While the Russian academic community uses various terms for company towns, such as “monoprofile settlement,” “monospecialized town,” “single-industry town,” and “sector-specific town,” the most general term used is “monotown” (Kozhin et al., 2008). A monotown has close links between the town and its economic and social aspects, which can significantly impact the town’s fate. Different definitions of the concept existed before the official listing of “monotown” in Russia’s Government Resolution of July 29, 2014. The listing clearly defines company towns as follows: • a municipal urban district or an urban settlement with a population of more than 3,000 people; • the largest companies (several companies within the same industry sector) employ over 20% of the settlement; • the company town is involved in mining (excluding oil and gas or industrial production); • the past-five-year data. The resolution also classified towns according to their socioeconomic well-being. The 323 company towns in 2021 are divided into three groups: (1) those with a high risk of socioeconomic decline, (2) those with a low risk of socioeconomic decline, and (3) those with stable conditions. Thus, comparing Russia with other countries, the Russian definition of “company town” has individual characteristics. The author’s definition is depicted in Fig. 1. In Russia, “monotown” is used most often to depict a company town. The basic difference between Russia and other countries is infrastructure ownership. The Soviet period outlawed the private ownership of houses, land, or roads. Most of the infrastructure (e.g., canteens and kindergartens) was on the balance of town-forming enterprises in the towns. Consequently, during the transformation of the Russian economy in the 1990s, enterprises in company towns lacked financial funding for infrastructure that accompanied a declining return on capital. These problems defined the process of transferring social infrastructure assets to the municipal level, finally allowing enterprises in towns to avoid corporate social responsibility. A company’s financial decline and public unrest induced sales of houses to the general population, which changed the ownership type and basic features of the settlement. By contrast, in the U.S. and other capitalist countries, company towns were originally constructed and owned by the capitalist enterprises. Since “monotown” is the most comprehensive concept among the other presented terms, the identified patterns of development of Russian monotowns can be applied

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Fig. 1 Comparative analysis of company town concept

for comparison with international research results. We present the methodology of this study.

4 Foreign and National Investments A key factor in the development of single-industry towns is the attraction of investments in the business development of single-industry towns, including national and foreign direct investments. In general, there are two points of view on the effect of foreign direct investment on the economy of the host country (Crescenzi et al., 2021). On the one hand, with the arrival of investments in TNCs, the efficiency of vertical effects increases, there is a transfer of innovations, an increase in wages. Competition in the market increases the efficiency of the horizontal effects. Existing companies in the market in the industry are “pulling up” to the level of foreign competitors. On the other hand, from the perspective of spatial development, foreign direct investment causes an increase in inequality between employees with high and low qualifications as well as some national security concerns. National investments can be carried out within the framework of a vertical and horizontal approach to the development of single-industry towns. The vertical, initiated by the state, the implementation of which took place in 2008–2019, consisted in the development of comprehensive investment plans for the modernization of a single-industry town, including investment and infrastructure projects. The key financial institution that funded the developed program was “Single-industry Towns

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Development Fund” established by Vnesheconombank. This case was in close relation with the international experience, i.e., Romanian case of the regional development through EU structural funding by “Fondul Na¸tional de Dezvoltarea Regional˘a” (Cret, an et al., 2017). During the period of 2014–2017, the fund spent was 17.76 billion rubles from the state budget with the aim to develop the most depressing single-industry towns. The state subsidy was distributed as follows: 2.4 billion rubles (13,5%) were provided for labor and other expenses of the fund, the remaining funds were directed under cofinancing programs to 32 single-industry towns shown in Fig. 2. The leading position in terms of the amount of funds raised is Ust-Katav in Chelyabinsk region, which received over 1.4 billion rubles of state subsidies under the program of cofinancing the development of a single-industry town. At the same time, this particular town is considered “hidden” due to the fact that before 2017, it was the branch of the other company (State space research and production center named after Khrunichev). That is why its financial data was “hidden.” Currently, this enterprise is reregistered as non-public Joint Stock Company “Ust-Katavsky Car-Building Plant.” Thus, we face a serious contradiction: the largest city in terms of subsidies cannot be included in the general sample of single-industry towns for analysis, due to the fact that it is not possible to correctly assess the main indicators of the monoindustry: the share of the monoindustry in revenue, fixed assets, and wages. However, these expenses resulted in the recognition of the single-industry towns development program as ineffective, as well as the expenses themselves. According to the report on the “monitoring and evaluation of the progress of implementation 16,00,000 14,00,000 12,00,000 10,00,000 8,00,000 6,00,000 4,00,000

0

Naberezhnye Chelny Zelenodolsk Nizhnekamsk Anzhero-Sudzhensk Jurga Tashtagol Novokuznetsk Krasnoturjinsk Yauz Belokholunitsk Kameshkovo Cherepovetz Kumertau Belebey Kaspiysk Kanash Dimitrovgrad Zarinsk Navolokskoe Ust-Katav Kotovsk Sarapul Glazov Votkinsk Serdobsk Pavlovsk Novotroitsk Pogarsk Nadvoitsky Vichuga Seversk Selenginsk

2,00,000

Fig. 2 Funds actually transferred to the single industry town of the Russian Federation, thousand rubbles

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of the priority program of complex development of single-industry towns” by the Accounting Commission: The measures of state support for single-industry towns have not yielded tangible results. As before, more than half of their residents assess the situation in their city as a “crisis”. The dependence of the revenue base of the budgets of single-industry towns on the financial condition of the town-forming enterprises remains. When developing the passport of the program … the readiness of the regions for its activities was not evaluated (Agaptsov & Shtorin, 2019).

The Accounting Commission concluded that during the period of the program implementation from 2016 to 2018, business activity decreased, the number of liquidated organizations exceeded the newly registered ones. The average monthly salary of employees of organizations in single-industry towns is lower than the national average, the unemployment rate is growing, the population is decreasing (Agaptsov & Shtorin, 2019). At the same time, the focus shifts to the parameters of investment attractiveness of programs and projects to attract global capital investments (Stuvøy & Shirobokova, 2021). Nevertheless, investments cannot be considered as the only factor of the single-industry town development that proves the case of Detroit received significant investments and finally has bankrupted in 2013 (Biles & Rose, 2021). Investments of all types should primarily achieve the general aim of town development: improving the level and quality of life of the population. In 2014, the national investments strategy changed the focus from the vast variety of complex investment plants to the more restricted territories in single-industry towns entitled “territories of advanced development” (Stuvøy & Shirobokova, 2021). In comparison to the previous approach to the national investments, the state focused on the short list of single-industry towns and the special arias within with the taxation incentives.

5 Methodology We offer to consider “hidden” single-industry towns in accordance with the stage of economic restructuring, highlighted by Zamyatina and Pilyasov (2015). The database of the research is formed on the basis of the microdata of the financial statements, presented in the SPARK information-analytical system, for the entire list of singleindustry towns for 2013–2017. This database represents one of the most comprehensive databases conducted in similar studies on single-industry towns. Within the framework of the study, we offer the following web application: https://monotowns. web.app/, which reflects the cartographic material on the full list of single-industry towns. Assuming that Russian single-industry towns are experiencing the first stage of development, associated with the addition of new technologies in core industries,

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this study focuses on the dynamics of the share of the core industry and the specifics of its spatial location. The following indicators are used for these tasks: (1) Share of the core industry in terms of revenues, fixed assets, wages: Cm C Ri = Σ n j=1

Cj

,

where C R i —the share of a core industry by indicator i (revenue, fixed assets, wages); Cm —absolute value of indicator i, corresponding to a core industry; C j —absolute value of the indicator corresponding to the enterprise; n—number of enterprises. (2) Herfindahl–Hirschman index of revenues, fixed assets, and wages: H H Ii =

n Σ

C R ij

2

j=1

where C R ij —the company’s share of the indicator i (revenue, fixed assets, wages). (3) Income per capita is the ratio of the aggregate labor remuneration fund to the number of population in a single-industry town. The application of these indicators makes it possible to identify the socalled “hidden” single-industry towns. “Hidden” core industries on the map are represented by black dots. For the purpose of this study, it is proposed to consider single-industry towns with zero shares of revenues. It can happen for two reasons: the city-forming enterprise is a branch of a higher structure; the city-forming enterprise is a bankrupt. In the latter case, in order to clarify the data, it is necessary to check the availability of fixed assets by core industry. The branch of the enterprise within a city will show zero values by all three indicators (revenue, wages, and fixed assets).

6 Results In accordance with the methodological part of the research, the results are visualised as follows; Figs. 3, 4, 5, 6 and 7 show the estimated values of the share of singleindustry towns by revenue, wages, and fixed assets. Taking into account the proximity of location, the following co-located groups of single-industry towns are clearly distinguished: (1) Siberian; (2) Ural; (3) Central;

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Fig. 3 The shares of core industries by revenues in 2013 and 2017

Fig. 4 The shares of core industries by wages in 2013 and 2017

Fig. 5 The shares of core industries by fixed assets in 2013 and 2017

Fig. 6 Income per capita in 2013 and 2017

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Fig. 7 Herfindahl–Hirschman indices by revenues in 2013 and 2017

(4) Northern; (5) Far Eastern and Transbaikalian. The highest concentration of single-industry towns is observed in the Siberian grouping. We offer to consider “hidden” single-industry towns in accordance with the stage of economic restructuring, highlighted by Zamyatina and Pilyasov (2015). The Siberian group of single-industry towns is represented by single-industry towns mostly located in Kemerovo region, which has the largest number of single-industry towns within its boundaries (24). Krasnoyarsk monotowns can also be locally referred to them. This area is characterized by both a cluster of single-industry towns with a high share of old industry and the largest number of “hidden” single-industry towns, including: Taiga, Salair, Krasnobrodsky, Mundybash, and Sheregesh. Town-forming enterprise in Taiga is a branch of PJSC Russian Railways. The network interaction of this enterprise with the structure of a higher level is so strong that it is not possible to expect a transition to the second stage of development of this single-industry town. There remains only the prospect of individual innovations in the core activity. Conducting a study of the framework of learning infrastructure of single-industry towns on the example of the system of placement of universities, the authors conclude that the identified groups of single-industry towns and the territorial concentration of universities generally correspond to each other, which is shown in Fig. 8. Nevertheless, universities are generally located in regional centers and in large industrial cities, while single-industry towns remain at a distance. This is especially noticeable for the territories with a large concentration of single-industry towns— Kemerovo, Sverdlovsk, and Chelyabinsk regions. Thus, in the Kemerovo region, the universities do not cover most of the single-industry towns, especially the “hidden” ones. In such circumstances, it is extremely important to develop other, more flexible, elements of the training infrastructure. With the bankruptcy of town-forming enterprises of Sheregesh and Salair, we define a break-up of existing networks and the construction of new, tourism-oriented ones. Salair is developing in the image of a more successful Sheregesh, which has become not just a centre of ski tourism, but also formed an example of educational infrastructure on the basis of the forums of entrepreneurs. The only concern is the

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Fig. 8 Educational infrastructure (universities of the Russian Federation) and single-industry towns’ allocation (in terms of the share of single-industry town revenues) in 2017

strengthening of the vertical power structures, which is formally manifested in the displacement of small businesses and the concentration of hotels in the ski resort Mount Zelenaya in the ownership of a limited circle of individuals. The latter reflects the path dependence effect as an essential factor in Kemerovo region. At the same time, Mundybash and Krasnobrodsky, having ruined old networks, have not yet found the trajectory of innovative search. Thus, we can see how the towns in the official list of monotowns show 0 shares in revenues (Fig. 9). We have revealed a significant scale of geographical distribution of hidden singleindustry towns. The Ural group of single-industry towns, which is comparable to the Siberian group by the number and shares of core industries, but inferior by the number

Fig. 9 Evaluated shares of the core industry by revenues in the Kemerovo region (with focus on the “hidden” single-industry towns)

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of “hidden” single-industry towns, including Volchansk, Nyazepetrovsk, and UstKatav. The peculiarities of the central grouping of single-industry towns are insignificant displays of hidden single-industry owned by revenues and larger displays— by fixed assets and wages. Thus, we conclude the localization of bankruptcy of town-forming enterprises in this territory. Central group has the maximum number of single-industry towns, whose network interactions are destroyed and they are ready (with proper strategic management) to move to the second stage of development in accordance with foreign experience. Central group of single-industry tows includes the following “hidden” towns: Demjanovo, Petrovsky, Vyska, Ivot, Lyubokhna, Selzo, Kolashnikovo, Veliko-Oktyabrsky. The Northern group of singleindustry towns includes Revda and Monchegorsk. Zabaikalskaya—Sherlovaya Gora, Vershino-Darasunsky, and Kokuy “hidden” towns. Far Eastern “hidden” towns are Luchegorsk and Vostok.

7 Discussions The identified problems of the methodology for assessing the concentration of singleindustry towns testify to the need to revise the approach to the study of singleindustry towns. Failure of the assessment methodology of “hidden” single-industry towns does not allow us to monitor the results of single-industry towns and assess the effectiveness of the implemented programs. The results of the study suggest the possibility of singling out a group of connected single-industry towns, which allows us to propose the development of the network concept of single-industry town development management. As Zamyatina and Pilyasov (2015) note, institutional projects alone in singleindustry towns are not capable of causing innovative search. We should add to this idea that, in general, in order for infrastructure projects to begin to form the investment climate of a single-industry town, to create conditions for innovative search, it should be a fairly large-scale comprehensive investment. Whereas, implementation of infrastructure projects takes place in conditions of double (duplicate) financing. Such insignificant changes in the infrastructure are simply not able to stimulate investor interest (Agaptsov & Shtorin, 2019). Different territorial cohesion of single-industry towns also suggests different degrees of “networking” of single-industry towns both among themselves and with other small towns, as well as with large agglomerations. The maximum cohesion is observed in the Kemerovo, Sverdlovsk, and Chelyabinsk regions. At the same time, the regional authorities of the Kemerovo region are known to make attempts to exchange experiences between the cities. However, repeated statements by representatives of municipalities about their unwillingness to adopt the experience of individual single-industry towns indicate that the implementation of the process has a number of additional “pitfalls.” Fore instance, the reluctance of the administration of Taiga refuse to consider the experience of Yurga, despite the fact that it was Yurga that became one of the first pilot projects for the development of single-industry towns

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in Russia and has significant results that can be evaluated and analyzed, was revealed (Antonova, 2018). The unwillingness to learn from the experience of more efficient single-industry towns indicates that the region has a low potential for building inter-city networks, on the contrary, the competition for attracting federal funding is brightly manifested. In such circumstances, building interaction, taking into account the minimization of personal influence on municipal decision-making is an important task. After all, it is the trust that allows to ensure the uninterrupted involvement in the management network of single-industry towns.

8 Conclusions “Hidden” single-industry towns are a key element in the transition from the first stage of single-industry towns’ restructuring to the second stage, highlighted on the basis of international experience. This study proposes to identify and characterize “hidden” single-industry towns within the proposed groups of single-industry towns (Siberian, Ural, Central, Northern, Far Eastern, and Transbaikal). The example of the Kemerovo region identifies “hidden” single-industry towns that have lost the old network interaction between the state and the core industry, such as Sheregesh and Salair. These single-industry towns have established new network relationships and reoriented the economy to the sphere of tourism (ski resorts) with varying degrees of efficiency. Other single-industry towns (Mundybash and Krasnobrodsky), having a ruined network system, failed to conduct innovative searches and transform the economy. To summarize the study, the key features of the development of Russian singleindustry towns in comparison with international experience are the participation of Internet technologies in the formation of interaction networks, the prevalence of competition for federal funding over the construction of networks of inter-city interaction, geographical factor and the strong influence of the path dependence effect even on those single-industry towns, whose network relationships with the former old industry are destroyed. The limitations of this study are related to the lack of reliable methods for estimating the numerical values of indicators of hidden single-industry towns: the share of monoindustry, concentration, and diversification. The follow-up research is related to the development of a methodology for “restoring” data for hidden mono-industries in order to include them in the general sample for comparison. Otherwise, the administration of the city of Taiga is limited to a whole range of benchmarking methods for developing development directions. Acknowledgements This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP09260795). This research was supported by TPU development program.

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Appendix

Single-industry town

Region

Funds actually transferred to the subject of the Russian Federation, thou rubbles

Naberezhnye Chelny

Tatar republic

851 491,81

Zelenodolsk

Tatar republic

922 773,25

Nizhnekamsk

Tatar republic

327,732,16

Anzhero-Sudzhensk

Kemerovo region

982 939,21

Jurga

Kemerovo region

129 318,28

Tashtagol

Kemerovo region

271 333,68

Novokuznetsk

Kemerovo region

0

Krasnoturjinsk

Sverdlovsk region 581,977,91

Yauz

Kirov region

174,801,07

Belokholunitsk

Kirov region

241 840,88

Kameshkovo

Vladimir region

598 440,47

Cherepovetz

Vologotsk region

809 913,40

Kumertau

Republic of Bashkortostan

250,252,52

Belebey

Republic of Bashkortostan

209 462,80

Kaspiysk

Republic of Dagestan

549 145,91

Kanash

Republic of Chuvash

299,165,55

Dimitrovgrad

Ulyanovsk region 77,781,79

Zarinsk

Altai kray

46 147,22

Navolokskoe

Ivanovo region

75 000,00

Ust-Katav

Chelyabinsk region

1 481 509,43

Kotovsk

Tambov region

21,251,79

Sarapul

Udmurt republic

73 978,66

Glazov

Udmurt republic

26 258,50

Votkinsk

Udmurt republic

23 534,84

Serdobsk

Penza region

44 237,25

Pavlovsk

Voronezh Region

0

Novotroitsk

Orenburg region

12 640,00

Pogarsk

Bryansk region

0

Nadvoitsky

Republic of Karelia

225 482,69

Vichuga

Ivanovo region

0 (continued)

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(continued) Single-industry town

Region

Funds actually transferred to the subject of the Russian Federation, thou rubbles

Seversk

Tomsk region

0

Selenginsk

Republic of Buratia

0

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The Level of Urbanization of the Regions of Kazakhstan: Assessment by the Index Method Aksana Panzabekova , Lidiya Bekenova , and Aksaule Zhanbozova

Abstract The study of urbanization processes is a topical issue as it represents a global trend, and the concentration of economic, innovative, scientific, public activity is able to increase significantly the economic efficiency. At the same time, the urbanization process development enhances the necessity to determine the optimal urbanization level to support the balanced development of the country. This investigation is aimed at estimating the urbanization level in Kazakhstan regions, revealing the difference among the regions, and reasons for the difference for more balanced policy in the field of urbanization. The investigation assumes the application of the index method for urbanization level estimation. By inclusion of the cities’ number and its sizes to the index, we managed to rank Kazakhstan cities by the following urbanization levels: very high; high; middle; low, and very low. These results can be used at elaborating the governmental policy on the urbanization process management in Kazakhstan.

1 Introduction In the whole world, the cities represent centers of the community development and prosperity. Cities concentrate the economic activity, allow the existence of very specific types of activity without which the cotemporary development hardly can be imagined: science, various services, innovative entrepreneurship. A. Panzabekova (B) · A. Zhanbozova Institute of Economics of the Ministry of Science and Higher Education, 29, Kurmangazy St., Almaty 050010, Republic of Kazakhstan e-mail: [email protected] A. Zhanbozova e-mail: [email protected] L. Bekenova Almaty Humanitarian and Economic University, 36, Momishuli St., Almaty 050031, Republic of Kazakhstan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_5

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The UN reports on the estimation of the cities’ development show positive correlation between the economic growth rate and urban population share as well as labor efficiency and city size (UN HABITAT, 2010). Rosenthal and Strange (2004) indicate on more than twofold increase (from 3 to 8%) in labor productivity under the city population doubling. Nakamura (1985) came to a conclusion that, in Japan, the doubling of the city population will increase the economic productivity by 3.4%. By the estimations of Ciccone and Hall (1996), the doubling of the city population will lead to the productivity increase by 6% for some USA regions. In such highly urbanized European countries as France, Great Britain, Spain, Germany, Italy, the productivity growth will be 4.5% if the city population is doubled. Thus, the urbanization is definitely one of the factors of economic efficiency increase and favors the economy growth. That is why, the investigation of the urbanization is a topical issue for scientific elaborations, and the study of methods of analysis and estimation of urbanization allows understanding of the instruments available for this. The urbanization process is determined by several specific features: 1. The growth of urban population. The more people are living in cities, the higher is their role in the social and community development. 2. The growth of population concentration in cities. The growth of people number itself does not show the density of cities population and the share of population in the whole population number. This indicator shows the structural changes. 3. Increase of city area. The city space is a unique type of landscape arranging the life in a quite different way. The increase in cities’ area definitely changes the lifestyle of citizens. 4. The increase of cities’ share in the country’s GDP. As far as the cities’ economy becomes more complicated and diverse, its significance in the economy is also growing. The urbanization as a process of growth of the cities’ role, its number, and population is an important factor of the global and local development. In the developed countries, the growth of the urban population occurs due to natural reasons. In some cases, even the decrease in urban population is observed due to the settling in suburbs. In the developing countries, the rate of urban population growth is higher than in the developed ones due to rural population moving to cities. The main problem for these countries is disbalance in the social and economic development of regions caused by a range of factors. The understanding of difference among the regions and reasons of this difference will allow for a more balanced policy in the field of urbanization.

2 Literature Review Statistical methods are the most important for the urbanization study in definite regions as these create a base for the analysis of the spatial and temporal interconnections among the urbanization indicators (Chebanova, 2013). For the initial estimation, the methods of descriptive statistics are used (Peng et al., 2016; Brown

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et al., 2015). The further analysis of statistical data is possible in the form of both, absolute and relative indicators. The indicators analysis in absolute terms allows identifying and comparing the sizes of the studied issues (for example, urban population number) and its quantitative parameters (Pivovarov, 1996). The methods of economic and mathematical modeling allow using indicators that do not show directly the physical parameters of cities. Its advantage is measurability, large variability, and relatively easy data acquisition process (Ilyina, 2012). Some works, for the urbanization tendencies estimation, apply the method of least squares (LS) (Doan & Oduro, 2012). This method assumes the creation of a regression line through the minimization of sum squares of vertical distances from dots to (hypothetical) line (Rogus & Dimitri, 2015). For example, Danish et al. (2020) have applied the method of dynamic least squares to reveal the interrelations between the ecological footprint from the economic activity and urbanization level. Destek et al. (2016) have applied a vector model of error checking (variety of LS) to analyze the interrelation between urbanization, CO2 emission, energy consumption, actual GDP, and trade openness. Fernando et al. (2012) has applied in his work the factor analysis to create a composite index of urbanization used for direct estimation of urbanization, and in the research by McDade and Adair (2001) the factor analysis was used to determine the urbanization constituents. In the research by Maliˇcka (2020), the cluster analysis is applied for the classification of the EU regions by the urbanization degree and consumption of definite goods. Liu et al. (2018) have applied cluster analysis together with methods of autocorrelation analysis and descriptive statistics to analyze the urbanization in so-called “New Silk Way” countries. Similar to the cluster analysis is a comparative geographic method that was first proposed by Humboldt and Ritter (1959). The comparative geographic method is closely connected with typologies elaboration, i.e., it creates two or more groups within which the objects are similar to the maximum possible extent, but the differences among the groups should also be maximum possible (Animitsa et al., 2009). Jiaming et al. (2018) have applied this method for the analysis of spatial urbanization features in Chinese and Indian cities. The research on urbanization by Dewan and Yamaguchi (2009) in Bangladesh is based on satellite data to determine the density of construction, population, and geographical distribution of core cities. The American economist Hoyt has proposed a sector model for urbanization analysis (Trutnev, 2008). This model is based on the differences between the city structure sectors for the analysis of urbanization character. Harris and Ullman (1945) have elaborated a multicore model for cities analysis. The model feature is a division of a city in zones—“cores” in which the different forms of business activity are developed. Zhang et al. (2018) have applied a gravitational model for the investigation of city centers movement effect on the occurring social and economic, and ecological processes. While investigating the territorial social systems interrelation processes with nature resource base, the method of resource cycles is widely used. The founder

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of the resource cycle concept is Comar (1975), who has grounded the availability of six generalized nature resource cycles with a subcycles system. The resource cycles do not take into account the energy type used in a production process—the circulation of energy and energy resources is one of six generalized cycles (Sherin, 2019). The diversity of the existing methodical approaches to estimate the urbanization is determined by types of origin information and ways of integration and presentation of obtained data. A universal research instrument of aggregation of a huge amount of objective (statistical) and subjective data in individual fields are indices (Akhunov & Yangirov, 2021). However, this method is not widely used for the urbanization degree estimation. Our analysis will be focused on namely index method of urbanization estimation. The advantage of this method is that it allows for the following: (1) integrate a huge amount of data different on its quantitative measurements to a unified standardized indicator allowing to get a whole picture of regions urbanization; (2) analyze differentially individual constituents of index; (3) reveal the contribution of each indicator into the whole urbanization pattern and reveal the most problematic areas requiring the targeted steps of social policy; (4) carry out the interregional comparison of the obtained integral values. The conducted review allows for elaborating the following recommendations on the selection of this or that estimation method depending on the research aims. 1. If it is necessary to estimate the urbanization for regions ranking, then the best for this purpose is indices methods. For indices forming, the standard statistical indicators can be used directly or apply more advanced methods like factor analysis to reveal the most important factors and further creation of index. For primary estimation, such statistical indicators as urban population share can be used. 2. If it is necessary to estimate the urbanization character, then the best methods are allowing to reveal qualitative parameters. The cluster analysis can be used for region classification by indicators related to the urbanization, and comparative geographical method and analysis of satellite images add the spatial components to the classification. The factor analysis can also be used to reveal internal characteristics of urbanization. 3. For the analysis of more complicated interrelations and temporal dynamics, the contemporary researchers apply the method of least squares and its improved and more specialized variants. It allows, under the proper simulation, determine the cause-and-effect relations between the interested variables and urbanization.

3 Methods Description For taking into account the number and size of cities to estimate the urbanization degree in Kazakhstan regions, it was decided to apply the urbanization index proposed by a Russian scientist, Yefimova (2014). Iur b =

 n i wi U  ni P

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where Iurb U P ni wi

index of region urbanization; number of urban population; total number of population; number of cities with corresponding number of citizens on the region territory; city weight depending on the citizens number.

To calculate the urbanization index in a region for a definite year, an indicator of relative density of urban population in the total number of region’s population that gives us the calculation of U/P ratio can be used. The values of urbanization index can range from 1 to 10 inclusively. And, it is assumed that the closer the value of the region urbanization index to 10, the higher the urbanization degree. For the cities of republican status—Nur-Sultan, Almaty, and Shymkent—the urbanization index is 10 as its population is completely considered urban. The proposed urbanization index allows to estimate, first, the dynamics of Kazakhstan cities number; second, dynamics of urban population number and the whole number of population in the region. The standard statistical indicators were used directly for the indices forming.

4 Data Description One of the simplest and understandable indicators of urbanization is the share of urban population in the whole population number. For Kazakhstan, the scatter of this value is quite big (Fig. 1). Visually, only two clear groups are seen. The first includes three cities of republican status (Nur-Sultan, Almaty, Shymkent) having 100% share of urban population. The second group includes Turkestan and Almaty regions with about 20% share. Other regions form a gradient from 80% to (almost) 40% without any clear groups. The indicator of urban population share provides good first image on the urbanization, but contains a little information. For example, it does not provide any information on what cities are in the region: a lot of small towns or some big cities? As big cities differ from small towns due to the scaling of social and economic processes (Kireyeva et al., 2022), the consideration of this factor will allow for better estimation of the region’s urbanization. It also informs nothing about the life quality in these cities or towns and its economic efficiency. As of today, in Kazakhstan, there are 87 cities including three cities of republican status, 40 cities of regional significance, and 44 cities of rayon subordinance (Altaev & Kozhakeyeva, 2020). The indicator allows to take into account the number of cities and its size, i.e., adds useful information to the estimation. Within this work, the big cities have larger weight as these are more effective economically (Alonso, 1971). The distribution of weights is the same as the one presented in the work by Yefimova (2014) (Table 1).

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Fig. 1 The share of urban population in Kazakhstan regions in 2021 (%). Source compiled by data of the Bureau of the National Statistics of RK Table 1 Relative weight of a city depending on the amount of its citizens and the number of cities in RK

Population number, persons

Weight

Number of cities in RK

More than 1000 000

10

3

From 500 000 to 999 999

9

2

From 250 000 to 499 999

8

6

From 100 000 to 249 999

7

10

From 50 000 to 99 999 6

7

From 20 000 to 49 999 5

34

From 10 to 19 999

4

11

From 5 000 to 9 999

3

11

From 3000 to 4 999

2

1

Less than 3 000

1

2

Total cities Source Yefimova (2014)

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5 Results The index value was calculated for fourteen regions and three cities of republican status in Kazakhstan for 2018–2022. The regions were sorted out according to the index values, for 2021 (then 2020, 2019, and 2018) in descending order. After that, each box was colored according to the indices values classification from the work by Yefimova (2014). In addition, if to compare the opportunities for the regions grouping by the difference between values, the index allows for better grouping. If the share of urban population allows for marking only three groups in RK: two, including maximum and minimum, and third that includes other regions, then index allows for marking five groups: having index value 10; index 3.5–4.7; index 3.059– 3.160; index 1.863–2.292; index 1.109–1.117. Table 2 was calculated according to the criteria given in Table 1 and based on population data for cities in Kazakhstan (National Bureau of Statistics of the Republic of Kazakhstan). In Nur-Sultan and Almaty, the index is maximum for the whole period of the analysis. In Pavlodar, Mangystau, and Almaty regions, the indices values decreased. Table 2 Distribution of regions and cities of republican status by the urbanization index

Interpretation of the color indices codes: blue—very high; green—high; yellow—average; orange— low; red—very low

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Other regions show the growth of urbanization index. The most significant growth in terms of the index shows Shymkent city (+1 point), Atyrayu, Kostanay, and Aktyubinsk regions. The change values for others are within two decimal places of index share, i.e., are less significant. In 2021, 5 regions show very high index values, 2—high values, 3—average, and 7—very low values. The moving of Atyrayu and Kostanay regions to the regions of high urbanization is clearly seen (in 2019 for Atyrayu region, and in 2021 for Kostanay region). This happened due to the cities’ movement from the category having weight 7 to the category of weight 8 (one city in each region), and due to small (up to 0.04% units) increase of urban population share. Aktyubinsk and East Kazakhstan regions moved from the category of low urbanization level regions to the category of average urbanization regions. In Aktyubinsk region, the index growth was due to the moving of one city from the category of weight 8 to the category of weight 9. In the East Kazakhstan region, it happened due to the increase in urban population share.

6 Discussion As Kazakhstan is a developing country, the strengthening of regions urbanization occurs due to people moving from rural areas to cities, and due to natural increase (Nurlanova et al., 2022). The disproportions in urbanization degree of different regions are caused by different factors, among which not unimportant are geographical conditions. Among others, the huge territory of the country is represented by semi-deserts and desert lands. Such regions require more resources for development. The complicated management of these territories in the past led to a small number of cities in such regions. Particularly, this is related to the urbanization processes in Mangystau and Kyzylorda regions (Bekenova et al., 2021). The regions having huge reserves of natural subsoil resources, for example, Atyrau, Karaganda, Pavlodar, and Kostanay regions have developed the industry that required large number of workers for which the cities were constructed; this can explain the relatively high urbanization degree at that regions. Until 2016, Almaty was the only city in Kazakhstan with a population more than one million people. It is one of the most attractive cities for migration. When the capital was moved to Astana, its attractiveness for migration continuously increased. By data for 2021, net in-migration for Astana is higher than for Almaty (The Bureau of the National Statistics, 2022). In 2019, Shymkent also became one of the cities with a million people population. These three cities, as of 2021, show the maximum possible value of urbanization index—10. Each city has its own features. In view of contribution to the country’s GDP, the difference is quite significant. In particular, Shymkent falls back two other million population cities as well as some less urbanized regions. This can be a sign of social and economic development problems, and weak connection between the population growth and economic potential of the city—“false urbanization.” In particular, the share of self-employed persons in Shymkent is very

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high and constitutes 30% of employed population. This level of self-employment in Kazakhstan is more specific for rural areas, not big cities. It means that such level of self-employment can hide long-term involuntary unemployment, and contribution into the country’s GDP can be connected with informal or unproductive employment. The current urbanization processes in RK are of spontaneous character and create a pressure on urban infrastructure and strengthen the social problems (Muratova & Baygozhayeva, 2020). Such issues as provision with affordable housing, places in pre-school and school institutions, access to medical and housing, and utility services are the most vulnerable fields. Another factor complicating the urbanization growth in Kazakhstan are economic disproportions. High living costs in urban area, comparing to rural one in Kazakhstan, limits the immigration opportunities from rural area to urban (Seitz, 2021). In particular, the cost of food and housing is much higher in cities than in rural area. Additional complications are caused by undeveloped market of rental housing. High living cost impedes the access to highly dynamic labor market for rural area population having low income.

7 Conclusion In this work, we have applied the urbanization index suggested by Yefimova (2014) to estimate the urbanization degree in Kazakhstan regions. The inclusion of cities number and its size into the index allowed us receiving more information on urbanization degree in Kazakhstan and better understanding the difference among the regions. The results were presented in the form of graphics for convenience. This work has the same weakness as the methodological original: the application of more amount of data enlarged the information accessible for the interpretation, but it still does not consider numerous urbanization factors providing an opportunity for improvement. The inclusion of city numbers and their sizes only do not reflect the quality of infrastructure and urban institutes. Thus, the adding of supplementary data to the analysis, most probably, will provide the opportunities for a more precise estimation of urbanization, and allow revealing strengths and weaknesses. Such adding can be done, at least, in two ways. The first way suggests the complication of the index structure. It means, the adding of new indicators to the index so it would become more informative and could divide the regions better. The definitive advantage of this approach is visual expression: the result will be always represented by one number in a definite range allowing for easy rating or ranking the regions by the urbanization degree. The disadvantage of this approach is complicated process of indicator selection, and complicated index normalization at large number of indicators. The second way is application of cluster analysis to understand the urbanization peculiarities of different Kazakhstan regions. Its definitive advantage is an opportunity of deeper study of regional urbanization features and understanding of more interrelations while using enough number of indicators. The disadvantage of this

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approach is its investigational essence: the results will depend on the indicators selection reasons and possibilities of researchers to interpret the received results. Acknowledgements The article was prepared as part of the implementation of the GF project “Organizational and economic mechanism of managed urbanization in the post-pandemic period” (IRN: AP09260795).

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Differential Approach to Shaping Models of Priority Socio-Economic Development Territories Gulia Galiullina

Abstract Pursuant to the existing law of Russia, Priority Socio-Economic Development Territories in mono-profile municipal entities (PSEDT) shall be created per one model in which systemic factors shall be the conditions for receipt of the status of a resident (number of workplaces, amount of investments, and line of business). When building up the model of PSEDT, special features of each territory are not investigated thoroughly enough or taken into consideration. It is alleged that by creating a “paradise” for investor via tax benefits and state preferences, those automatically trigger a trend for priority development. The article presents differentiated models of PSEDT, built on the basis of an institutional-synergetic approach, in which phase, structural transformations in the PSEDT system and the degree of uncertainty of the external environment are taken into account, which made it possible to represent the evolution of special territories from the level of governance, which deals with issues of survival and ending up with levels of innovative development. In addition to that, a lower level of PSEDT development is the basis for constructing the model of a higher level. The elaborated technology of modeling is designed to take into account differences in the existing potential for priority development of monocities and is aimed at assessing, on a par with quantitative indicators, also the qualitative parameters which impact attainment of government-identified objectives set in the PSEDT sphere. In this case, management of a PSEDT will be aimed at creating, accumulating resources necessary to transition to the next level of the territory’s technological development.

G. Galiullina (B) Center for Structural Policy, Institute of Economics of the Ural Branch of the Russian Academy of Sciences, 29 Moskovskaya St., Ekaterinburg 620014, Russian Federation e-mail: [email protected] Department of Economics of Enterprises and Organizations, Kazan Federal University, 18 Kremlevskaya Str., Kazan 420008, Russian Federation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_6

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1 Introduction A Priority Socio-Economic Development Territory is one of the 15 types of the country’s territories, whose resident organizations are granted government preferences (Table 1). As a rule, there are tax benefits, a lower rate for deductions from the payroll fund made to the social funds, administrative preferences, and low customs duties. The presence of a large number of territories with special regimes for doing business, on the one hand, increases competition for potential residents, on the other hand, the negative experience of previously open preferential territories creates distrust in society toward such a mechanism of public administration. The tool of preferential territories is actively being utilized by many countries: in China (Fei, 2017), in USA (Riveras et al., 2018), in Russia (Galiullina et al., 2019a, 2019b; Sosnovskikh, 2017), in Poland (Jensen, 2018; Nazarczuk & Umiñski, 2018), in Ethiopia (Giannecchini & Taylor, 2018), in the United Arab Emirates (Al-Saleh, 2018), in Mauritius (Allam & Jones, 2019). Studies of their performance efficiency are pursued by both national (Anatolevna et al., 2015; Ergunova et al., 2017; Galiullina et al., 2019a, 2019b; Ladrennikova, 2017; Shvetsov, 2017) and international scholars (Alder et al., 2016; Davies & Mazhikeyev, 2019; Liu et al., 2018; Quaicoe et al., 2017). Priority Socio-Economic Development Territories (PSEDTs) are established in monoprofile municipal entities (company cities), in closed administrative and territorial entities (CATEs), in the Far East, and in the Arctic Region (Table 2). At the same time, according to the preferences presented, the PSEDT formed in the closed administrative territories, in the Far East and the Arctic are close to the regime of special economic zones, which in Russia were created in the likeness of similar territories in China. The preferential regime of PSEDT in single-industry towns for certain parameters is significantly limited: • created within the boundaries of one city (in the Far East, possibly in the area of several municipal districts of one region); • the conditions of the free customs zone do not apply; • the right to apply the application procedure for VAT refunds has not been granted; • does not create its own management company; • the term of the regime is limited to 10 years (with the possibility of prolongation for another 10 years), in contrast to the long-term action of the special regime for doing business in the Far Eastern PSEDT (70 years); • there is no compensating subsidy for reimbursing the interest rate on loans attracted by investors for the construction of infrastructure facilities (up to 100% of the refinancing rate); • direct budgetary investments in the infrastructure of a special territory are not provided; • there is no one window mode for an investor;

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Table 1 Register of territories with preferential regimes for the pursuit of entrepreneurial activities as of 01.07.2021 Type of special territory

Administering Ministry

Year that operations began

Free economic zone

Ministry of economic development of the Russian Federation

1990

3

Special economic zones

2005

38

Skolkovo innovation center

2010

1

Innovative territorial clusters

Number as at 25.06.2021

27

PSEDT in company cities

2016

84

PSEDT in CATE (Closed Administrative Territorial Entity)

2017

8

Innovative science and technology centers

2019

7

Science Cities/ Technopolises

Ministry of science and higher education of the Russian Federation

1999

73

Industrial parks

Ministry of industry and trade of the Russian Federation

2005

165

2015

80

2016

12

Ministry of industry and trade of the 2014 Russian Federation, Ministry of digital development, communication and mass communications of the Russian Federation

169

Industry parks Innovative leader-clusters Industrial clusters Techno parks

42

PSEDTs in the Ministry of the Russian Federation for far east and in the development of the far east and the arctic arctic region region

2015

Free Port of Vladivostok Total territories with special regime of conducting entrepreneurial activities

23

1 733

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Table 2 Number of residents as of 1.07.2021 by type of created PSDETs PSEDT type

Number of PSEDTs, units

Date of last entry in the effective register

Total residents, units

Out of whom the following number of residents have lost their status of a resident Units

Percentage, %

Far Eastern Federal District

22

22.06.2021

597

86

14,4

Company cities

84

26.05.2021

1034

111

10,7

CATE

8

17.06.2021

48

0

0,0

Arctic region

1

15.06.2021

9

0

0,0

1688

197

11,7

Total

115

• there are no preferential rental rates for residents for property and land. Taking into account the differences in the creation of PSEDT in the Far East and in monotowns, the object of this study is the modeling of the latter. As of the end of May 2021, PSEDT have registered 1034 residents, however, whereas in Togliatti, 82 companies have opened production operations or 7.9% of the total number of residents, in Kameshkovo (this city was awarded the status of a PSEDT on 06.09.2018.), not a single resident ever materialized (Fig. 1). Ten PSEDTs, with the greatest number of residents, have accumulated in their territory 403 companies or 39% of the total number of residents. On an average, these

Fig. 1 Distribution of residents among different PSEDTs established during 2016–2020

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PSEDTs have 40 residents each. Ten PSEDTs with the smallest number of residents have attracted 18 companies or 1.7% of the total number of residents. On an average, these PSEDTs have two residents each. That goes to show that, on an average, the 10 PSEDTs with the greatest number of residents are 20 times more efficient than the monosettlements with the smallest number of residents. The remaining 60 PSEDTs have 613 residents which on the average constitutes 10 residents per each special territory. At the present time, the number of residents attracted is one of the efficiency indicators of a PSEDT, or to restate more precisely, an indicator of how active those officials are whose responsibility is to implement the government project for establishment and the functioning of Priority Socio-Economic Development Territories. It is noteworthy that PSEDTs have been created both in Uglovka with a population of 2.2 thousand people, and in Togliatti, with a population of nearly 700 thousand people. These company cities with the status of a PSEDT do not differ in the number of inhabitants only but in other indicators as well, such as industrial specialism, budgetary sufficiency, population’s level of income, investor appeal, etc. Pursuant to the existing law, in monoprofile municipal entities, PSEDTs shall be established per the same model in which the systemic factors are the number of investors attracted whose business case (or business plan) satisfies the terms and conditions to qualify for the status of a resident (number of jobs created, amount of investment made, and line of business). Difference in the potential for socioeconomic development of a territory sets the task of developing differential models of a PSEDT, which would take into consideration the system-shaping factors of each city, some of which are the level of technological development typical of the key industrial organizations and unequal pace of development of the country’s territories.

2 Methods Differential models of a PSEDT were developed using the institutional and synergetic approach, which takes into consideration unequal pace and non-equilibrium of a territory’s evolution and is oriented to obtain positive synergy effects in territorial development. The proposed approach to the running of a PSEDT allows coherent combination of process based (synergetism) and project-based (institutionalism) approaches and examine the development of a territory in time (synergetism) and in space (institutionalism). Synergetic approach in economists’ research is primarily targeted at generating synergy effects (Bellanger et al., 2021; De La Cruz et al., 2021; Lavers et al., 2021; Lupova-Henry et al., 2021; Rahman, 2021). A synergy effect manifests itself when the result of the system’s functioning (output) is not in an adequate ratio to the costs incurred in obtaining such result (input into the system), including the case where a weak signal at the input can be

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answered with an inadequate signal at the output. Synergetic efficiency includes not only quantitative but also qualitative changes in the system, including the changes predicated by redistribution of interactions in the system, combinatorics of horizontal and vertical links in the system, phasic and structural transitions. Synergetism is a school of scientific thought that crystallized itself during the second half of the twentieth century based on pooling potentials, efforts, resources, intellectual power of humankind; it takes into consideration non-equilibrium, nonlinearity, irreversibility, phasic and structural transformations in systems of various nature. However, synergetism does not give up on classic developments, and integrates some of them as separate system blocks which are used at different stages of managing development of the territory. Synergetism is looked upon by us as an alternative to the traditional system of production and distribution (capitalistic), which is an economic world outlook, based on egoism, consumerism, and competition (primarily, it is competition for profit rather than competition for quality). The future of humanity, which is tackling the fundamental super-problem of survival during the period of continual political, economic, world outlook driven and biological crises cannot be rooted in fighting, wars, destruction of one group of individuals to humor or satisfy the whims of another group. Inter-species and intra-species fighting is atavism bequeathed to present-day humanity by our distant ancestors. This is a phenomenon of the doomed epoch. Respectively, competition, heir of the departing world, cannot underlie proactive socioeconomic development of the territories. Competition can only be a driver of local acceleration. From the perspective of synergetism, development is growth of responsibility, acquisition of new intellectual world space, based on perceiving humanity as one single community endowed with a special capacity for cooperation, co-evolution, solidarity in resolving problems, and implementing common objectives. Russian researchers (Tatarkin & Romanova, 2014) believe that “the most important idea following from synergetics is that for the sustainable development of socioeconomic processes, a certain amount of chaos is needed, that is, spontaneity of development and self-government, as well as a certain amount of external management. Moreover, these two components—self-organization from below and organization from above—must be balanced.” Synergetic approach is an approach oriented to qualitative transformations rather than to quantitative increments within the range of the same quality, synergetic efficiency rather than economic one; coordinated synchronizing action of all the subsystems for production and socioeconomic activities (Abu et al., 2021; Zheng et al., 2020). Synergetic governance is directed at coordinated (coherent) unification of everyone in actions aimed at dealing with topical problems; structuring horizontal bonds of participants in a common project; at elaborating the mission and a system of goals, tasks, based on priorities, coordinated in space and time, including phasic and structural transformations. Synergetics does not negate cybernetics; instead, synergetics includes it as a specific case, when self-regulation forces are capable of suppressing disturbance

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from outside. As stability of functioning is just as a legitimate part of the life of a system as development, both these activities are part of common systemic approach. Constructing a system for running development of a territory within the framework of cybernetic (conventional) approach cannot be productive for the following reasons (Table 3). The synergetic approach identifies several stages of territorial development and at each of them, it enables its management mechanisms. Initially, socioeconomic environment of a territory is developing at a slow speed and is of quasi-laminar shape: traditional and separate germinating elements of a new development vector move in layers, along parallel trajectories without mixing together and without pulsations. Development proceeds at a uniform rate, without erratic leaps of pressure, direction, and speed. During this period of territorial development, it is the cybernetic approach to management that is successful, i.e., holding the system within the range of defined target function. However, after passing a certain critical value of the elements of the new development trajectory (it varies from condition to condition), the mode of territorial evolution changes: jet-like (quasi-laminar) flow becomes chaotic, eddy-like, i.e., turbulent. The system is pronounced to be unmanageable from the perspective of cybernetics and, at this point in time, mechanisms of synergetics are triggered. At some point in time, the system transitions to an extremely unsteady state, reaches bifurcation point and an accidental event or, from the perspective of synergetic management, a small management decision (pricking in trigger points) takes the system to the next turn of development, there happens a phasic transfer. At this moment, the quality of management impact is utterly important, as either a positive or a negative synergetic effect may be produced. Any development process is accompanied by a background of incidents most of which exercise an influence, which is weak and non-commensurate to the main course of events. Nature pushes its way through a multitude of vain attempts at empty tests. The question that remains open is when and in what incident (fluctuation, chaos at the micro-level), a breakthrough will be made and the type of the general course of events which is to become a structure, a natural or social specimen will be defined? From the standpoint of the institutional-synergetic approach, Russian scientists study the issues of sustainable development of the regional socio-ecologicaleconomic system (Shedko, 2013), innovation processes (Shmanyov & Egorova, 2012), innovation and investment activities (Egorova et al., 2012; Lisichkina & Goloktionova, 2014), problems of inflation regulation (Rumyantseva, 2013), labor market (Legchilina, 2013), crisis phenomena in the economy (2019b; Galiullina et al., 2019a). The institutional-synergetic paradigm considers development institutions as the main driving force of economic progress (Shmanev, 2017). From the perspective of the institutional and synergetic approach, one of the fundamental principles for territorial governance is as follows: when a PSEDT (viewed as an open non-linear system) is in a state of instability (the system’s sensitivity to small fluctuations, enhanced by enabling the positive feedback mechanism), there

Strengths of the institutional and synergetic approach

– The cybernetic theory cannot offer a satisfactory explanation of the concept of “qualitative development,” it cannot forecast future development, foresee crises, consider inequality of socioeconomic indicators, plan their phasic or structural changes

– Positive feedback in the system is responsible for selecting direction of progress at bifurcation point and taking the system development onto a new trend – Prevalence of developing positive feedback compared to stabilizing negative feedback (allows the process of system change to be held within the framework of the defined trend, as a rule, for a short space of time) (continued)

– Key management tools constructed within the framework of cybernetics copy stabilizers and adaptors, usable in – Triggering the self-development technical systems which have no potential for effective management under the conditions of qualitative transitions mechanism of a manageable system – Identifying trigger points (fields of in socio-technical systems activities, which are the most sensitive to changes) and impacting those rather than the whole system at the same time

Limitations of the cybernetic (conventional) approach

Table 3 Comparison of cybernetic and institutional and synergetic approaches to territorial management

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Strengths of the institutional and synergetic approach

– Cybernetic approach in management, while allowing of many types of varia-tions in a cycle, considers operations – Controlling quasi-laminar, turbulent within its limits only. As soon as they overstep the range of the cycle (the systems begin developing), it is declared processes and processes with to be unmanageable aggravation in socio-economic development – Controlling processes of self-oscillations, autocatalysis, autowaves, cyclicity, cluster hybridization in socioeconomic development of a territory

– Cybernetic principles for developing a management system for socioeconomic development of a territory orient it – Priority of qualitative results over to increasing some indicators (production volume, amount of profit, etc.) or reducing other indicators (sum of net quantitative ones losses, Gini coefficient, etc.). In the meantime, parameters in different temporal and spatial scales can have not – Orientation to synergetic efficiency only different quantitative assessments but they can change signs to reverse ones, i.e., change their quality. – Synchronization of the territory’s Specifically speaking, decline in the level of unemployment (indicator for social tension) is an important one for development objectives based on the authorities, but for entrepreneurs it is a hindrance for development, telling them that the territory lacks labor self-organization rather than on resources hierarchic directives – Establishing institutional forms and relations for implementing the desired event in self-development mode – Implementation of disruptive innovations when transitioning to a new technological space – Priority of local autonomous organizations over hierarchic organizational structures

Limitations of the cybernetic (conventional) approach

Table 3 (continued)

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breaks through such kind of incident as has suitable conditions created (deliberately or inadvertently), primarily, development institutes of the next level. In this respect, one of the conditions for governance of a PSEDT is the institutional support of the process. Institutional environment of a territory is regarded as an accumulation of socioeconomic institutes, relations, and tools. The institutional approach is widely used by scholars to investigate topical issues of the present day (Akberdina et al., 2018; Dyatlov et al., 2018; Li et al., 2019; Liu et al., 2020a, 2020b; Mahmoudi et al., 2018). To perform the target function of a PSEDT, adequate changes in traditional institutes of the territorial economy and the shaping of new institutional conditions are required. The institutional conditions are the norms which set “the rules of the game” for all the business operating subjects of the economy and affect all the spheres of the region’s socio-economic life. For example, the interests of PSEDT residents, and, as a rule, these are new enterprises, are supported by the government, to a certain extent, at the expense of the taxes paid by the existing enterprises—long timers of the territory. And it is important that such institutional conditions are put in place as would strike a fine balance between the interests of the stakeholders and those business operating subjects which do not fall under the operation of special terms and conditions of entrepreneurship. At the same time, development institutes are constantly perfected by not only coherently adapting themselves to the constantly changing internal and external environments, but also by molding the changes which enable, from bifurcation point, i.e., the extremely unsteady state of the system, to take its development onto the trajectory of required quality, that is the mechanism of the territory’s self-development is in action. Governance on the basis of the institutional and synergetic approach is the art of managing complex systems, which harmoniously combines chaos (freedom) and order. The state of instability does always actually consist in something that points to a link between micro- and macro scales (Onuchin, 2014). It is precisely in these cases that the conditions of chaos, small disturbances can determine the macro-picture of existence (development of the territory), type of the macro-structure of the future (the vision of the territory’s desired future). The proposed institutional and synergetic approach allows attention to be focused on development institutes and institutional transformations, allows phasic and structural transformations in the PSEDT system to be taken into consideration, allows for design of an organizational and economic mechanism which takes into account the uncertainty degree of the external environment, the backbone and core factors, which, as combined in one package, impart the necessary acceleration to socioeconomic development of the territory, and going forward, to the region and the country at large.

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3 Results From the perspective of the institutional and synergetic approach, the system elements of the PSEDT conceptual models vary from recurrent to unexpected ones as the external environment dynamics increase (Table 4). The system of evolution of PSEDT is built on the basis of the concept of organizational development by I. Ansoff (Ansoff, 2007). Researcher put the evolutionary changes taking place in the enterprise management system into dependence on changes in the phase of uncertainty in the external environment. Based on the level of variability of conditions and the nature of innovative variability (defined by him as the main characteristics of changes in the external environment), he identified five types of uncertainty in the external environment and the corresponding types of organizations. The mainstay of the PSEDT development is the technological development level of the key industrial organizations in a city. Industrial development of a city is assessed based on S. Glaziev’s theory of technological paradigms (TP) (Glaz’yev, 2009). Conceptual models present evolution of territories beginning from the level of governance at which such tasks as survival are dealt with onwards to the levels of innovative development. Besides, a lower level of PSEDT development serves as the base for constructing a model of higher level (Galiullina, 2020). Governance efficiency consists in identifying discontinuities and making management decisions to swiftly eliminate them. To identify discontinuities between planning and actual execution of the plans in real life, initially desired vision of the object is created through a number of key indicators (strategic planning, foresight, from the future into the present). It is proposed that the vision of desired future of each PSEDT be characterized via a system of indicators which includes indicators of the population’s quality of life, level of science intensity of the residents’ production operations, labor productivity, wages and salaries, energy intensity of the production operations, environmental features of the projects being implemented. For each PSEDT, current values of the indicators are calculated and desired indicators are projected, which are to be achieved in 10 years. The time span of 10 years has been determined based on the law-enshrined timeframe for operation of the special regime for entrepreneurship in PSEDTs, in the monoprofile municipal entities established. Depending on the parameters of the external environment, the level of PSED technological development and the governance goals, models of indicators for five models with provisional names have been constructed: industrial territory, agglomeration, innovation territory, smart territory, cyber territory.

3.1 The Model “Industrial City” The model “Industrial City” is characteristic of the territories where residents deploy the operations, which use technologies of the II-nd paradigm, i.e., extraction of

III-rd TP—heavy machine building industry, electrical engineering industry

II-nd TP—extraction of mineral resources, ferrous metallurgy

Recurrent

Very

Technological development level of PSEDT key organizations

Level of environment changeability

Rate of technology change

PSEDT is an operational management tool

Project team (leader)’s thinking

PSEDT is a tactical tool

PSEDT is tactical thinking

Tactical management

Control and operational solution of crisis problems

Concept

Operational management

Seeking customary changes, asynergism

Moderate

Changing

IV-th TP—automobile industry, non-ferrous metallurgy, oil refining, synthetic polymer materials

Integration

Degree of system Rejecting changes Adapts itself to openness changes

Slow

Expanding

Differentiation

System parameter Pioneering

PSEDT is a strategy tool

Strategic management

Seeking changes, global scale

Borrowing technologies

Jerky

V-th TP—electronics and microelectronics, information technologies, genetic engineering, telecommunications

Association

Table 4 PSEDT evolution from the point of view of the institutional and synergetic approach

PSEDT is strategic thinking

Innovative strategic management

Seeking radical changes, creativity

Emergence of new technologies

Unforeseen

(continued)

VI-th TP—biotechnologies, molecular, cellular and nuclear technologies, nanobionics; new medicine, forms of transport and communications, appliances; live tissue and organism engineering

Creativity

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Growth of the territory’s indicators

Survival of territory

Operative

Efficiency

Not considered

Main goal

Leader’s role

Criteria to assess functioning efficiency of PSEDT

Reaction to change in external environment

Taking changes into consideration

Relevance

Tactician

Differentiation

System parameter Pioneering

Table 4 (continued)

Control over changes

Coordination

Navigator

Territory development

Integration

Reflective reaction

Adequacy

Strategist

High-quality development of the territory

Association

Synergetic impact

Coherence

Innovator

Innovative development of the territory

Creativity

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mineral resources, ferrous metallurgy, light and textile industries, etc. These territories are confronted with an annual decline in population, high level of unemployment, low level of revenue base, as low added value industries prevail in these territories (Table 5). The main purpose of governance is to create jobs, to raise investments for plant, property, and equipment with a view to improving socioeconomic development of the city. Existing status and desired vision of the territory are described by the indicators, which have been systemized into four groups: development level of the city’s economy; availability of urban infrastructure facilities; development level of the city’s financial system; employment level of the city’s population. At this stage, a narrow range of PSEDT functioning indicators are tracked with the control function of the territory governance being implemented. Authorities, unilaterally, select paths of solving the issues. Such paths are developed by a team of selected stakeholders. The general public, in the case of the best scenario, is informed after accomplished fact and is not involved in such work. Governance Table 5 Vision of the future “Industrial City” Activity

Indicators

Development level of the city’s economy

City’s industrial production index Industrial production index of PSEDT residents Specific weight of residents’ industrial production volume in the total volume of the city’s industrial products manufactured Utilization level of average annual production capacity Trend development of major products output Assessed demand for products of the city’s organizations (order portfolio) Assessment of factors which limit production growth in the city

Availability of infrastructure facilities in the city

Level of housing availability to city inhabitants

Development level of the city’s financial system

Local budget level of revenue per 1 city inhabitant

City’s level of availability of social facilities infrastructure Kindergartens, schools, public health, cultural, sporting facilities PSEDT residents’ level of tax revenues in the total amount of taxes collected in the city Net financial result of city organizations Net financial result of PSEDT residents

Employment level of the city’s population

Unemployment level Specific weight of the population employed by production operations of PSEDT residents in the total number of the city’s able-bodied population Level of labor resources availability to production operations of PSEDT residents

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consists in operational resolution of current issues that is why long-term programs are not wanted.

3.2 The Model “Industrial City” Production operations of the III technological paradigms as well as the PSEDT model “Agglomeration” correspond to the expanding external environment (Table 6). The key purpose of a PSEDT is to provide production facilities of operating organizations and PSEDT residents with labor resources of necessary qualifications, to lift the existing restrictions in the area of production, transport, energy infrastructure. Relevant indicators are added to the system of indicators. Enterprises in heavy machine building industry are developing, as well as in electrical engineering industry, timber processing industry. External environment is changing slowly and is predictable. Change in the environment is perceived as expanding change. The system adapts itself to changes. Frequency of new products implementation into production, rate of technology change are low. Development takes place through simulation of technologies and a new type of products. The level of investments in R&D is low. Authorities, in order to deal with development tasks, engage some experts. In challenging cases, working commissions are set up with representatives of the business community with a view to working out ways of resolving topical issues. Governance consists in tackling tactical tasks, medium-term programs on certain vectors of territorial development are wanted. Critical to the functioning of a PSEDT becomes the presentation and analysis of the information in the terms which are of interest in order to grapple with the tactical issues related to the city development.

3.3 The Model “Innovation City” The model “Innovation City” is a PSEDT model which mainly operates/opens up production facilities of the fourth technological paradigm (Table 7). This is a territory in which there is a critical level of intellectual resources capable of creating innovations in any spheres. While at the previous development stages, the main competitive advantages of the territory were its transport and geographical situation and availability as well as accessibility of necessary natural resources, here, companies’ and territories’ competitiveness hinges on the quality of human potential and better quality of life. Previously, the key avenue of PSEDT governance was creation of a favorable investment climate for business, at this particular stage, the key prime mover is creation of conditions for an inflow or nurturing of innovators, i.e., creative, dynamic, ambitious individuals with an entrepreneurial turn of mind and business acumen. Such people should make

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Table 6 Vision of the future “Agglomeration” Activity

Indicators

Quality of the city’s economy development

Entrepreneurs’ confidence index of the city Profitability of PSEDT residents’ production operations Proportion of residents with production operations of 3-rd and higher technological paradigm in the total number of PSEDT residents Production volume proportion of residents with production operations of the 3-rd and high technological paradigm in the total volume of products manufactured by PSEDT residents Quantity of new technologies acquired by organizations (technical achievements) Export products volume of the city’s organizations Specific weight of products volume exported by PSEDT residents in the total volume of products exported by the city’s organizations

Demographical situation

Change in the population number Population density Level of demographic burden

Quality of labor market

Availability level of the information about the labor market in the city Index of change in labor intensity of industrial facilities in the city Level of structural unemployment

Quality of social sphere services in the city

Quality of housing developments, comfort level of housing

Living standards of the city’s population

Level of pay in the city’s organizations

Level of ecosystem

Level of indicators for monitoring the city’s ecosystem

City’s production infrastructure

Level of development institutes: Industrial Parks, Industry Techno Parks, Centers for Collective Use of Equipment, Test and Certification Centers, etc

Quality of services provided by social sphere facilities of the city Availability of educational institutions to suit the line of business done by key organizations in the city Level of pay in PSEDT residents’ production operations

Level of environmental parameters of PSEDT residents’ production operations

Development level of the city’s transport network Availability of engineering and communal infrastructure facilities and capacity in the city

up the majority of the local community, their proportion of the population should be constantly rising. The population becomes more mobile (available and easily circulating is the information about living conditions, job and career prospects, unlocking capabilities and talents, self-realization…), whereas migration flows become more ramified. Decisions to move to another place are more and more predicated by a whole set of social, economic, and personality-related factors. In this context, an important

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Table 7 Vision of the future “Innovation City” Activity

Indicators

Innovative economy

Level of labor productivity in the organizations of the city Level of labor productivity in PSERD residents’ production operations Proportion of residents with production operations of the 4-th and higher technological paradigm in the total number of PSEDT residents Proportion of production volume for residents with production operations of the 4-th and higher technological paradigm in the total volume of products manufactured by PSEDT residents Innovative activity of the city’s organizations Innovative activity of PSEDT residents Specific weight of high technology goods in the total volume of exports/ imports

Innovative production infrastructure

Development level of innovative production infrastructure: Techno Parks, Prototyping Centers, Engineering Centers, Centers for Competencies in Research and Developments in Technology Leadership Discipline, etc. Number of research divisions incorporating a large number of employees researchers in the structure of the city’s organizations Number of research divisions incorporating a large number of employees researchers in the structure of PSEDT residents

Level of human resources potential

Specific weight of high-productivity workplaces in the employment structure of the city’s population Specific weight of highly paid workplaces in the employment structure of the city’s population Mobility level of highly qualified human resources including engagement of leading international professionals with managers among them Specific weight of population employed by PSEDT residents with production operations of the 4th and higher technological paradigm in the total number of able-bodied population of the city Specific weight of researchers in the total number of the city’s employed population

Quality of city management

Ratio between expenditures incurred to acquire results of intellectual activities and to perform R&D activities and revenues generated by core operations of the city’s organizations Ratio between expenditures incurred to acquire results of intellectual activities and to perform R&D activities and revenues generated by PSEDT residents Development level of strategic planning in the city (continued)

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Table 7 (continued) Activity

Indicators

Quality of the city’s Activity level of inhabitants and city administration in liquidating ecosystem unauthorized garbage dumps Development level of the system for separate collection and disposal of solid domestic wastes in the city Quality of the city’s Availability of developed university complex including state-of-the-art social sphere campus, residences, sporting facilities, cultural centers infrastructure Availability in the city of unique facilities, centers which act as magnets for young people including cultural and social ones Development level of online services to monitor public transport in the city Development level of the system for automatic registration of violations: public order, traffic regulations Quality of the city’s Level of venture financing for PSEDT’s priority activities financial system Level of subsidizing innovative projects conducted by universities and educational organizations Level of financial support for small and medium innovative organizations of the city Level of financial support for innovative projects of PSEDT residents

condition of PSEDT governance is the ability to attract and retain persons who are capable of creating new things and assisting in maintaining connections with those of them who left the city (having outgrown it, however who appreciate the conditions which were instrumental in these persons’ self-realization), to make them promote the image into the outside of the city that brings up innovators. The human values of an innovator are a variety of opportunities for self-realization, availability of the whole world, civil liberties, comfortable environment. That means that the values of a PSEDT should be the same values, i.e., creation of conditions for self-realization, comfortable conditions, civil liberties (as talents in a cage do not multiple, they die). The key purpose of a PSEDT is to create/enable the institutes of the city’s innovational development with an aim of attracting residents who invest in manufacture of innovative products and who create high productivity jobs. The strategy of PSEDT is aimed at establishing a “flow of high technology projects,” reaching the worldclass competitiveness level of the residents’ products, increasing the proportion of disruptive research and developments of world class. The functioning of a PSEDT can proceed along the lines of the following two scenarios: • orienting to utilization of the potential that science and educational organizations have as located/being established in the territory. That presupposes recruitment of Russian and international residents to cause them to deploy high technology operations using available human resources potential and research infrastructure in the territory, as well as vibrant development of “mass production” small and medium

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business innovative entrepreneurship by commercializing the technologies being developed; • orienting to the leading role of medium and large-scale industrial production. In this respect, the functioning of a PSEDT is contemplated as using a more intensive transfer of scientific and engineering research into the activities of already operating industrial companies as well as the setting-up of new small and medium businesses—residents of a PSEDT, integrated into added value chains built up by major companies. In this particular PSEDT model, it is required to lift the restrictions on the line of business of the company, which is the main employer and earnings generator in the city and introduce additional preferences for companies who are residents which use the fourth and higher paradigm technologies (beneficial credit facilities, debt financing, subsidized interest rates). The external system is in a non-equilibrium state and is changing fast. It is exactly starting from this development level of a PSEDT that it is necessary to actively create and enable, in governance, positive feedback channels, which are responsible for selection of the development path at bifurcation point and transition of the system development to a new trend, thus insuring synergetic self-organization. Creation of a system for effective governance of a PSEDT in this model is of top priority. It incorporates, apart from government officials, representatives of scientific and business community, interested active inhabitants of the city. The governance level is strategic, based on long-term plans for implementation of major, including cooperative, city development projects in the area of science, technologies, and innovations. Technological leadership activity and the city’s brand take shape as a territory favorable for implementation of innovative projects with a high quality of life for the population. Undivided attention is paid to developing respective human resources potential, there takes place the fine-tuning of the educational system to suit the needs for development of the residents. In the PSEDT governance system, special emphasis is laid on facilitating expansion of exports of high-technology products made by residents and organizations of the city.

3.4 The Model “Smart City” “Smart City” is a PSEDT model with development level of production operations hitting the V-th technological paradigm (nuclear power, IT-technologies, electronics and micro-electronics, genetic engineering, etc.) (Table 8). The model “Smart City” is a territory which exerts conscious efforts to innovatively use information and communication technologies in support of inclusive, multivaried, and sustainable territorial environment on the principles of sustainable development.

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Table 8 Vision of the future “Smart City” Activity

Indicators

Smart economy

Specific weight of residents with production operations of the 5th and higher technological paradigm in the total number of PSEDT residents Specific weight of residents with production operations of the 5-th and higher technological paradigm in the total volume of products manufactured by PSEDT residents Development level of city organizations’ activities in the area of information and communication technologies

Smart inhabitants

Coefficient of invention activities (number of patent applications for inventions filed as calculated per 10th and of the city population) Specific weight of population employed by PSEDT residents with production operations of the 5th and higher technological paradigm in the total number of the city’s able bodied population Activity level of Internet users

Smart governance

Development level of PSEDT residents’ information and communication systems, those of organizations and authorities Level of information openness of the authorities, of PSEDT management system Level of citizens’ involvement in the running of the city Level of stakeholders’ involvement in working out and implementing strategic decisions relating to the functioning of PSEDT

Smart technologies

Export level of technologies developed by the city’s production operations (number of agreements, amounts of the agreements) Export level of technologies developed by PSEDT residents’ production operations (number of agreements, amounts of the agreements)

Smart eco environment

Specific weight of energy resources produced using renewable energy sources in the total production volume of energy resources in the city territory Development level of systems for monitoring and preventing environmental safety hazards

Smart infrastructure

Development level of charging stations network for electric vehicles Development level of information systems to manage urban construction Development level of services in the city Development level of communication networks for telemetry services, free-of-charge wi-fi access, mobile broad band access Development level of free-of-charge wi-fi access services on public transport

There exist two approaches to creating “smart cities.” • transforming a conventional city into “a smart city” (Amsterdam, Stockholm, Barcelona, Singapore). By connecting, using intellectual technologies, vast areas of urban facilities, efficiency of city systems, and the quality of the population’s life have been increased multiple times.

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• designing new cities (South Korea, UAE, China). In most cases, these are relatively small, compact residential locations, where the infrastructure, from the outset, is built to pre-elaborated, usually rather stringent standards.

3.5 The Model “Cyber City” Cyber City is a vision of the future city where disruptive innovations of the VI-th technological paradigm are implemented. Production facilities of such a level are virtually non-existent in this country and there are no residents prepared and willing to develop production facilities of such a level. External environment is not constant, it is changing fast, and efficiency of a PSEDT is impossible without orienting to synergetic effects based on implementation of basic innovations. Linear thinking, attempts to extrapolate past experience onto the present, and even, what is worse, the future is not just a risk but a threat to the innovative development strategy. Innovative process is not only non-linear, but it is also unequilibrial, emergent, synergetic and, therefore, it does not lend itself to template management. The proposed system of indicators of five models of the PSEDT evolution is the author’s development and is debatable, based on the features and capabilities of the Federal State Statistics Service.

4 Conclusion Having designed a desirable vision of the future in the form of indicators with respect to the key spheres of territorial development, one can assess the quality of the available potential for the city development and go on to generate potential resources for proactive development of a territory. Evolution of a PSEDT at each level proceeds along the lines of the following four vectors: development potential, interaction in development, priorities in development, development management which are recurrent elements of the PSEDT models and observable at each scale level. This way, self-similarity of the PSEDT system is implemented (Table 9). We propose that proactive development of a territory, which has acquired the special status be deemed to be evolution of the PSEDT model from the current level to cyber territory. The driving forces of evolution are receptiveness to changes in uncertainty of the external environment and the level of technologies employed in production operations of the PSEDT residents. Transition from one level to another may take rather a long period of time and may be carried out with discontinuities. Therefore, one of the project team’s tasks is to reduce time discontinuities based on developing a governance model with positive and negative feedback channels.

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Table 9 Differential models of a PSEDT PSEDT sub-system

Models of priority socio-economic development territories Industrial city

Agglomeration Innovation city

Smart city

Cyber city

Technological development level of PSEDT

Second TP

Third TP

Fourth TP

Fifth TP

Sixth TP

Development priorities (goal setting mechanism)

Financial support provided by key organizations of the city

Development of production infrastructure in the city

Creating the infrastructure for scientific research, implementing innovations in the city

Fostering favorable investment climate for high technology production operations in the city

Setting up the infrastructure in the city for diffusion of innovations

Development management (self-regulation mechanism)

Resolving current (urgent) issues

Operational

Tactical

Strategic

Innovative

Stakeholders’ Infrequent interaction (self-organization mechanism)

Low

Moderate

High

Synergetic

Strategic thinking Passive of the territory’s elite (self-control mechanism)

Reactive

Proactive

Investigative Creative

Pursuant to B. L. Kuznetsov’s theory of synergetic market, economic systems develop in non-linear way, with discontinuities, in a discrete way, with phasic transitions of the I-st, II-nd, and III-rd kind. From Landau’s universal phenomenological theory of phasic transitions, there follow several practical conclusions relating to transient processes, including those in economic systems: a. real transient processes in economic systems can be only blurred, while this fuzziness constitutes the essence of the phasic transition phenomenon itself and is caused by emergence of the borderline between the phases; b. all the phasic transitions have several stages, therefore, a change in the state of the system in the event of a phasic transition cannot be described with a continuously differentiable (smooth) function, with the whole process of transition being able to be presented by piecewise continuously differentiable functions (Kuznetsov, 2005). Transformation of the PSEDT model begins with changes of the third type, which constitute an accumulation of new elements in the system and growth of the system

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as a whole. This type of changes does not cause new functions to be formed as transitions of the second kind, neither does it cause new structures to be formed as transitions of the first kind; it is only the quantitative changes that occur at the lowest level. Phasic transitions of the third kind are difficult to identify, however, it is from them that all the changes begin. Changes of the second kind are connected with the birth of a new element in the system. For example, there emerges the infrastructure of the new technological paradigm. These changes result in quantitative growth in the system of the new level elements and this type of changes can be classed as phasic transitions of the second kind which are related to the governance system potential and that of the PSEDT residents. Referred to the first type of changes are the changes related to qualitative structural transitions of a PSEDT from one hierarchic level to another. The transition process represents assignment of new functions to the newly formed institutional structures and takes place after all the new functions corresponding to the new level in the hierarchy have been put in place. The crux of similar changes amounts to the need to insure interconnection of the PSEDT elements, lack of contradiction, and orderliness. Such changes are classified as phasic transitions of the first kind. The discontinuity that occurs between the existing potential of a territory and the residents’ technological development is the driving force for PSEDT development. In this case, PSEDT governance will be aimed at creating, accumulating resources that investors require. On the other hand, impetus to transition can be given by the discontinuity between the pledged targets for development of the system of the superior level (region, country) and the current vector in the PSEDT development. Therefore, the technological development level of the city’s key organizations is the principal object of the PSEDT model and simultaneously the main factor which influences its construction. The second factor in terms of its significance is the strategy of the superior hierarchic level. The locomotive of the transition is the tendency to impart order and stability to the system, to reduce discontinuities between the PSEDT elements (discontinuities of the first kind). Solution of the problem with such discontinuities is a direct function of the PSDET team’s professionalism who are supposed to optimally design the development structure and processes in the system such that they are placed in selforganization and synergetic development mode. Thus, based on the changes of the first and second kind, a PSEDT develops, heightening its functioning efficiency, quantitative and structural growth, while changes of the third kind are conducive to qualitative development of its essence. Typology of the changes allows PSEDT structural genesis mechanisms to be analyzed. Whereas, the phasic transitions of the first kind result in new structures responsible for performance of newly created functions, whereas phasic transitions of the second kind lead to the birth of new elements of the function, the phasic transitions of the third kind play the role of germination centers for new quality of the territory’s development.

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At the beginning, at the level of a territory, embryos of a PSEDT take shape depending on the technological development level of the key enterprises in the territory. The embryo germination center is transformed into clusters, into groupings of newly shaped functions for governance components. Achievement of the fractal cluster’s critical volume (realization, acquisition of the function by the critical number of participants in a PSEDT) defines commencement of structural formation or phasic transition of the first kind. Formation of a new structure in the system, as development goes on, results in formation of new quality. That is followed by formation of a new development trajectory aimed at implementing the next scale level, i.e., of the next PSEDT model. Transition from one scale level to another is a reflection of bilateral process: on the one side, resolution of the assigned tasks and, on the other side, the overcoming of external environment’s uncertainty. From the point of view of the theory of self-organization in complicated systems, activation of processes at each level is necessary for external disturbance pulses which arise in the system and in the external environment to be transformed into ordered structures. Compression, processing of information about these disturbances and the sampling of internal pulses enable the system to adapt itself to external effects. In the course of its development, the PSEDT system is to undergo several phasic transitions of the first kind, which are accompanied by changes in the structure of the territory governance system. These changes occur in jerks due to the system seeking to reduce discontinuity of the first kind. Each scale level (industrial city, agglomeration, innovative city, smart city, cyber city) in the PSEDT system emerges as a result of phasic transitions of the second kind and has its own “elementary building blocks,” which are the ultimate structures of the previous level. The conditions necessary for the implementation in practice of the institutionalsynergetic approach to the management of PSEDA include: • The choice of a leading development link (enterprises, leaders) capable of performing the role of an order parameter. • The presence of project stakeholders capable of fulfilling the task of catalysts for development. • Coherence in actions, readiness for contacts, and compromises of the driving forces of the project—representatives of business structures, authorities (municipal, regional, federal), scientific community, financial capital, active part of the population of the territory. The system of differential PSEDT models developed on the basis of the institutional and synergetic approach is designed to create institutes and institutional relations, with which development of a territory will be “continuously renewed, benefiting from unpredictable events, shocks, stresses and variability” rather than designed to add up losses resulting from them. Besides, of importance is not only transition of the controlled system to a new trend but also selection of the best possible development scenarios, i.e., production of a synergetic effect.

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Models of Spatial Organization of Regional Economies Yulia Lavrikova

and Arina Suvorova

Abstract The socio-economic development of regions is closely connected with their spatial organization, which explains scholarly interest in this topic, especially the selection of parameters that should be targeted for spatial optimization. This study models the spatial organization of regional economies. Methodologically, it is based on the methods of analysis and synthesis, abstraction, generalization, and classification. The study describes the universal parameters of an optimally organized economic space such as integrity, cohesion, heterogeneity, and polycentricity as well as the parameters that may have different significance for regions of different types such as openness and changeability. Heterogeneity and cohesion may also have different significance for different regions. Special attention is given to patterns of economic activity in regions of different types and the corresponding models of spatial organization—zonal, functional, hierarchical, and network models. The models are also aligned with the key strategic areas of spatial development. The study shows the need for a more differentiated approach to the choice of goals and priorities of regional spatial development and its findings may be used for strategic policymaking on the regional and national levels. Keywords Spatial organization · Regional economy · Economic space · Modeling · Spatial development

1 Introduction Spatial aspects of economic development shape the transformations of large economic systems and thus determine the role played by specific parameters of spatial organization in the management of such systems. In this time of unprecedented Y. Lavrikova (B) · A. Suvorova Institute of Economics of the Ural Branch of the Russian Academy of Sciences, 29 Moskovskaya St., Ekaterinburg 620014, Russian Federation e-mail: [email protected] A. Suvorova e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_7

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change, when all aspects of social and economic life are undergoing rapid transformations and new challenges emerge every day, the questions of spatial planning and management are also gaining currency. Since spatial transformations normally happen at a rather slow pace, it may be difficult to adjust spatial parameters to changing circumstances, and, therefore, in spatial planning and management it is especially important to set the right priorities and goals from the very beginning. Management of spatial transformations on the regional level occupies an important place in policies of individual countries or groups of countries. Other documents such as guidelines, strategies, and programs of different levels deal with more specific aspects of regional socio-economic transformations. Spatial development is a separate subject of state regulation: the goals, priorities, indicators, and tools of spatial development are described in a wide range of framework documents such as the European Spatial Development Perspective (ESDP, 1999) in the European Union; the Concepts and Strategies for Spatial Development (CSSD, 2016) in Germany; the National Spatial Strategy (NSS, 2015) in Japan; and the Spatial Development Strategy (SDS RF, 2019) in Russia. Even in countries where there is no unified approach to the management of spatial transformations or the national system of spatial planning has not been formed yet (for example, in the USA (Elliot, 2008; Gawronski et al., 2010), Canada (Gajevski & Sagan, 2020), and Brazil (Rocco et al., 2019), spatial policies are actively implemented on the regional and local levels. Interestingly, despite the differences in the scales of territorial systems and their spatial organization, these policies tend to highlight more or less the same aspects: the development of agglomeration processes and core zones (special economic zones); creation of the infrastructure and conditions to foster resource mobility; enhancement of interregional cooperation; and provision of support for rural and struggling areas. Scholars, no less than policymakers, are interested in the problems of spatial planning and management. The majority of these studies analyze the spatial characteristics of national and regional economic complexes at present (Hudson, 2021; Jung & Vijverberg, 2019; Santos & Vieira, 2020) or retrospectively (Feldman et al., 2021; Miessner, 2020). They also describe the methodological tools for such (Fang et al., 2020; Guillain & Le Gallo, 2020; Modica, 2017) and examine specific elements of spatial planning and management that should be targeted in order to solve problems faced by territorial systems (Brezden & Szmytkie, 2019; Karahasan & Bilgel, 2019). A separate group of studies deals with the problem of optimal spatial development: these studies explore the mechanisms and paths of spatial optimization. Their findings normally agree with the ideas underpinning spatial planning policies and strategies: much attention is paid to enhancing territorial cohesion (Madanipour et al., 2021; Popovic et al., 2021) and minimizing regional disparities (Papageorgiou, 2017; Kuran & Bayraktar, 2020; Broitman & Czamanski, 2021), in particular through the stimulation of struggling territories (Bondarenko, 2018; Lofving et al., 2021; Ushachev et al., 2021). Some studies, on the contrary, probe for weak spots in the official concepts of spatial development (Belyaeva et al., 2021; Blanutsa, 2021) or explore the reasons why the execution of such strategies and plans may fail (Cotella et al., 2012; Faludi, 2021).

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It is impossible to develop a single vision of optimal spatial organization for all territories: each territorial system has its own spatial characteristics which constitute its unique socio-economic profile. Therefore, to set the priorities of a certain region, it is necessary to take into account its unique spatial characteristics. In other words, the general principles of spatial development should be supplemented with customized regional solutions. Thus, although there are goals and priorities of spatial development that all territories share, policymakers should be aware of their country’s or region’s unique characteristics such as the size of the territory in question, the trends and patterns in the development of its economic system and the corresponding social processes. It is also important to distinguish between the characteristics that can be adjusted and the characteristics that are impossible to change. Furthermore, it is essential that the general priorities of national spatial development should be adjusted to meet the needs of individual regions. In this study, the analysis focuses on Russian regions, which vary significantly in terms of economic structure and problems, with the purpose to describe an optimal model of economic spatial organization that would take into account regional specifics. This research objective determines the following set of interconnected tasks. First, some general parameters of optimal spatial organization will be described. These will be followed by the parameters whose significance varies for regions of different types. Afterward, the analysis will focus on patterns of economic activity in regions of different types and the corresponding strategic priorities for these regions’ spatial development.

2 Optimal Spatial Organization of Economic Systems: General Parameters Although many studies discuss at length the topic of spatial organization in social and economic spheres, there is no uniform approach to the definition of the term “spatial organization” (Albasri, 2018; Acosta & Lyngemark, 2021). Spatial organization is usually discussed in relation to such questions as the location of production facilities and resources (Adler et al., 2019; Kaplan et al., 2020; Ovchinnikov et al., 2019), although the term “spatial organization” may also be used in the analysis of organization of the living environment (Martinez-Arino, 2020). However, in the context of economic research, spatial organization is usually approached by investigating the relationships between economic processes and individual elements of physical space. Such studies may consider the question of the optimal location of economic entities in relation to each other or the optimal zoning parameters. Some studies seek to justify the need for the establishment of special tax regimes or special economic areas (special economic zones, clusters, and so on) in certain locations. In some cases, the terms used by the authors to denote the best form of spatial organization appear rather questionable: for example, Sun et al. (2019) refer to “rational” spatial organization; Shkuratov et al. (2021) to more “efficient” spatial organization; while

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Tang et al. (2018) discuss the ways of “optimizing” spatial organization. A similar diversity may be observed in the studies that formulate the requirements an economic space needs to meet in order to qualify as “optimal,” “rational,” or “efficient”: these include contrast (Corpataux & Crevoisier, 2007; Kozhevnikov, 2019), heterogeneity (Cantwell, 2014; Rey & Janikas, 2005), openness (Astapenko, 2018; Prykhodko, 2017), and connectedness (Cicerone et al., 2020; Kolmakov et al., 2019; Lorenzen et al., 2020). According to the Spatial Development Strategy of the Russian Federation (SDS RF, 2019), optimal spatial organization should ensure the integrity of space (all elements should be regulated by the same rules and share the same opportunities for development) and cohesion (development of cooperation between individual elements of space, e.g. regions and municipalities). In addition, all elements of space should be able to preserve their unique characteristics (economic, historical, cultural, etc.), which should be given due regard by the policies initiating transformations of the spatial complex. In her analysis of the theoretical framework behind the Spatial Development Strategy of the Russian Federation (the analysis was conducted when only the Concept of the Strategy was available), Mikheeva (2018) points out that it is based on the principles of spatial cohesion (integrity and connectivity of space) and spatial accessibility of social goods (equal access to resources for all the citizens regardless of their place of residence). Moreover, a comprehensive approach is applied to spatial transformation, that is, spatial transformation projects should take into account a multitude of factors—geopolitical, economic, and social. Other principles outlined by Mikheeva (2018) include balanced spatial development (preventing the outflow of people or their excessive concentration in specific locations, development of new growth centers); combination of state regulation with market self-regulation mechanisms; and the absolute value of each location (each element of the spatial complex should have its own goals and priorities; this principle also underlies long-term spatial development strategies of the European Union). The approach to spatial organization described in the Strategy has much in common with the principles declared in the plans and programs of the EU. It should be noted that in the last decades, much importance has been attached to the regulation of spatial transformations in the EU. The priorities are specified in the Guiding Principles for Sustainable Spatial Development of the European Continent (Guiding Principles, 2000), adopted in 2000 at the 12th session of the European Conference of Ministers Responsible for Regional Planning (CEMAT). This document aims to ensure a more balanced development of the European continent, eliminating the disparities between its countries and regions or what the document refers to as the “gap between the ‘two Europes,’” that is, between the old and new members of the EU. According to this document, an optimally organized space should possess such qualities as integrity, cohesion, connectedness of individual elements, and the absence of large disparities between the elements. The concept lays a special emphasis on sustainability, which implies that the present generations should act in the interests of future generations for preservation of cultural and environmental heritage. It should be noted that in Russia, the priority of relative spatial homogeneity (reduction of

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interterritorial disparities) is given less attention while the main accent is placed on the “reduction of regional disparities in the living standards and quality of life.” This vision is reflected in one of the priority areas outlined in the Strategy. In reality, however, the most persistent and critical disparities are often found on the municipal level (inside one region)—these are the disparities that constitute the urban–rural divide. In the research literature, one can find different variations of the list of principles of spatial development. For example, Shvets (2016) points out the following principles of regional spatial development: • sustainability, that is, the need to maintain the balance between economic, social, spatial, and environmental priorities; • maximization of the efficiency in the use of spatial potential (resources of the territory); • the balance of interests of all the economic entities involved in the realization of the spatial development strategy (including local communities); • consistency (the need to embrace the diversity of factors and their results); • coordination of all the strategic priorities of territorial and economic development; • polycentricity based on multiple zones of advanced development with network effects; • development of different partnership types in the region; • unity, consistency, coherence, and complementarity of different types of strategic planning; • continuity of framework documents; • openness and transparency of decision-making in the sphere of spatial development; • segmentation of territories into types and differentiation of approaches to their development; • concentration of resources and town-planning activity in growth points; • subsidiarity and reciprocity. The space organized in accordance with these principles is polycentric, has a network structure (its individual components are actively interacting with each other), it is heterogeneous and its elements differ in terms of resources, their concentration, and use. The above-described principles can be systematized and summarized to formulate the parameters of optimal spatial organization: • integrity (all elements of space should enjoy the same opportunities and there should be no barriers between them); • cohesion (economic entities in different parts of the region should enjoy ample opportunities for partnership and cooperation, in particular through developed infrastructure); • heterogeneity (elements of space have different characteristics and thus require a differentiated approach to their development);

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• polycentricity (there is a wide range of centers and areas of development with varying functions and of varying scale). This list requires some further comments. First, some important aspects (sustainable development goals; alignment of priorities of economic and spatial development, etc.) that were discussed in the studies cited above and are taken into consideration by policymakers and public administrators were not included in this list because they determine ways and methods of spatial transformation rather than spatial organization as such. Second, some parameters that are significant to regional systems (openness, changeability) will be discussed in more detail further: at this stage, the focus is made on the universal parameters characterizing efficiently organized space in spatial complexes of different scales regardless of the geopolitical conditions in which they operate. Third, certain parameters of spatial organization may be more or less pronounced in some territorial systems (in other words, not only are these parameters detected on the qualitative level—as existent or non-existent—but also on the quantitative level—whether they are present to a greater or lesser extent in comparison with the territory taken as a reference). For instance, the optimal level of polycentricity and the number of growth centers (including potential ones) can be different in different conditions.

3 Parameters of Optimal Spatial Organization Depending on Types of Regions The previous section discussed universal parameters of optimal spatial organization, although their significance may vary. Moreover, this list can be supplemented with other parameters important for specific types of regional complexes. On the other hand, even a more precise list of parameters provides a general framework for optimization of regional economic space: in each particular case, depending on specific regional conditions or timeframe, further adjustments will be necessary. In order to compile a more precise list of parameters, regions should be grouped according to their characteristics. Such typology may be based on different approaches. We propose to focus on the resource potential of regions. A more detailed description of the typologization procedure and its results exemplified by the cases of Russian regions (Table 1, Fig. 1) were given in my previous studies (see, for example, Suvorova, 2021). Therefore, for brevity’s sake, here only the general outline of the typology is provided. The resource-based typology of Russian regions is illustrated in Fig. 2. Each of the eight types of regions is highlighted with a separate color corresponding to the color-coded indication in Table 1. The algorithm of typologization takes into account the competitive advantage gained by regions having access to different resources. Regions may possess

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Table 1 Resource-based typology of regions

Regions relying predominantly on intangible resources

Regions with comparative advantages (traditional resources)

Regions relying predominantly on tangible resources

Regions without Regions with comparative comparative advantages advantages (traditional (traditional resources) resources)

Regions without comparative advantages (traditional resources)

Regions with comparative advantages (innovative resources) Regions without comparative advantages (innovative resources)

Fig. 1 Resource-based typology of Russian regions

resources of the two main types—tangible or “traditional” resources (e.g. human potential, natural resources, and previously produced goods) and intangible or “new type” resources (the so-called knowledge assets securing regions competitive advantage in the information economy). Moreover, it is important to consider the form

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regions with access to 'new type' resources

closedness of space

openness of space mid regions lacking in 'new type' resources

Fig. 2 Significance of openness for regions of different types

of resources that regional economies depend on: resources may differ in terms of mobility or the degree of their attachment to specific locations. In each of the three variants of the typology the regions are divided into two groups. As a result, eight groups are differentiated according to their access to “traditional” or “new type” resources and the form of the key resources (tangible or intangible). The algorithm of typologization takes into account the competitive advantage gained by regions having access to different resources. Regions may possess resources of the two main types—tangible or “traditional” resources (e.g. human potential, natural resources, and previously produced goods) and intangible or “new type’ resources (the so-called knowledge assets securing regions competitive advantage in the information economy). Moreover, it is important to consider the form of resources that regional economies depend on: resources may differ in terms of mobility or the degree of their attachment to specific locations. In each of the three variants of the typology the regions are divided into two groups. As a result, eight groups are differentiated according to their access to “traditional” or “new type” resources and the form of the key resources (tangible or intangible). It should be noted that this is a rather general typology (based on regions’ access to specific kinds of resources): with time, regions may change their positions for objective reasons (for example, if the resources that used to be crucial for the region’s economic growth are depleted or exhausted) or because of the policy-driven transformations (for example, if a region actively implements innovation policies and policies aimed at increasing the significance of the “new type” resources). Moreover, even if a region does not change its place in the typology, it may be undergoing a transition from one category to another (for example, because of the changes in its economic specialization). What matters the most is not so much the resources regions have but the resources that their key actors rely on for their development. All of the above does not restrict the applicability of the proposed approach for optimization of spatial economic organization. However, it is necessary to take into consideration not only the characteristics of regions but also the threats they are facing, and the

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prospects and goals of their development in the future. Depending on the purpose of research, some groups of regions may be merged into larger groups (and broken down again into smaller subgroups at later stages of analysis) within each of the three criteria. Before looking at the parameters of regional spatial economic organization, we need to discuss some of the characteristics that may define space but have previously escaped our attention. One of such characteristics is openness, which has much in common with the above-mentioned parameter of cohesion. An economy needs to be open in order to develop (Coulibaly et al., 2018). The openness of an economic system, in its turn, implies the absence of economic barriers to interactions with the external environment. In other words, for an economic system, being open means being able to serve as a place of cooperation (and competition) for external actors (Nijkamp, 2021). Not every space can be described as open, in other words, openness is not an intrinsic quality of economic space since the degree of openness may vary considerably for different spaces. Closed systems may be quite effective and better organized than open ones, although regional complexes by their very nature cannot be closed as their willingness and readiness for interactions with the external world (other regions) and openness to new contacts is an important factor of successful economic performance. It should, however, be noted that in real life, the existence of a completely open (or completely closed) space is impossible. Spatial openness is crucial for regions lacking in advantages associated with access to the “new type” resources: territorial complexes from this group are often lagging behind others and their economic efficiency is much lower than that of the regions making active use of new technologies and innovations. Therefore, a high degree of spatial cohesion (on the macro-level, not on the regional level) enables such regional structures to get involved into large-scale economic projects, interact with more successful partners, and thus receive an impetus for development. Thus, spatial openness is a major factor helping these regions benefit from relationships with strong partners. This does not mean, however, that regions abundant in “new type” resources and enjoying higher rates of economic growth should be more close and minimize contacts with other participants of economic processes. Nevertheless, for these regions it is less important to stay open than the outsiders of innovation development (Fig. 2). One more parameter that may take different forms in different conditions and in different regional systems is the ability of space to change or its changeability. Spatial transformations may unfold at a slow pace, which is why the speed of socio-economic transformations in a region may exceed significantly that of its economic spatial reorganization. Nevertheless, space never ceases to change and its characteristics are also constantly changing (although slowly): space can become more or less polarized, attract new actors to gain momentum for the next stage of transformations, and so on. Spatial development may be intensive or extensive (for example, if it is caused by the shifting of boundaries). The speed of change as well as its nature may be different while the space itself may be more resilient to change (or stable) or, on the contrary, more changeable (or flexible).

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Changeability is an ambivalent parameter: on the one hand, it is important that the region’s spatial structure should be flexible enough to keep up with the socioeconomic changes; on the other hand, the lack of resilience and occasional changes in the localization of production forces may result in socio-economic instability and impede the achievement of long-term goals. Moreover, if a system is inertial, a substantial amount of resources will be required to accelerate change should the need arise. Therefore, the optimal solution would be to balance resilience and changeability, although regions lacking in “traditional” and “new type” resources should foster the changeability of their spatial structure, which has a reciprocal influence on socio-economic transformations by reflecting and stimulating them. If a region is lagging behind other, wealthier territories, the focus should be made on enhancing the efficiency of resource use and searching for new (alternative) points (or centers) of economic growth. Territories whose low competitiveness stems from the lack of resources should improve the adaptability of their space (Fig. 3). Although heterogeneity is characteristic of any economic space (as Zubarevich, 2014 rightfully observes, “it has to do more with economic rather than social inequality”), in regions relying predominantly on tangible resources the degree of heterogeneity will be considerably higher. In future, however, this situation is likely to change: the regions relying on immovable assets with specific localization in space will not be able to distribute these resources efficiently across their territory (unlike the regions whose resources are intangible and movable). Such heterogeneous space may be considered optimally organized only if it displays a high degree of cohesion, which means that the leading localities (that is, centers concentrating the resources for regional growth) may stimulate the development of other elements of the spatial complex (Fig. 4). An optimally organized space in regions reliant on intangible resources tends to be more homogeneous although space cannot be completely homogeneous (even though

regions with access to 'new type' resources but lacking in 'traditional' resources

regions lacking in 'traditional' and 'new type' resources

stability of space

changeability of space mid

regions with access to 'traditional and 'new type' resources

regions with access to 'traditional' resources but lacking in 'new type' resources

Fig. 3 Significance of spatial changeability for regions of different types

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heterogeneity of space

homogeneity of space mid

regions relying predominantly on tangible resources

regions relying predominantly on intangible resources

cohesion of space

fragmentation of space mid

Fig. 4 Significance of spatial heterogeneity and cohesion for regions of different types

non-material resources are generally more mobile, they still tend to concentrate in specific locations). Spatial cohesion is important for regions of this type but not as important as for regions specializing in manufacturing. The significance of specific parameters for regions of different types is discussed here from the qualitative rather than quantitative perspective since it is quite difficult to calculate the precise values of each parameter for specific groups of regions. Even within the same groups, the regions may be quite diverse, which makes any attempts to set a specific reference for all members of the group an exercise in futility: a much more viable solution, therefore, is to align groups of regions with the corresponding models of spatial organization.

4 Models of Economic Spatial Organization of Regions of Different Types Modeling of spatial development and economic spatial organization is of particular interest to studies of large territorial systems and their transformations. Variations of integrated models describing the factors and patterns of economic spatial organization were proposed by the landmark studies that contributed to the development of classical location theories (Launhardt, 1882; von Thunen, 2009). With time, scholarly interest shifted from the interests of individual economic entities in need of the best location for their activities to problems of spatial organization of

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large territorial systems. This gave rise to location theories (Hotelling, 1929; Losch, 1954), the growth pole theory (Boudeville, 1966; Perroux, 1988); the cluster theory (Porter, 2008), and models of the new economic geography (Dixit & Stiglitz, 1977; Friedmann, 1966; Krugman, 1991). Models describing the criteria of optimal location of elements of economic systems in a country or a group of countries are of special interest. The most illustrative examples of the European models are the Blue Banana (Brunet, 1989), Bunch of Grapes (Kunzmann & Wegener, 1991), Europe of 7 Apartments (Lutzky, 1990), and Red Octopus (Van Der Meer, 1998). The models of spatial organization for Russian territories can be roughly divided into two groups (Suvorova, 2020): the models focusing on specific elements (for example, the model of priority development of the territories with the maximum potential) and the “all-inclusive” models, that is, the models that require the involvement of all spatial elements into the general transformation processes (for example, the model of functional zoning). Overall, there may be distinguished four types of models of spatial organization (Fig. 5). The zonal model means that a region’s assets and resources are concentrated in a limited number of localities and areas, which may share economic relationships with each other or may be included into large economic chains and not interact with their nearest neighbors.

Fig. 5 Models of spatial organization

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In the hierarchical model, like the functional model, individual components of a spatial complex may be interconnected. The difference between these two models lies in the fact that while in the hierarchical model, most of these relationships are vertical (they are shared by economic centers of different scales and with different goals of development), the functional model is based primarily on horizontal connections. In the functional model, the space is loosely divided into areas performing different functions (depending on their economic specialization) and not always interacting with each other. The network model is more complicated and is based on the principles of decentralization (no single center or axis of development) and cohesion (horizontal connections prevail and encompass all spatial elements). According to Castells (2009), network structure is a complex of interconnected nodes, that is, individual components of a network are sophisticated multi-functional entities. The network structure is open, which allows for the incorporation of new elements (Kim & Lee, 2018). The above-described models differ in terms of their complexity and may be interpreted as stages in the evolution of spatial organization, which may evolve from the zonal to network model. Obviously, it may be difficult to fit specific regions into these models as regions may comprise a large number of diverse economic systems, each with its own peculiarities. Moreover, some elements of different models may co-exist harmoniously (for example, inside functional areas there may be local hierarchical structures). Space itself may be in the process of transition from one model to another and thus combine the characteristics of different models. This typology may be used to select those properties of the models that correspond to the aspects that constitute optimal spatial organization of regional economies. In Table 2, the region types described above are aligned with the most suitable models of spatial organization and the most significant spatial parameters. Although the network model is now considered to be the most efficient form of spatial economic organization (especially on the national level) (Bilozor et al., 2018; Brand & Drewes, 2020; Czerny & Czerny, 2019; Zhao et al., 2021), not every region at its current stage of development is ready to implement it. As was noted above, the network model is the most sophisticated of all models and the region’s spatial structure has to “mature,” especially in terms of economic growth, for this model to be implemented. Therefore, for regions lacking in comparative advantage (e.g. access to tangible or intangible resources), the optimal solution could be the simplest zonal model. After these regions change their position in the typology, for example, through the acquisition or development of resources that may serve as a source of competitive advantage, they may move to a higher level model. The key strategic goal of spatial development in such regions is to identify the most promising centers of economic growth of the whole territorial complex (or its part), places of their localization in space, architecture of their possible interconnections with the surrounding territorial systems, and with more remote ones. The pivotal parameters for regions of this type is changeability and openness. Changeability means that with time, as the potential of such regions grows, their systems of spatial relationships become more complex and so

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Table 2 Characteristics of the proposed models of spatial organization in regions of different types Regions relying predominantly on intangible resources

Regions with comparative advantages (innovative resources) Regions without comparative advantages (innovative resources)

Regions relying predominantly on tangible resources

Regions with comparative advantages (traditional resources) Network model C -----|-▼--- O S-----▼-----C Ho ----|---▼-He F -----|---▼- C

Regions with comparative advantages (traditional resources) Network model C -----|-▼--- O S-----▼-----C Ho ----|-▼---He F -----|-▼--- C

Regions without comparative advantages (traditional resources) Network model C -----|-▼--- O S -----|-▼--- C Ho ----|-▼---He F -----|-▼--- C

Functional model C -----|---▼- O S -----|-▼--- C Ho ----|-▼---He F -----|-▼--- C

Zonal model Hierarchical model C -----|---▼- O C -----|---▼- O S -----|-▼--- C S -----|---▼- C Ho ----|-▼---He Ho ----|---▼-He F -----|---▼- C F -----|-▼--- C

Regions without comparative advantages (traditional resources) Network model C -----|-▼--- O S -----|-▼--- C Ho ----|---▼-He F -----|---▼- C Zonal model C -----|---▼- O S -----|---▼- C Ho ----|---▼-He F -----|---▼- C

C–O: Closedness–Openness; S–C: Stability–Changeability; Ho–He: Homogeneity–Heterogeneity; F–C: Fragmentation–Cohesion

does the model of spatial organization. Openness is important because without largescale network interactions or technological chains, these regions will hardly manage to build up enough momentum for a radical transformation. As was mentioned above, regions which rely predominantly on tangible resources need to enhance the cohesion between individual elements due to the higher degree of spatial heterogeneity (for objective reasons). The functional model can be the optimal choice for regions rich in “traditional resources” and specializing in the service sector but lagging behind other territories in terms of “new type” resources. The most significant strategic areas of regional spatial development based on this model include the following: smart and efficient zoning to enhance the economic potential of the whole regional complex as well as its individual participants (through the economic integration of entities in the same locations) and enhancement of the relationships between specific actors with a similar set of products or services in the same sector (located in the same zone). For these regions it is especially important to be able to join higher order economic zones, which leads them to prioritize openness. Although the network model is now considered to be the most efficient form of spatial economic organization (especially on the national level) (Bilozor et al., 2018; Brand & Drewes, 2020; Czerny & Czerny, 2019; Zhao et al., 2021), not every region at its current stage of development is ready to implement it. As was noted above, the network model is the most sophisticated of all models and the region’s spatial structure has to “mature,” especially in terms of economic growth, for this model to be implemented.

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Therefore, for regions lacking in comparative advantage (e.g. access to tangible or intangible resources), the optimal solution could be the simplest zonal model. After these regions change their position in the typology, for example, through the acquisition or development of resources that may serve as a source of competitive advantage, they may move to a higher-level model. The key strategic goal of spatial development in such regions is to identify the most promising centers of economic growth of the whole territorial complex (or its part), places of their localization in space, architecture of their possible interconnections with the surrounding territorial systems and with more remote ones. The pivotal parameters for regions of this type is changeability and openness. Changeability means that with time, as the potential of such regions grows, their systems of spatial relationships become more complex and so does the model of spatial organization. Openness is important because without largescale network interactions or technological chains, these regions will hardly manage to build up enough momentum for a radical transformation. As was mentioned above, regions which rely predominantly on tangible resources need to enhance the cohesion between individual elements due to the higher degree of spatial heterogeneity (for objective reasons). The functional model can be the optimal choice for regions rich in “traditional resources” and specializing in the service sector but lagging behind other territories in terms of “new type” resources. The most significant strategic areas of regional spatial development based on this model include the following: smart and efficient zoning to enhance the economic potential of the whole regional complex as well as its individual participants (through the economic integration of entities in the same locations) and enhancement of the relationships between specific actors with a similar set of products or services in the same sector (located in the same zone). For these regions it is especially important to be able to join higher-order economic zones, which leads them to prioritize openness. For regions with the same characteristics as the above-described type but oriented toward manufacturing to a greater extent than toward the service sector, an optimal solution would be the hierarchical model, which implies building a vertical economic structure comprising large industrial enterprises as nodes and auxiliary supporting units. For efficient spatial development, such regions need to build value chains and vertical connections, and involve the so-called peripheral areas lagging behind other parts of the regional complex into the production processes of economic growth centers. The latter can be achieved by activating the potential of peripheral areas for joint realization of projects with regional leaders. To this end, it is essential to identify those local growth points that could become part of the hierarchical structure and to facilitate the interactions of these points with the “nodes” of the structures and with each other. The network model is more suitable for regions with access to “new type” resources. To realize this model, a more complex economic structure is needed, which includes a more complex structure of network nodes and the architecture of the relationships between them. It should be noted that individual enterprises cannot be independent elements of such structures while the role of nodes is usually performed by larger complexes uniting actors in pursuit of common goals. As for the

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architecture, the connections between the components of networks may be horizontal or vertical. In some cases they may be more disorganized and changing fast together with the changing needs and demands of economic entities. It should be noted that the network model can be used in regions of different types through the adjustment of the most significant parameters. For example, for regions relying primarily on tangible resources (and expecting to rely on them in the future) it makes sense to build their networks around the already existing production “core” while the network structures evolving in regions specializing in the service sector are likely to be more dispersed in space. Maintaining the balance between stability and change in spatial economic organization is a goal that is mostly relevant to regions rich in resources of different types. If a region does not have access to “traditional resources,” then it is much more important than its spatial organization should remain flexible: the higher is the likelihood of transformation of the regional economy, the greater is the need to adjust the region’s spatial parameters to adapt to such transformation. To summarize all of the above, the key priorities and aspects of regional spatial development are as follows. First it should be noted that some priorities are applicable to all regions. These are determined by the set of parameters of an optimally organized economic space described in research literature and in strategic planning documents such as the EU’s Guiding Principles cited above. These parameters are as follows: • elimination of the barriers between specific spatial components; tackling the existing disparities and ensuring equal opportunities for all components (integrity); • creating conditions for communication between economic entities from different parts of the regional space, for example, through developed infrastructure (cohesion and openness); • preservation of the uniqueness of specific elements and application of a differentiated approach to their transformation, in particular the development of growth poles (heterogeneity); • development of centers and areas of growth (including potential ones) with different features and of different scales (polycentricity). This list does not include the aspects corresponding to changeability, which can be explained by the fact that changeability of space is associated not with the strategic areas of development but with the nature of the transformation process itself. Despite the significance of this parameter, not all regions should prioritize it—for example, for some regions, especially those with access to a wide range of resources, it is more important to maintain the balance between stability and change. Second, it is important to consider regional resource configurations, which may shape the spatial transformations of economic systems of different types and require certain adjustments to the previously set general priorities (Table 3). In practice, there may be constraints to the realization of the models stemming from the specific conditions in the regions and the corresponding peculiarities of regional systems, socio-economic policymaking (including the national policymaking process), and the established approaches to problem-solving determined by

Regions without comparative advantages (innovative resources)

Advanced development of the zones that hold the greatest potential; identification of their possible partners and the architecture of relationships between them

Openness

Openness Changeability

Parameters of special significance to this type of regions

Efficient zoning; development and enhancing of the relationships between actors with a similar set of products or services in the same sector

Openness Heterogeneity Cohesion

Emergence of local growth points; creating conditions for their interaction with the ‘nodes’ of the structure and with each other; building a vertical economic structure

Openness Changeability Heterogeneity Cohesion

Advanced development of zones that hold the greatest potential; identification of their possible partners and the architecture of relationships between them

Regions with Uniting actors in pursuit of common goals into larger complexes; helping them establish and maintain flexible connections (both comparative horizontal and vertical) based on the principle of decentralization; involvement of all the elements of space into network processes advantages (innovative Parameters of special significance to this type of regions resources) – – Heterogeneity cohesion Heterogeneity cohesion

Regions without comparative advantages (traditional resources)

Regions with comparative advantages (traditional resources)

Regions with comparative advantages (traditional resources)

Regions without comparative advantages (traditional resources)

Regions relying predominantly on tangible resources

Regions relying predominantly on intangible resources

Table 3 Strategic areas of spatial development for different types of regions, based on the proposed models of spatial organization

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the spatial organization. Thus, further research could explore the possibilities of implementing these models in Russia and determine the mechanisms of adjusting regional systems to the proposed models.

5 Conclusion The unprecedented speed of change in the socio-economic sphere creates new challenges for the development of economic entities and their groups, which makes the task of spatial planning and management more urgent than ever. What comes to the fore is the choice of the optimal direction of spatial development, taking into account the factor of spatial inertia, which renders any adjustment of spatial characteristics more difficult. A well-organized space displays such qualities as integrity, cohesion, heterogeneity, and polycentricity. Moreover, spatial development cannot be addressed separately from socio-economic development. There is no one-size-fits-all solution to organizing spatial transformations: territories of different scales may have their own unique characteristics, which are resistant to change, as well as patterns of social and economic activity. This means that the universal parameters for optimal spatial organization may be adjusted to fit the unique profiles of regions. For instance, one of the key priorities for regions lacking in comparative advantage is to identify the most promising centers of economic growth, their localization in space, and the architecture of their possible interconnections with the surrounding systems and more remote ones. The main strategic areas in the spatial development of regions oriented toward the service economy and, though rich in “traditional resources,” lagging behind other Russian regions in terms of access to “new type” resources is smart zoning, enhancement of the relationships between the regions sharing similar features. To transform regions specialized in manufacturing, it is necessary to build a vertical economic structure consisting of large-scale industrial enterprises as its major nodes and supporting auxiliary units. Regions whose comparative advantages lie in the access to “new type” resources should prioritize building a more complex structure of nodes and architecture of the relationships between them. Regions relying primarily on tangible resources should be constructing their networks around the already existing production “core.” The models of optimal spatial organization differ for regions of different types. The zonal model works best for struggling regions; the functional model, for regions specializing in the service sector and having access to “traditional” resources but lagging behind in terms of “new type” resources; the hierarchical model, for regions specializing in manufacturing and leading in terms of “traditional” resources but lagging behind in terms of intangibles; and, finally, the network model, for regions whose competitive advantage stems from the availability of intangible assets. In practice, this differentiated approach requires further adjustment: what we have outlined as the current direction of spatial transformations can help determine future goals of the necessary changes. The findings of this research can be used to improve

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diagnostic methodology for analyzing spatial organization of regional economies and may contribute to priority-setting for future spatial development. Acknowledgements The research was prepared in accordance with the grant of the President of the Russian Federation for state support of young Russian scientists MK-3442.2019.6

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Overcoming Interregional Economic Disparities in Russia Through Implementation of Resource Projects Valery Kryukov , Nikita Suslov , and Yakov Kryukov

Abstract This paper examines the state and analyzes the possible directions of development and functioning of the fuel and energy complex (FEC) of Russia and, in particular, Asian Russia (territories located to the East of the Urals) from the perspective of being integrated into the globally promoted “agenda” of energy transition. It is shown that the lag in solving such issues as forming the sector of scientific and production services in the East of Russia, as well as specialized machine building and deeper integrated processing of fuel and energy resources, significantly reduces the chances of achieving both the stability of socio-economic development and the solution to the problems of reducing carbon intensity in this territory. Solving the above-mentioned problems requires procedures and approaches to the institutional adjustment of the system of state regulation as well as managing the processes of development and use of the huge energy potential of the East of the country. A special role in this is assigned to forming a feedback of export-oriented supply of energy resources along with the implementation of projects aimed at changing the structure of the economy of Asian Russia. The authors believe that the currently observed growth of demand for domestic primary energy resources in the markets of China and the Asia–Pacific region as a whole should not be considered as a sustainable long-term trend. Improving the stability of fuel and energy complex of Asian Russia and its economy as a whole makes it necessary to consider and develop projects that rely on the connectivity and interaction of different sectors and regions.

V. Kryukov (B) · N. Suslov · Y. Kryukov Institute of Economics and Industrial Engineering of the Siberian Branch of the Russian Academy of Sciences, 17 Academician Lavrentyev Av., 630090 Novosibirsk, Russian Federation e-mail: [email protected] N. Suslov e-mail: [email protected] Y. Kryukov e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_8

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1 Introduction In the first quarter of the twenty-firstcentury, Russia is once again considering stepping up the development of the east of the country—first of all, of the territories lying beyond Lake Baikal. The principal reasons for the increased attention and efforts to solve the problems of socio-economic development of these territories are complex and multidimensional in nature. Namely: external reasons—geopolitical (strengthening and rapid development of the southern neighbor—China), global economic (relocation of the epicenter of the world economy to the Asia–Pacific Region and to South-East Asia);—socio-economic, environmental, and demographic ones. It should be noted that many of these problems are far from new (Levitov, 1905). A significant range of internal problems has a “deferred nature”—they have been accumulated over the years and require an urgent solution (first of all, these are the issues of elimination/neutralization of previously caused environmental damage). We do not subscribe to the viewpoint that it is necessary to speed up the development of Eastern Russia only for reasons related to the exhaustion of the most effective natural resource potential in the western part of the country in previously intensively developed macroregions, such as the North of the European part and the Urals. In our opinion, it is necessary to strengthen the role and importance in modern society (of which the East of Russia and, even more so, the Far East are an integral part in our country) of new value criteria in the implementation of socio-economic problems and tasks. The list of new value criteria is now better known as the SDG (Sustainable Development Goals), an initiative of the United Nations to define sustainable development goals (What are the Sustainable Development Goals, 2015). The most important, above all, include the provision of equal basic realistic conditions and opportunities for life and activity for all citizens of the country, regardless of where they live. The difficult demographic situation, which is observed in the eastern regions of the country, is, to a certain extent, a reflection of the growing importance of solving the problems of socio-economic development in this direction. It is quite obvious that without the development of a modern and dynamic economy in the East of Russia, overcoming these trends is not possible. At the same time, in our opinion, the transition to a new system of value criteria cannot be carried out both within the framework of spontaneous integration into global economic processes (in the form, for example, of hasty introduction into value chains, which leads to consolidation on the lower “floors” of commodity redistribution), and within the framework of isolationism and “bet on your own forces.” The solution of problems and tasks of this level requires a very fine and scrupulous adjustment of the institutional system in various spheres of socio-economic activity, as well as the related creation and development of technological systems that meet modern and economic and environmental realities (Industrial policy for the Sustainable Development Goals, 2021). We share the point of view of our colleagues from the Institute of Economic Research of FEB RAS that “…the transformation of part of export flows into products and services exported to North-East Asia…involves much larger and more finely focused institutional modernizations” (Russian Far East…, 2017, p. 97).

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We propose considering tendencies, problems, and approaches to the formation and tuning of the institutional system focused on the solution of urgent socioeconomic problems of Asian Russia (which we include Siberia and the Far East) and corresponding to the specifics of modern processes of formation of new value criteria we assume to consider on the example of the leading complex of industries—fuel and energy sector.

2 Trends in the Fuel and Energy Sector Currently, it is generally accepted that the most important problems of the economy of vast territories of Asian Russia include (Proposals to the Plan of Integrated Development of Siberian Branch of RAS, taking into account the priorities & long-term plans for the development of the Siberian Federal District, 2018): the need to accelerate technological development, and compression of the innovation cycle, which means a significant reduction in the period between the appearance of new knowledge and the creation of technologies, products, and services; the continuing significant role of raw materials and energy production in the GDP structure of Russia (which negatively affects the sustainability of dynamics of socio-economic development). New factors and circumstances undoubtedly incorporate the global energy transition “agenda”—striving to reduce carbon emissions, increasing the role and importance of alternative energy sources, and in general, increasing attention to environmental issues. The movement in this direction will be accompanied by increased competition in the markets for energy, investment, and human capital. Not all sources of energy will be able to maintain their efficiency in the new system of coordinates, which includes not only the return on investment associated with the production of energy resources, but also the minimization of the “carbon footprint” of their development. Non-compliance with these conditions will inevitably lead to an outflow of resources in the innovation sector: not only finances, but also people—vehicles and generators of new ideas and practices. It is impossible to solve such problems within the framework of the previously formed approaches of stronger raw material orientation of functioning and development of the economy. An important role here will be played by increasing the scientific and technical level of the fuel and energy complex—first of all, through the development of modern machine-building and production-service industries. This will promote the creation of high-tech jobs, as well as the growth of the production and educational potential of Eastern Russia and, as an indisputable consequence, the mitigation of the unfavorable demographic situation. At present Russia lags behind advanced countries in the depth of resource processing and energy efficiency. For example, exporting almost half of its total energy production, the country produces more energy per capita than most countries in the world, five times ahead of the global average and three times ahead of the average for OECD countries. At the same time, the energy intensity of the Russian

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economy is also noticeably higher than the global and OECD averages—by threequarters and twice as much, respectively. It is also higher than in Canada, Norway, Finland, and the United States, which have the most energy-intensive economies among the OECD countries. The reasons include not only the harsh climate, continental location, and long distances for transporting goods, but also the traditionally dominant focus on the implementation of unified solutions that took little account of regional specifics of both obtaining energy resources and their use (Suslov, ). The electricity intensity of production in Russia is 13% higher than in the United States, and compared to the average level in OECD countries—by 35%. At the same time, there is pronounced “energy poverty” in many regions, which are themselves leading producers of electricity. In general, Russia uses significantly less energy per capita than the United States, as well as countries with colder climates and energy-intensive structures—such as Canada, Finland, Norway, and Sweden. Local power and heat supply systems based on fossil fuel combustion are among the key sources of carbon emissions in cities and settlements in Eastern Russia. At the same time, for example, grid-based heating systems are widespread in a number of northern countries. As a result, the energy-intensive structure of the economy and inefficient use of energy in Russia leads to high emissions of pollutants. Carbon dioxide emissions per unit of GDP and energy intensity of GDP here are much higher than in most economically developed countries of the world (Table 1). The fuel and energy complex of the “western part” East of Russia, Siberia, is the most important part of the country’s energy sector and is localized in the Siberian Federal District as well as the Tyumen Region, which is administratively part of the Urals Federal District. It produces 89% of all natural gas, 80% of coal, and about 64% of the country’s oil (Table 2). If we add data on the Far East, the Asian part of Russia produces 94% of all gaseous fuel, 96% of coal, and 70% of the country’s liquid fuel. Siberian hydropower plants provide more than half of all hydropower produced in Russia. For several decades, the region’s energy resources have filled the country’s federal budget and provided most of the foreign exchange earnings from foreign trade. Currently, up to 40% of Siberia’s gas and coal are exported, and about 70% of Siberia’s oil, including oil products exported from the country. The scale of fuel and energy complex of the East of Russia (and first of all Siberia) significantly exceeds not only the demand of this macro region, but the country as a whole. This began in the 1960s—first oil, and then natural gas were exported in significant volumes. The main destination for many years was the countries of Europe—first Eastern and then Western Europe. It would be worthwhile recalling the precedent of “anticipatory” consideration of hydrocarbon supplies, primarily natural gas, both eastward (Japan) and far westward (the United States) in the early to mid-1970s. Those were projects to build two liquefied natural gas plants: in Murmansk (the North Star project with subsequent supply of LNG to the East Coast of the United States) and in Okhotsk with a corresponding system of gas pipelines (from the central regions of Yakutia and the newly discovered Urengoy gas condensate field) (Cowan, 1973; Memorandum from Under Secretary of State for Economic Affairs (Casey) to Acting Secretary of State Rush, 1973). These projects were never destined to come to fruition for reasons similar to those

27,1

71,5

79,7

78,6

107,6

85,5

45,8

World

OECD

Canada

Finland

Norway

Sweden

Russia

154,8

53,6

588,6

53,7

215,0

50,7

28,6

FERC output per capita

77,1

71,7

78,4

90,7

117,7

60,5

27,6

FERC consumption per capita

165,4

83,8

72,5

114,9

147,6

81,3

101,7

Energy intensity of GDP

Source: Energy data: International Energy Agency, Population, GDP: World Bank

GDP PPP per capita

Country

52,8

101,5

183,9

121,0

117,7

62,3

24,9

E/power per capita

113,3

118,7

170,1

153,3

147,6

83,7

91,7

Electricity intensity of GDP

73,1

22,5

45,2

53,0

98,2

59,5

29,4

CO2 emissions per capita

Table 1 Energy output, consumption, and carbon dioxide emissions in the global economy, as a % of U.S. levels in 2018

156,8

26,3

41,8

67,1

123,1

80,0

108,2

CO2 emissions per GDP

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Table 2 FER production by Federal Districts of Russia in 2019 Federal district

Coal, million tons

Russia as a whole 439,4

Oil, million tons

Gas, billion m3

Electrical power, billion kWh

Hydropower, billion kWh

561,0

739,4

1121,5

196,5

Central

0,0

0,2

0,0

227,2

3,3

Northwest

4,7

31,2

4,9

126,7

12,1

North-Caucasian

5,5

14,6

19,6

83,3

13,3

Southern

0,0

1,1

0,4

20,7

6,5

Privolzhsky

0,0

119,2

21,7

188,2

30,5

Ural

0,0

309,3

639,1

200,5

0,0

349,7

51,5

17,5

206,2

111,9

73,8

34,1

36,2

68,6

18,8

Siberian Far Eastern

Source Rosstat data, EMISS

of today—due to the need to comply with “human rights” requirements, and also due to geopolitical and financial circumstances. The Okhotsk LNG terminal project is currently being proposed by A-Property LLC (A-Property is planning an LNG project with a resource base in Yakutia & construction of a plant off the coast of the Sea of Okhotsk, 2020). Nevertheless, this example shows the long time ago perceived role that the fuel and energy complex of Eastern Russia (and primarily Siberia) can potentially play as a highly flexible supplier of energy resources to the West and East. In the twenty-first century, the factor of “flexible” geography of export supplies has become crucially important in determining the directions of energy development in Russia and its Asian part. In the context of emerging geopolitical challenges and expected trends in global demand for energy resources, the priority for Russia is not so much the quantitative growth of energy production as the formation of a new system of interconnections “FEC—national economy” with the existing and emerging export opportunities in order to create new high-tech areas of economic development (primarily, the creation of high-tech jobs in the East of Russia). According to the Energy Strategy of Russia for the period up to 2035 (2020), domestic consumption of energy resources in the country may rise by 12–27%, with solid fuel consumption at best by 7%, and gas consumption by 17%, but by no more than a quarter; primary oil refining volumes may drop by 20–25%, which is tied in with increasing oil refining depth. As we noted above, the modern economy (increasingly aimed at DSG priorities, including ESG) along with the fuel and energy complex has an impact on structural changes in the economy of the country and its regions, as well as, in general, on the formation of socio-economic and low-carbon development in line with the modern system of priorities.

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Unfortunately, we have to admit that very little attention has been paid to these issues of development of the fuel and energy complex and the country and the East of Russia so far. The main emphasis is placed on creating the infrastructure for “entering” the markets of Southeast Asia and the Asia–Pacific region—this applies both to oil and natural gas (including products of primary hydrocarbon processing— see below about the Amur GPP, the Amur Gas Chemical Complex and the discussion of the Eastern Gas Chemical Complex project) and to coal (the so-called “Eastern Polygon”). As our colleagues from the Institute of Economic Research of the Far Eastern Branch of the Russian Academy of Sciences point out, “In 2000–2005, …., the task of combining the infrastructure function of the Far East with the task of forming a ‘new industrial base’ in the form of creating clusters of high-tech industries and services in the southern part of the region. And the solution to this problem, as well as the implementation of the concept of new industrialization in general, was presented in the form of creating industrial and service arcs in the southern part of the Far East. …However, by the end of the first decade of the twenty-first century the situation had drastically changed. The idea of forming ‘intercepting border arcs’ is being implemented in the northeastern provinces of China bordering the Russian Far East, where a special program for modernizing the old industrial base is in place” (Russian Far East…, 2017, p. 95). The assessment of such an export-dominant orientation of the development of the fuel and energy complex of both the country and the East of Russia is overly categorical, but essentially justified: “As part of its innovation development strategy, China is investing heavily in the development of new energy technologies—such as clean coal, CO2 capture and burial systems, batteries and other forms of energy storage, super power distribution networks, advanced materials, and artificial intelligence and data processing in the energy sector. China is developing innovative energy industries, in part because it recognizes its high dependence on imported oil and gas and the associated strategic vulnerability. In terms of the energy transition, China has an economic scale advantage that no other country has had since the United States after World War II. China dreams of becoming the leader of a new global economy in an era that will come after the abandonment of oil and gas. Russia, on the contrary, wants the era of hydrocarbons to last as long as possible” (Chou, 2020). It should be noted that in China, following the above-mentioned path begins with a macroeconomic analysis and subsequent forecast of the volume and dynamics of FEC products—with a breakdown into the actual production and transport (transmission) of primary energy, and the development of works and services of scientific and production nature (World & China Energy Outlook, 2050). An important role is also played by the active participation of regions in these processes (Coase & Wang, 2016). It is due to the above-mentioned reasons, in our opinion, that the implementation of the idea of creating “industrial-service arcs” both in the fuel and energy complex of Eastern Russia and in the fuel and energy complex of the country as a whole is still “waiting for its time.“ The answer to the question about the reasons lies both on the theoretical plane—determining the ways of formation and development of institutional systems in the FEC (taking into account historical traditions, previously

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created main assets, composition, and specifics of interaction between the main economic agents), and the practical plane of energy policy (not only in terms of composition and structure of strategic documents, but also the role and place of different levels of the state regulation hierarchy in addressing the above tasks).

3 Questions of Methodology The authors believe that the theoretical foundations for solving the above problems are of a general nature. Namely, the key role is played by the basic provisions of the system approach and closely related generalizations of modern institutional theory. In applying these provisions and approaches it is also necessary to take into account the peculiarities and specific features of those production and economic systems, which were created earlier and/or reflect the characteristics of a particular country in a particular period of time. The latter is important not so much from the point of view of ensuring adherence to the previously chosen path, but rather from the point of understanding and taking into account the starting conditions for the transition of the FEC to a new quality (in this case, the possibilities of following the modern guidelines of its development) (Kryukov, 1998).

4 Institutional Basis—Tradition Plus Vision of the Direction The experience of various countries is diverse and multidimensional. They differ both in terms of directions and dynamics of realized changes in the institutional system (the key “fork” is the issue of state participation in ownership of energy assets), and in terms of the role in these changes of different levels of the state hierarchy—the country as a whole and its individual regions (the key “fork” is the role and place of regions in determining the conditions for using the energy potential of the territory and in receiving part of the revenues of rental nature). France is without a doubt among the slowest and most gradual in the development of its fuel and energy complex. Over the past decades, the country has been gradually adapting its energy policy to international environmental requirements and the resulting obligations (Andriosopoulosa & Silvestre, 2017). At the same time, the issues of scientific and production support are addressed within the framework of previously adopted approaches—while preserving the active role of the state both as a direct participant and as a legislator. The US energy policy is quite distinctive, especially in connection with the need to address both the energy transition and the development of the coal industry. A very significant role in determining the steps in the energy transition (reduction of greenhouse gas emissions, as well as in reducing the role of coal generation) is

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played by individual states. This raises the problem of inertia in the implementation of decisions made and their coordination at the federal and individual state levels (Roemer & Haggert, 2021). China’s energy policy is also unique. “…From 1978 to 2018, China’s government agencies gradually built up their experience and skills in managing the national energy market, as well as their experience in coordinating in this area… To address the emerging challenges, the Chinese government issued a series of documents, including local resource utilization, scientific and technical support, renewable energy subsidies and regulation” (Kong et al., 2020). A distinctive feature of the approach to form and implement the main provisions of China’s energy policy can be attributed to the insignificant role of the legislative support for the proposed and implemented measures. As a rule, it is based on the prescriptions, instructions, and directives of various levels of government. The fuel and energy complex, as noted above, is still largely based on the extraction of non-renewable energy resources. Therefore, one of the most important directions of formation and development of institutional systems related to their extraction and subsequent use is to create economic, financial, and scientific and technological conditions for the establishment and development of not only the production of alternative energy sources, but also a wide range of technologies—from information to new materials and fundamentally new technologies of the widest purpose. This process, quite obviously, cannot be carried out only on the basis of and within the framework of economic preferences of companies of various levels. This is due to both the duration and high risks of exploratory projects and new solutions. Innovations play a significant role, but, as a rule, they are developed on the basis of previously obtained fundamental knowledge and guidelines. Such issues are components of the process of formation of the institutional system in the energy field. In a number of countries at the legislative level the conditions and frameworks of ensuring the interrelation in the line “FEC—economy of the country (region)” are defined. First of all, it concerns the financial and economic results of production and development of traditional energy resources for obtaining new knowledge and development of new technologies (including the development of technologies and use of alternative energy resources). Each of the countries noted above (France, the United States, China) has its own peculiarities in this area. Thus, in France (due to the lack on its territory of any significant reserves of traditional fuel and energy resources) the main role in transforming the financial and economic results of power generation into developing the use of alternative energy resources is played by the state and companies with its participation. In the United States, a significant role is assigned to businesses and the regional level. The latter is due to circumstances such as “…the presence of strong social capital with diverse stakeholder and organizational linkages, with the ability of different communities to learn and organize themselves to solve problems, with shared governance systems and strong institutions that encourage cooperation and experimentation” (Roemer & Haggert, 2021). In China, the solutions implemented in the fuel and energy sector are based both on the above-mentioned regulations and guidelines and on the state policy of redistributing the financial and economic results

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from the production and use of traditional energy resources in favor of creating new alternative energy technologies (in the form of subsidizing the higher costs of their production). This approach takes place both at the level of the country as a whole, and in individual provinces and projects. One of the most interesting approaches, in our opinion, in forming a system of steps and measures toward the implementation of SDG-priorities is Norway. It has managed to form and implement a specialized institutional system (by Oran Young’s definition—the resource regime Young, 1982), which has allowed not only to form the unique domestic competence of hydrocarbon prospecting and production on the shelf, but also to create the basis for a successful transition to a low-carbon model of functioning and development of the oil and gas sector and the FEC as a whole, as well as forming the basis for the long-term socio-economic stability. It is based not on the vertical redistribution of financial and economic effects in order to finance certain activities through the state budget system, but on the formation of targeted scientific and technological regulation at the level of individual projects for the development and use of hydrocarbon resources on the national shelf. At the same time, the priorities also include environmental issues and socio-economic benefits for the country as a whole. The state management bodies define together with the subsoil user companies both production-technological and scientific-technical conditions of subsoil use. This system is based not on the prescriptions and guidelines of the higher state management bodies, but on the mutual obligations of the state and consortiums of companies-owners of the rights to use subsoil areas on the shelf. Obligations have the force of a contract and can be challenged in court. It is important that such a resource regime implies not only the mutual responsibility of the parties—the state and business, but also “forces” various companies (consortium members) to interact and cooperate in the framework of individual “lower level” projects. This ensures not only the reduction of risks of each of the participants, but also enables the flow of knowledge and competencies between them. The results are impressive: Norwegian service companies are among the world leaders in offshore production and successfully develop modern technologies in many industries and spheres of human activity (such as information technologies, shipbuilding, etc.). It is quite understandable that the creation of the above mentioned “industrialservice arcs” in Norway was a natural consequence of such an approach. Such cities as Stavanger, Kristiansand, Trondheim, and Bodo are places where hundreds of hightech companies are concentrated and where major world-class scientific, engineering, and educational centers operate (in essence, not in “self-definition”). It is important that the discussion, formation, and promotion of different approaches to the interaction of the fuel and energy sector and the economy of the country was conducted in the context of social value creation and development. For example, the Norwegian Government’s June 2021 report to Parliament (White Paper) entitled “Putting Energy to Work” (Government publishes White Paper on long term value creation from Norway’s energy resources, 2021) is based on developing and expanding the role of energy resource development and use in shaping a new system of values. The report provides direction and approaches on how Norway

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can use its energy resources for sustainable eco-economic growth and job creation. The document is one of the fragments of the government’s broad climate action plan, detailing the impact of renewable energy and the new energy mix on expanding the use of electricity and phasing out fossil energy sources (the country has a widespread use of electric room heating). In general, the Norwegian energy policy consists of three main blocks: climate and environment; energy security and reducing dependence on imports; industrial and economic development. The success of the experience of the above countries is based on the systematic approach to the formation of a modern system of values in addressing the problems of fuel and energy sector development, as well as the vision not only (and not so much) of the energy (or resource) component itself, but a wide range of socioeconomic, environmental and climatic consequences and results. An important role is also played by taking into account intra-country regional specifics. The above features (described briefly) allow us to consider from a broader perspective both the current problems of the fuel and energy complex in Eastern Russia and the challenges to be addressed.

5 Main Priority of the Russian FEC—Production Volumes Plus Taxes By now, Russia has developed a large list of documents defining the role and place of the fuel and energy complex in solving the tasks of socio-economic development of the country and its individual regions. These include the Energy Strategy of the Russian Federation for the Period up to 2035 (2020), Strategy for Scientific and Technological Development of the Russian Federation (2016), Strategy for the Development of the Mineral Resource Base of the Russian Federation until 2035 (2018), Program of development of the coal industry of Russia for the period until 2030 (2020) and a number of other important and necessary “upper” level documents. They have a certain legislative basis, e.g. in the form of the RF Law “On Subsoil” (1992), the RF Law “On Electricity” (2003), etc. In addition to sectoral documents of strategic nature, there are several documents reflecting (aimed at) detailing the general approaches and provisions in relation to individual regions and macro-regions. For example, Strategy of Spatial Development of the Russian Federation for the period up to 2025 has been developed and approved. (2019), Strategy of socio-economic development of Siberia (2010), “National program of socio-economic development of the Far East…” (2020), and many others. All of the mentioned documents reflect, to a greater or lesser extent, the issues of development of the fuel and energy complex and its most important components. In our view, the main problem areas of these documents, which significantly affect the solution of the range of issues we are considering (in line with the movement toward the SDG priorities), should include their exclusive focus on preferences, understanding, and vision from the corporate level. The main emphasis is placed

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on the implementation of infrastructure or production projects and the creation of preferential tax conditions within the boundaries of certain specially designated territories (TOROs—Territories of Advanced Development, SEZs— Special Economic Zones). The issues of formation and development of regional and interregional infrastructure are supposed to be solved within the framework of special projects, financed, among other things, by the federal “development institutions.“ The issues of the formation of cooperative ties (both in scientific and production issues and in the organization of the production of more complex products with higher added value in the East of Russia) receive far less than the highest priority. The main reason is the “lack of an internal market.” At the same time, its formation and development are not only and not so much a question of individual companies as of the state policy and the vision of the directions of socio-economic development of the Russian East and its regions. Such an important tool as “forcing” to cooperation and cooperation of energy producing companies in determining the forms of state support and granting the rights to use subsoil areas in Russia is hardly used. As we noted earlier (Kryukov, 2020), the specialized innovation system in the sphere of subsoil use (aimed at forming and strengthening the role of socio-economic values) is based on such circumstances as: • administrative-legal character of relations between the state and the subsoil user company (not civil-legal, as, for example, in Norway). This entails the impossibility to discuss the mutual obligations and responsibilities of both parties, which significantly increases the risks of the subsoil user company; • granting of the license for this or that site of subsoils on the basis of a principle “one site—one company-subsoil user” (that considerably “narrows” the area of knowledge transfer) and also leads to the appearance of huge “fiefdom” territories controlled by large companies (that allows them to “maneuver” resources and move from one field to another during “optimization” of a current cost level). As a result, there remains a low degree of extraction of commercial oil reserves and selective development of deposits in the case of solid minerals; • determination of production levels and rates of mineral extraction, based on the approaches and practices proven in the past; adherence of the subsoil user companies to the conservative scenarios of field development and development (due to the lack of conditions for taking the risk associated with the application of new and innovative solutions and technologies); • lack of requirements and conditions associated with the development of domestic scientific and technological and human resource potential in the implementation of certain projects in the mineral sector (On Subsoil, 1992, Art. 13.1). A direct consequence of the above approach is the priority orientation of companies implementing projects in the FEC toward supplying energy and energy resources for export, while other issues (related to addressing socio-economic development in connection with a possible change in value orientations) receive low priority.

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6 Fuel and Energy Complex of Eastern Russia—“Economies of Scale” 6.1 Energy The dynamics of indicators characterizing the development of the power industry in Russia and Siberia is shown in Tables 3 and 4. The power demand of the Siberian Federal District (SFD) is met by the electric power plants of the Siberian UES, as well as small communal and departmental power plants (mostly diesel-fueled), operating in isolated power nodes. The total installed capacity of UES Siberia power plants is 52.1 GW (21.2% of total Russian capacity). SFD, being surplus in capacity, is deficient in electric power. The total generation volume in the Siberian UES in 2020 is 207.0 billion kWh (19.8% of the total volume in Russia). Starting from 2000, the SFD has been supplied with power from Kazakhstan and the Urals Federal District. The electric power industry of the Siberian UES is represented by three types of generation: thermal, hydraulic, and solar. There are no nuclear power plants in Table 3 Power generation capacity structure by types of generation in 2000–2020, GW Type

2000

2010

2015

2019

2020

UES of Russia

213,0

215,0

239,8

246,3

245,3

44,2

44,9

47,9

49,9

49,9

incl. HPP NPP

21,2

24,1

27,1

30,3

29,4

TPP

148,5

146,0

160,2

164,6

163,3

OPP of Siberia

50,1

49,9

51,8

52,1

52,1

incl. HPP

23,2

22,3

25,3

25,3

25,3

TPP

26,9

27,4

26,5

26,6

26,5

Source Rosstat data, EMISS database, RAO UES reports, ODC

Table 4 Electric power generation structure by types of generation in 2000–2020, billion kWh Type

2000

2010

2015

2019

2020

UES of Russia

877,8

1038,0

1026,9

1080,6

1047,0

incl. HPP

165,0

168,4

160,2

190,3

207,4

NPP

131,0

170,5

195,3

208,8

215,7

TPP

580,0

687,1

671,4

679,9

620,6

OPP of Siberia

195,2

210,2

201,2

208,7

207,0

92,8

91,4

88,3

107,8

117,7

102,4

117,5

103,4

100,8

89,0

incl. HPP TPP

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the region. As of 01.01.2021 the share of the UES with the generating capacity of 300,2 MW is 0,6% in the structure of the installed capacity of the energy system. Specific features of the electric power industry of the Unified Energy System of Siberia are the dominating share of hydropower plants in the structure of generating capacities (48,6% against 19,8% at the national average) and dominating share of coal in the structure of fuel consumption at the thermal power plants (84% against 22–24% of the all-Russian share). With a significant role of HPPs in the structure of installed capacity, management of operation mode of Siberian UPS is complicated by natural instability of annual flow of Angara-Yenisei cascade rivers and inconstancy of water content of rivers. The past reform of the industry, which focused on solving the above-mentioned industry problems (such as covering prospective power deficit with simultaneous capacity renewal), has led to the preservation and multiplication of some of them: • low innovative activity, leading to a growing technological lag from the world level; • high depreciation and low efficiency of equipment (specific fuel consumption indicators are 20% higher than in developed countries) • high dependence on imported equipment and external production and service and engineering services; • underdevelopment of power grid facilities (resulting in underutilized capacities and 1.5–2 times higher losses in power grids), lagging behind small and distributed power generation, imbalance in fuel consumption by TPPs. All-Russian problems are urgent and often more acute for the SFD power industry as well: • a significant share of its installed capacity cannot be used to cover load schedules, as it is characterized by a significant uneven distribution of generating capacities with insufficient development of power grids; • the use of outdated coal combustion technologies at TPPs causes a low level of efficiency (only 38%, whereas abroad it is 43–46% at coal-fired steam-turbine units) and an increased environmental load. At the same time, the trends determining the development of the energy sector are changing quite significantly. Not only the demand for electric power grows, but the requirements for its quality also change—first of all, its availability in case of changes in demand. This presupposes, in particular, both availability of a certain capacity reserve, and consideration of qualitative features of changes of both current and perspective dynamics of demand. Lack of consideration of this circumstance (along with the above-mentioned value benchmarks) led to the fact that the development of the General scheme with investment projects of electric power industry of Russia and Siberia focused only on the forced growth of generation (General Scheme…, 2008). Alas, in reality there was no significant growth in electricity consumption. Actual electricity consumption in the Siberian UES was only 209.4 billion kWh in 2020. In 2020, there was a record drop of 2.4% in the volume of electricity generation in the Russian energy system.

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Stagnation of demand for electric power brings up other issues of development of the electric power industry. Not only the issues of power supply reliability, but also the environment and those closely related to the reduction of greenhouse gas emissions (SDG and ESG) are on the agenda. At the same time, many industry experts still believe that coal “has no economically adequate alternatives” in Siberia and the Far East (Forecast of the development of the energy world & Russia for the period up to, 2040). The point of view that gas in this vast territory can be considered as an alternative to coal only in regions where the Unified Gas Transmission System is being developed does not fully correspond to the realities of the energy transition. The development, for example, of local gas supply systems based on LNG in a number of places may well provide a substitute for coal (primarily in coastal Arctic settlements—see below). In general, the development of electricity generation based on coal fuel can be carried out in the following main directions: (a) reconstruction of old plants to improve economic, technological, and environmental efficiency; (b) construction of new coal-fired power plants based on existing technologies; (c) construction of coal-fired power plants using new energy efficient and environmentally friendly coal combustion technologies; (d) development of small-scale energy, especially co and trigeneration with maximum replacement of boiler houses with mini-cogeneration (coal-based) plants. In connection with the growing trends of decarbonization, an alternative to the retiring capacities of coal-fired plants could be both the aforementioned LNG-based plants and HPPs. The latter is especially relevant in Eastern Siberia—hydropower resources are currently only 20% utilized, and the undeveloped potential is more than 150 billion kWh. In the East of Russia, especially in remote areas, power can also be supplied by small capacity nuclear power plants (Dyatel, 2020). The advantage of nuclear power plants is the ability to produce energy with minimal emissions of harmful substances into the environment. It is also important that in this case there is no need to supply fuel to the nuclear power plant and thus create transport infrastructure. It is also possible to consider the possibility of producing equipment for small-size NPPs, including in the production and industrial centers of Eastern Russia (which will contribute to the creation of high-tech jobs and the retention of qualified personnel). The process of development of the energy sector in the framework of the modern system of values is characterized by a change in its structure—strengthening the role of small and medium-sized energy. The energy sector, both in Russia as a whole and in its east, is still focused on large facilities. For example, the share of large thermal and hydraulic power plants in the Siberian energy system is not just large—it is huge. At the same time, increasing local generation (both in conventional and alternative energy) is the most effective way to meet the challenges of the modern economy. We share the view that “…the most realistic model seems to be a consistent reasonable combination of large-scale generation and distributed energy, which will ensure gradual adaptation of the unified energy system (UES) of the country to the ‘energy transition’” (Distributed Energy in Russia, 2018). The complementary nature

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of these processes is noted, for example, in the work of colleagues from the Institute of Energy Systems named after L. A. Melentiev (Stennikov & Voropay, 2017). Modern trends in the development of business processes (including digitalization), the increasing role of innovation, and the accelerated development of the human capital economy are also responsible for a more serious increase in electricity consumption—by 24–36%. In the East of Russia, the response to these trends in recent years has been mainly associated with exports to the Asia–Pacific Region and South-East Asia. There is no shortage of electricity export projects—from the formation of the North Asian market (Bradbrook, 2002) to the construction of plants of colossal capacity (Scheme of Territorial Planning of the Russian Federation in the Field of Energy. Decree of the Government of the Russian Federation No., 2013). This approach, based on economies of scale, is ubiquitous in the case of the fuel and energy complex of Eastern Russia: not only in the electricity sector, but also in the coal industry, and in the oil and gas sector—from production and to the supply of natural gas, oil and large-tonnage petrochemical products (as a rule, the initial energy-intensive conversion processes). The continental part of eastern Russia does not have very favorable conditions for the development of RES: in Siberia the winter period lasts about seven months. During a long winter in the cities of the region coal-fired generation provides not only electricity production, but also works in cogeneration mode: simultaneous generation of electricity and heat. The use of coal not only creates problems (such as greenhouse gas emissions), but also has certain advantages: coal allows to solve the problem of heat supply in different ways—it can be stove or central heating. Coal can be stored and consumed during the winter period. Nevertheless, the use of renewable energy— wind and solar—in remote regions is justified and logical. RES along with modern multi-fuel mini-stations can play a prominent role in the modernization of local power systems of the North and the Arctic. The basis of the energy sector built on the use of solid fuels can be, for example, energy technological enterprises with comprehensive processing of fuel and obtaining a wide range of products with attractive marketable properties and high added value. More than five hundred products are produced from coal in the world: gasoline, plastics, motor oils, lubricants, chemicals, etc. The development and application of technologies for the integrated and deep utilization of solid fuels has every reason to become among the priorities of technological development (for example, the expansion of the scope of application of “Thermocox” type technology—in particular, the production of sorbents from brown coal) (LLC Thermocox—https://termok oks.ru). Improving the sustainability of power supply in the East of Russia and, above all, in Siberia involves the development of electric grid infrastructure. The Russian Ministry of Energy noted in 2017 that the installed capacity of generators in the Siberian UES was not used to its full potential: for example, TPPs were only 46.45%, HPPs were 42.41%, and SESs were 14.2% (Report on Functioning…, 2020). From the point of view of the trends we noted in the FEC at the beginning of the article—the impact of the new system of guidelines on its development—the dynamics of electric power industry of Eastern Russia is still largely determined

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by those inertial processes, which were formed in previous years. Influence of new processes and new approaches so far is more than modest for reasons, first of all, of institutional and structural character. There is a predominance of approaches based on the “economy of scale” and the related predominance of large single producers and consumers. At the same time, the focus on the market of one country—China— does little to overcome this trend and increase the flexibility of the energy system of Eastern Russia.

6.2 The Coal Industry in Russia and Asian Russia Coal in eastern Russia is available in abundance, safe for transportation and storage, and relatively inexpensive fuel. Almost 80% of the predicted coal resources are located in Siberia, including more than 70% in the Kuznetsk, Kansk-Achinsk, and Tunguska coal basins. In the European part of the country, where the Pechora, Donetsk, and Moskow basins are located, there are a little less than 9% of Russia’s explored reserves, and in the Far East—about 10%. Since 2014, coal consumption in the world has been decreasing. However, the reduction is not universal, and a number of countries (primarily in the Asia–Pacific region) are increasing their use. The growth of coal consumption, primarily in the electricity sector, is a priority, for example, for India and some ASEAN countries, while China has taken a course to gradually reduce it in the energy sector; for such countries as Japan, the Republic of Korea and Taiwan the forecast of coal consumption is characterized by uncertainty. Russia’s coal industry has seen steady growth in recent years, supported by an increase in exports: on average, production grew annually by 2.9% and export shipments by 9.6%. In 2017, export shipments exceeded domestic shipments for the first time in history. Total volume rose from 37 million tons in 2000 to 210 million tons in 2018. Over a decade, Russia’s share of global coal trade has grown from 7 to 14% (Table 5). The current situation at the world market turned Russian coal-makers to the East where there is a high demand for this kind of fuel and raw materials and, correspondingly, an acceptable price level. In 2010, Asia–Pacific countries became the main destination for Russian coal exports. The supplies of coking coal will be most stable (at least in the mid-term perspective) in conditions of the energy transition and the world economy becoming more and more oriented toward SDG priorities. Thus, according to the results of the first half of 2021, Sibantratsit, the leader in mining and export of anthracite, increased its sales of coal to India by 140% against the last year’s level. In 2021, the company’s production increased by 22% to 10.4 million tons, of which anthracite accounted for 6.3 million tons and metallurgical T-grade coal for 4.1 million tons (Zainullin, 2017). In Russia, the largest coal-exporting companies are: SUEK JSC, Kuzbassrazrezugol Management Company JSC, SDS-Ugol Holding Company JSC, Kuzbass

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Table 5 Dynamics and structure of Russian coal supplies in 2020–2018, million tons Indicator Share of exports in global trade (%)

2000

2005

2010

2015

2016

6,8

9,9

10,5

11,2

12,7

2017

2018

13,5 14,0

Coal exports

37,5

82,5 116,4 151,4 162,3 186,3 193,2–210,1

Share of export in the total supply structure (%)

15,3

29,5

Supply of Russian coals to the domestic market

39,3

47,1

49,2

52,4 53,8

207,5 197,5 180,1 170,0 167,3 169,2 180,7

Share of domestic market in the overall supply structure (%)

84,7

70,5

60,7

52,9

50,8

47,6 46,2

Import

25,6

21,1

29,6

22,9

24,6

27,1 25,3

Fuel Company PJSC, Mechel Mining JSC, etc., (Fig. 1). They are also the largest exporters of steam coals. The main suppliers of coking coals for export are: Yakutugol Holding Company JSC (Mechel Mining JSC), SUEK-Kuzbass JSC, Raspadskaya Coal Company LLC (EVRAZ), Kuzbassrazrezugol Management Company JSC (UGMK), etc. Among the promising export-oriented projects of coal mining development, implemented until 2035, we should highlight Kuzbass, Tyva, Khabarovsk Krai, and the Republic of Sakha (Yakutia). The export direction of coal production is developing most actively (Long-term Program for the Development of the Coal Industry of Russia until, 2035). Both the implementation of projects to develop coal production 50

90

44 79

40

66.7

30 20

40

57.7 27.9 20.4

65

29.5 8.2

7.9

80 70

64.3

43.8

19 10.5

10

Coal exports, million tons

78.7

7.1 3.4

26 2.9

60 Share of 50 exports in production, % 40 (right scale) 30.130 21.6 20 17 2.7 2.4 1.7 10

0

Fig. 1 The largest coal exporting companies in the Russian Federation in 2018

0

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in southern Yakutia and the development of railway infrastructure to access seaports (the so-called Eastern polygon) are at the core (Russia will build an analogue of the BAM to export coal from the record field, 2021). Both the state (military builders are involved, and funds are allocated from the National Welfare Fund) and private business are actively involved in the implementation of these projects. In particular, the construction of a private third branch of BAM—from the Elga deposit owned by A-Property to the Sea of Okhotsk—is being discussed (Novikova, 2021). The fundamental problem associated with the intensive development of the Eastern polygon (the development of coal mining in southern Yakutia) and the increase in exports of coal in the eastern direction is to ensure the connection of its export vector to the solution of domestic socio-economic and environmental problems—both in the East and in the country as a whole. One cannot fully agree with the argument that it is the coal industry that provides a significant number of jobs, for example, in Kuzbass. Rather, the employment problems in Kuzbass are related not so much to the role of the coal industry in the region’s economy as to the lack of a “feedback” from this industry toward the region’s economy and social sphere (Kryukov et al., 2020a). That is why in mid-2021 the Ministry of Economic Development of the Russian Federation prepared a draft decree of the Russian government obliging coal companies of Kuzbass that wish to increase supplies to Asia to give part of their export revenues to the region’s economy (Milkin & Potapova, 2021). One of the options for their use is the implementation of projects aimed at the structural restructuring of the economy of this depressed region. The reason for the depressed state of the economy of Kuzbass (as well as the coal basins of Krasnoyarsk Krai, Khakassia, Irkutsk Oblast, and Primorsky Krai) is a sharp increase in the efficiency of the coal industry and further significant release of employed workers. At the same time, synchronization of the development of the coal industry with the creation of new jobs and new areas of employment was not considered. One of the unusual solutions being implemented is the creation in Kuzbass of an “Interregional training center for the construction industry of the regions of Siberia and the Far East” (Kuzbass will create a vocational training center for builders of Siberia & the Far East, 2020) to promote the implementation of large-scale construction projects (primarily in the territory of the “Eastern polygon”). The consequence, obviously, may be an increase in the outflow of workers and population from Kuzbass. The impact of the new system of priorities on the coal industry in the East of the country is very complex and contradictory. On the one hand, new projects for the development of coal deposits of better quality with greater export potential are being implemented, which are poorly synchronized with the adaptation of the “old” coal mining areas to solve the emerging acute social problems of employment and elimination of the earlier environmental damage. On the other hand, transport infrastructure, the availability of which can serve as the basis for the development of new energy projects (including RES), and improve living standards (Potayeva, 2021; Russia will build an analogue of the BAM to export coal from the record field, 2021). In this regard, it is necessary to note the experience of the province of Alberta (Canada), which in a similar situation (the presence of huge resources of bituminous

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sands) formed a special trust fund in 1976 with the purpose, among other things, to solve infrastructure problems, as well as the creation and development of new technologies associated with their development and extraction of new sources of energy resources (Murphy & Clemens, 2013). This fund ceased to exist in 1987, but in its place a number of “development institutions” were created to support the development and promotion of new energy-saving and green technologies in the energy sector (Energy & Environment Systems Engineering, 2021; Kaddoura et al., 2020). This fund is based on tax payments to the province of Alberta from the production and sale of oil and other minerals (primarily in foreign markets). In Russia, the formation of such funds out of deductions from the sale of energy resources on the domestic market is hardly possible due to the relatively low prices for them: “Electricity prices in Russia are below the level of foreign countries. The price of electricity for industrial consumers in Russia in 2018 was about 3.41 rubles/kWh., which at the average annual exchange rate of the Central Bank in 2018 corresponds to 5.42 US cents per kWh” (Tariff Campaign in the Electric Power Industry for, 2020). Relatively low prices on the domestic market, combined with the need to make significant investments in the development of new sources of energy resources, as well as the need to purchase many critical types of technological equipment, the government seeks to compensate with tax incentives and preferences (Milkin & Podlinova, 2021). Domestic production of equipment does not meet the demand for its supply. At the same time, it should be noted that there are a number of scientific and industrial centers in the East of the country—the cities of Omsk, Novosibirsk, Krasnoyarsk, Irkutsk, Khabarovsk, Vladivostok. A targeted system of measures and steps to use and develop the potential still available in these centers for the development of relevant production for the needs of the fuel and energy and mining industries is still waiting to be taken.

6.3 Oil According to Russia’s State Mineral Reserves Balance Sheet, Russia’s technologically recoverable oil reserves as of early 2019 were 29.8 billion tons and condensate reserves were 4.1 billion tons. In terms of oil production, Russia ranks third in the world, behind only the United States and Saudi Arabia. Russian oil supplies to the countries of the Asia–Pacific region, first of all to China, take the leading place. This is facilitated by the infrastructure that has been created and is being developed, especially the ESPO (Eastern Siberia-Pacific Ocean) pipeline. Deliveries to China have increased 5.5-fold in ten years, from 12 million tons in 2009 (about 5% of Russian exports) to 67 million tons in 2009. (about 5% of Russian exports) to 67 million tons in 2018. (26%). The Republic of Korea is also a major consumer of Russian oil in the region, with more than 5% of its exports going there. More than 2,000 (2,093 in 2018) oil fields are being developed in Russia. There are also more than 260 sites producing liquid hydrocarbons—gas condensate. The

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depletion of oil reserves exceeds 56%, and about 60% of the current reserves are classified as hard-to-recover (high-viscosity oil, low-permeability, and low-thickness reservoirs, under-gas zones). The Asian part (the Urals, Siberian, and Far Eastern Federal Districts) currently produces about 93% of natural gas and over 70% of oil. The main oil-producing region is the Urals Federal District. Fields of KhantyMansiysk Autonomous Okrug-Ugra are the main suppliers of liquid fuel, they account for about 45% of Russian production. Four fields, unique in their reserves size: Priobskoye and Prirazlomnoye oil fields, Samotlor and Krasnoleninskoye oil and gas condensate fields, altogether produce more than 30% of oil produced in KhMAO-Ugra and almost 14% of Russia’s oil production. Another 6.2% of oil is extracted in the fields of Yamalo-Nenets Autonomous Okrug. In recent years, the region has seen production growth due to the launch of a number of major projects. In the Far Eastern Federal District, major oil deposits are discovered in the Republic of Sakha (Yakutia) and in the Sakhalin region. More than half of the oil is produced at the Talakanskoye field; major facilities also include the Severo-Talakanskoye and Srednebotuobinskoye fields in the Republic of Sakha (Yakutia). The main new projects to develop liquid hydrocarbon feedstock fields are located in the Asian part of Russia (Table 6). The most dynamic projects (companies) in the oil industry of Eastern Russia at present include Vostok-Oil (PJSC Rosneft Oil Company) in the north of the Krasnoyarsk region, as well as in the Krasnoyarsk territory, the Sakha Republic (Yakutia) and the Irkutsk region (production, among others, of liquid hydrocarbons by the Irkutsk Oil Company). Table 6 Major new liquid hydrocarbon field development projects in the Asian part of Russia Field

Region

Planned oil production level, million tons per year

Year of attainment of planned production level

Srednebotuobinskoye

Republic of Sakha (Yakutia)

5

2021

Russkoye

YNAO

6,5

2022

Tagulskoye

Krasnoyarsk region

4,5

2022

Kuyumbinskoye

Krasnoyarsk region

7

2029

Yurubcheno-Tokhomskoye

Krasnoyarsk region

5

2019

Vostochno-Messoyakhskoe

Yamalo-Nenets Autonomous District

6,5

2020

Ignyalinskoye

Irkutsk region

0,9

2024

Pyakyakhinskoye

Yamalo-Nenets Autonomous District

1,5

2025

Imilorskoye

KhMAO-Yugra

2,8

2030

Payakhskaya group

Krasnoyarsk region

26

2030

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The Vostok-Oil project opens for the development of a new oil and gas area in the north of the Krasnoyarsk Krai. Its resource base amounts to 6.2 billion tons of oil, which surpasses many of its qualitative characteristics. It is expected that this project “will be implemented as a project with a low carbon footprint, which is expected to be about 25% of that of new oil fields” (“Rosneft” held a roadshow of the Vostok Oil project for suppliers & contractors, 2021). It is also important that this project is associated with the development of the transport artery of the Northern Sea Route. That is why among the additional effects of its implementation (in terms of “industrial-service arcs”) is the construction of at least 50 ships (most of which will be built at the Zvezdochka shipyard in Primorsky Krai). The activities of the Irkutsk Oil Company (The new economy of Eastern Siberia. Irkutsk company creates oil & gas chemical industry in the region, 2020) are aimed not only at the production of hydrocarbons (from very complex deposits, which have no analogues, for example, in Western Siberia), but also at monetization, primarily of the liquid fractions of gas condensate fields—their subsequent oil and gas chemical processing. For example, a helium plant with a capacity of 10 million liters of liquefied gas per year is being built at the Yaraktinskoye field, and a gas fractionation plant with a capacity of up to 900,000 tons of high-quality ethane feedstock per year is being built in Ust-Kut (Irkutsk…, 2020). The feedstock will be destined for a polymer plant, whose production technology will make it on par with the world’s leading producers. And in terms of production volume it will be the second in Russia. It is also extremely important that the company produces gas condensate fractions of natural gas using the method of re-injection of dry gas into the reservoir. This solves two extremely important problems—maintaining stable production levels of gas condensate fractions and solving the problem of access to the Power of Siberia gas pipeline system (more precisely, the company’s ability to level out its position as a non-priority supplier to PJSC Gazprom’s gas transportation system in the event of access problems). At the same time, the company also acquires the unique practice of methane gas reinjection into the subsoil—which is especially important during the transition to low-carbon development (Mordyushenko & Kozlov, 2017). Such technologies underpin “carbon capture, storage, and utilization (CCS), without which low-carbon (“blue”) hydrogen production from natural gas is impossible” (Beloglazova et al., 2021). Particularly in the implementation of projects in the oil and gas sector two circumstances should be highlighted, on which to a large extent depends the workability of the above-mentioned flexible institutional setting for its development in line with the new system of priorities. Namely, we are talking about stimulating the formation of a flexible organizational structure within the NGS—creating opportunities for companies with different experiences and financial capabilities to participate in the process of field development and development (Shafranik & Kryukov, 2016). Unfortunately, Russia does not pay due attention to this issue. Access to subsoil areas is determined on an auction basis and the corresponding right of use is granted to the winner of the auction. This leads to the unconditional dominance of large and major companies (such as Gazprom, Rosneft, Lukoil, etc., whose share in total hydrocarbon production exceeds 80%) in the Russian NHS. This also means that

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companies with relatively small financial opportunities, but with unique knowledge and experience are deprived of the opportunity to apply them. The above-mentioned Irkutsk Oil Company is probably an exception to this rule because it has assumed the risks of working at such sites, the practice of development and development of which large companies in Russia did not have. Another no less important circumstance is not only the investment attractiveness of projects in the sphere of core business, but also the assistance in the establishment and development of domestic contractors of various kinds of work (from research, equipment manufacturing and production and service operations) (Israel, 2021). Unfortunately, so far, the dominant incentives are in the form of a wide range of tax benefits and preferences, as well as (co)financing by the state of the construction of infrastructure facilities (such as roads, wharves, etc.—usually at the expense of the National Welfare Fund). With a considerable delay in 2021, the RF Ministry of Industry and Trade initiated two projects aimed at expanding the production of domestic equipment for the oil and gas sector (Smertina, 2021; Krasinskaya, 2021; Ministry of Industry & Trade proposes Russian LNG equipment market at 150 billion rubles, 2021). Another important direction—stimulating the process of formation and development of domestic (both generally Russian and regional) contractors—is represented more than modestly. The solution of such issues is based on the stabilization of tax conditions during either the period of return on investment or the selection of a certain level of initial reserves. The most tested approach in the world practice is a concession, i.e. a contract with unchangeable tax conditions between the state and the subsoil user. Contractual relations within the framework of concessions in Russia were widely discussed in the 1990s. The relevant law “On Production Sharing Agreements” (1995) was adopted, on the basis of which concession agreements were concluded with foreign companies in the Nenets District (Kharyaga field) and offshore Sakhalin Island (Sakhalin-1 by ExxonMobil and Sakhalin-2 by Sakhalin Energy). Nevertheless, the practice of such agreements has not received further development and deepening of their filling. It concerns, first of all, the obligations of participating companies to develop both domestic and local production and technological and scientific and personnel potential (see above about the experience of Norway, which is based on the practice of concession agreements). This circumstance is one of the missed opportunities to overcome the delayed formation of a “new industrial base” in the East of Russia and to promote, thus, the creation of a diversified economy, capable of effectively meeting the challenges of the new system of priorities.

6.4 Gas Subsector Russia is second only to the United States in terms of natural gas production, while being the main supplier of gas to the world market. In addition, over the past ten

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48 122

USA 39

Russia Qatar Nigeria

35

107

Australia Malaysia

105

29

Other countries

Fig. 2 Major LNG exporters in 2019, bcm

years the country has become one of the ten largest exporters of liquefied natural gas (LNG) (Fig. 2). In the structure of Russia’s fuel balance, gas accounts for about half of its total production of combustible minerals. The country’s gas production may increase in the future, but currently there is a market for only 0.7 trillion m3 of gas per year.

6.5 USA, Russia, Qatar, Nigeria, Australia, Malaysia, and Other Countries In 2018, 1,983 fields were being developed for gas in Russia, with six of them providing more than 60% of Russian gas production: Urengoyskoye, Zapolyarnoye, Bovanenkovskoye, Yamburgskoye, Yurkharovskoye, and Yuzhno-Russkoye. Russia’s leading free gas producer remains the Urals Federal District, which provided 84% of domestic gas production. The Nadym-Pur-Taz region (NPTR) of the West Siberian oil and gas province in YNAO contains unique oil and gas condensate fields, among which are the most productive in the country: Urengoyskoye, Yamburgskoye, Zapolyarnoye, and Yuzhno-Russkoye. The degree of depletion of the main gas horizon in PNTR is increasing every year: in 2018, the Cenomanian deposits already provided no more than 2/3 of the district’s gas production. The gas reserves of other oil and gas complexes in PNTR are mostly hard to extract. A new gas production center is being formed on the Yamal Peninsula to maintain the volume of gas production in Russia. In 2018, production was carried out at the Bovanenkovo oil and gas condensate field and amounted to 87.4 billion m3 . After reaching the planned capacity of 140 billion m3 of gas, this field will become the leader of Russian gas production. PJSC Gazprom is currently implementing the Eastern Gas Program. At the end of 2019, it began supplying gas to China via the Power of Siberia gas pipeline. The

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pipeline with an annual export capacity of 38 billion m3 runs through the Irkutsk and Amur regions, as well as the Republic of Sakha (Yakutia). The development of natural gas production is a fundamentally important direction not only because it makes it possible to use the advantages of this energy resource in terms of its significant role in reducing greenhouse gas emissions (as compared to the use of coal). It makes it possible to switch to the development and application of new modern technological processes. LNG makes it possible to solve the problem of energy supply to remote consumers, which is especially important in the North and North-East of Russia. Understanding the role and advantages of LNG does not mean their timely and advanced implementation—there is a significant inertia in decision-making and implementation. As we noted above, only in 2021 the Russian Ministry of Industry and Trade initiated the project to support equipment manufacturers “Breakthrough in the LNG market” (Ministry of Industry & Trade proposes Russian LNG equipment market at 150 billion rubles, 2021; Katkov, 2021b). Business, on the other hand, implements certain projects due to their direct economic feasibility. For example, in July 2021 PJSC NOVATEK established a subsidiary company “NOVATEK— LNG Fuel” to build small-scale liquefied natural gas (LNG) plants and sell it in the Russian domestic market (Krasinskaya, 2021). An important initiative of NOVATEK is its negotiations with Mitsui regarding its participation in the creation of ammonia production at the Ob GHK (ammonia is required for hydrogen storage and transportation solutions). It should also be noted that the level of gasification in Russia’s eastern regions is extremely low (Action Plan (“road map”) for the introduction of a socially oriented and economically efficient system of gasification and gas supply to the subjects of the Russian Federation, 2021). One of the main reasons is the long-standing dominance of coal as the main energy resource for heat production and power generation. This is explained by the focus on ensuring the sustainability of the energy system, as well as the need to “solve the social problems of employment” of the population in mining towns and villages. Among the main reasons, in our view, should be attributed to the low priority of environmental issues in previous years (including issues of both reclamation of man-made “landscapes” and greenhouse gas emissions into the atmosphere). In other words, the dominance of the system of priorities inherent in the era of industrialization. New oil and gas projects in the East of Russia—both in hydrocarbon production and processing—have considerable potential in terms of creating a basis for the influence of new value priorities in the development of the FEC, as well as the formation and development of the “industrial-service” arcs we have not once mentioned. Among the breakthrough projects, in terms of movement in this direction, undoubtedly, are the pioneering projects—the Amur GPP and the Amur Gas Chemical Complex (Katkov, 2021a), as well as the discussed project of the Eastern Petrochemical Complex (VNKhK). The importance of the Amur projects lies in their close technological interaction and thereby creating conditions for increasing the added value of manufactured products.

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The development of petrochemicals allows companies to significantly reduce the risks associated with primary energy prices due to greater price stability of the target products. However, at the same time, the emphasis is shifting from largetonnage products of initial redistribution—such as polyolefins (polyethylene and polypropylene)—to higher and, moreover, embedded in certain niche uses and related business models (Kryukov & Shmat, 2021). The movement in the above direction implies not only the creation of large production facilities in the immediate vicinity of the export infrastructure, but also their “incorporation” into the system of socio-economic and production relations with the economy of Eastern Russia. Unfortunately, the latter circumstance is still visible to a very small extent—the production facilities under construction and discussion are export-oriented, and technological processes are based on imported equipment. For example, in the case of the Amur Mining and Chemical Complex “equipment for pyrolysis units and polymer production is manufactured in the world centers of oil and gas chemical engineering. This is due to the uniqueness of the necessary components, as well as the economic efficiency of their use. At the same time, the Amur Mining and Chemical Complex construction project assumes the use of Russian construction materials, components and equipment up to 100% for a whole range of nomenclature at the sites not involving the purchase of unique licensed equipment, as well as in-house production of concrete and reinforced concrete structures” (A pyrolysis column weighing more than 1.5 thousand tons was sent to the Amur Mining & Chemical Complex, 2021). The implementation of these projects so far to a greater extent forms the basis for changing the structure of the economy of Eastern Russia in terms of promoting the formation of domestic high-tech knowledge-intensive companies. At present “…Far Eastern oil refineries mainly work for foreign markets. Thus, the share of exports in the production of oil products at Rosneft’s Komsomolsk refinery in Q1 2021 exceeded the company’s average Figure (70.9% against 62.6%)” (Measures of State Support for Fuel & Energy Complex Enterprises. Their justification & effectiveness, 2021). In our opinion, in line with the movement to a new system of values “the state policy today should be aimed not only and not so much at the development of the chemical industry as such, but rather at the chemicalization of the national economy, the formation of large-scale domestic demand for chemicals. Only in this case it is possible to realize the multifaceted effects of industry development, including multiplicative, both at the level of the national economy and at the level of the economies of individual territories” (Kryukov & Shmat, 2021). In general, in the oil and gas sector there is an extremely slow progress in terms of creating opportunities on the way of formation of conditions and prerequisites of movement in the direction of changing benchmarks. The scheme, as a rule, consists in the implementation of a large unique project, the provision of benefits and preferences of a tax nature, the import of technology and basic equipment. Unfortunately, the connections and directions of integration of such projects into the economy of Eastern Russia are hardly visible.

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6.6 New Products and Production-Technical Services The development of the fuel and energy complex in the framework of the transition is associated not only with the creation and expansion of new technologies for the production and use of “classical” energy resources, as well as strengthening the relationship with the production of oil and gas, coal and chemical products, but also with the organization of fundamentally new industries. The latter undoubtedly include the production of hydrogen, as well as the development of renewable energy sources (RES). “Renewable energy sources (with the exception of large hydroelectric power plants) are currently used insignificantly in the eastern regions. Only geothermal potential is most widely used: the total capacity of Pauzhetskaya, VerkhneMutnovskaya, and Mutnovskaya geothermal power plants in Kamchatka Krai and Mendeleevskaya, Okeanskaya geothermal power plants in Sakhalin Oblast is 83.7 MW” (Eastern Vector…, 2011). At the same time, power supply of remote areas is largely provided by local energy sources that use coal or diesel fuel as energy resources. As part of solving the problems of the energy transition in the East of Russia, we can talk about expanding the use of LNG: “The use of liquefied natural gas as an alternative to diesel fuel and the likely development of nuclear power generation in the Chaun-Bilibinsk energy hub of the Chukchi Autonomous District is one possible direction for diversifying fuel sources in the Eastern Arctic regions” (Saneev et al., 2021). The creation of hydrogen production on Sakhalin Island is undoubtedly among the fundamentally new projects (Dyatel, 2021). In the latter case, as in many presented earlier, the project is associated with the creation of new production facilities for the purpose, first of all, of exporting the promising energy carrier (Hydrogen: A Renewable Perspective. Report for the 2nd Hydrogen Energy Ministerial Meeting in Tokyo & Japan, 2019). The peculiarity of new high-tech projects (in all sub-sectors of the fuel and energy complex without exception) is, among other things, the formation and development of demand for services of industrial and technical nature based on new knowledge and skills. From this point of view, these directions of FEC development can also be considered as a prerequisite and basis for improving the quality of human capital in the East of Russia. The further development of the sector of scientific-industrial services in the FEC is associated with the deepening and expansion of the spheres and directions of its activity. Ultimately, this can lead to the formation and development of a strong and competitive service sector (following the example of Norway). However, as our research and analysis of the condition and development of service activities show, there are few chances for it so far. With companies extracting and producing energy and energy resources focused only on current commercial benefits, the service sector “has no tendency” to localize. This is largely facilitated by modern information technology and the concentration of scientific and technological centers in large agglomerations (Kryukov et al., 2020b).

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7 Conclusion The FEC plays a special role in the Russian economy, which is determined not only by the reliability of energy supplies to consumers in the domestic market, but also by the availability of energy and heat to the population. It is also important as one of the spheres of economic specialization in the system of intergovernmental relations and foreign trade, as well as the most important source of state budget income and employment for a large part of the population. The scale of the complex and its budgetary importance served as the basis for classifying it as one of the main drivers of development in the economy of the “resource curse”—due to the reduction of incentives for the development of knowledge-intensive spheres of modern economy and as a consequence of this underestimation of the role and place of human capital. One can agree with these arguments and generalizations only in the case of individual projects or when considering trends in the development of the complex over very short periods of time. The modern fuel and energy complex in most countries of the world is the object of the purposeful state policy—both in scientific-technical, and in economic fields. In the modern economy, its priorities are increasingly determined by environmental and climatic guidelines and circumstances. As the FEC develops—new ways of obtaining and using energy emerge—the forms and frameworks of interaction of the complex with the state and society, as well as between its subjects (individual producers and consumers of energy) change. The significant peculiarity of the functioning and development of the complex in Russia is predetermined by two important circumstances: the significant space within which its objects are formed and function, as well as the specifics of the previously created main assets (production and technological systems, which can be characterized as idiosyncratic—according to Williamson, 1985). Consideration of the peculiarities of space, as well as the role and place of the previously created and still functioning facilities of the FEC is extremely important— especially when considering the possibilities and directions of changes (carried out for various reasons and due to various circumstances). The special role of the territory from the Urals to the Pacific Ocean is determined not only by the continuing and increasing importance of the FEC in solving the problems noted above, but also as part of the “turn to the East” policy. This fact is reflected in the implementation of energy projects in the Asian part of the country with their primary focus on the export of energy resources or products of their primary processing. This approach raises many questions: how and to what extent these projects can contribute in the present and, especially, in the future to solving the problems of sustainable socio-economic low-carbon development of Eastern Russia. At the same time, the energy transition trends indicate a significant change in both geography and quality characteristics of exported energy resources. This places new demands both on energy resources and on the ability to respond to changes in demand and supply directions. The trend noted in this paper, especially strong in the East of Russia and in the Arctic zone—the concentration of large single facilities in the vicinity of export

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terminals—has both advantages and disadvantages (the main one is a weak connection with the economy of the internal regions of Eastern Russia). The deepening and strengthening of the processes of connectivity and complementarity of various FEC projects with the development of scientific and production and industrial centers in the East of the country may have a significant socio-economic multiplicative effect. However, the realization of this potential opportunity presupposes a departure from the isolated—“object approach” to the development of the FEC. Namely, the focus on extraction or production of energy and raw materials at individual sites without forming and expanding links and interaction with other projects and other sectors of the economy. New priorities aimed at ensuring the sustainability of socio-economic development, reduction, and subsequently cessation of methane and carbon dioxide emissions into the atmosphere imply the formation of mutually responsible relations in the triangle “state—business (FEC)—society (local communities).“ Without changes in the legal framework which ensures access to natural resources and without expanding the role of the regional level in discussing, analyzing, and making decisions about socio-economic impacts, the above goals are unlikely to be achieved. Acknowledgements This paper is based on the results of the research conducted with the financial support of the Russian Federation represented by the Ministry of Science and Higher Education of Russia in the framework of a major research project “Socio-economic development of Asian Russia based on the synergy of transport accessibility, system knowledge of natural resource potential, expanding space of interregional interactions”, Agreement № 075-15-2020-804 from 02.10.2020 (grant № 13.1902.21.0016).

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Industrial Districts and Industrial Clusters. Conceptual Approaches from Italian and Eurasian Experiences David Celetti , Larissa Bozhko , and Raf Avetisyan

Abstract Interest in the issues of cluster development, and their impact on the socioeconomic development of territories leads to numerous studies in this area. Moreover, despite the fact that the cluster rhetoric has strengthened in the scientific and political lexicon, a number of fundamental problems remain unresolved, which constrains the dissemination of this approach in practice. It is necessary to determine the rational basis of cluster policy, as well as the areas and conditions in which the application of this tool is justified. Without serious understanding, the cluster approach risks to be superficial, rather retouching the problems, and quickly replaced by other “fashionable” concepts, without having a significant impact on solving the problems of innovative development of the industrial region. The purpose of the study is to review conceptual approaches to the development of industrial clusters, extending their period from the early Italian experience to modern Eurasian studies. The study is based on scientific works of scientists devoted to the issues of defining the essence of clusters and cluster policy, assessment of its impact on the development of the economy of the region. The study uses methods of system and comparative analysis. The novelty of the author’s approach consists in expanding the temporal boundaries and highlighting various aspects of early and modern cluster approaches. The study identified historical, structural, and evolutionary features of cluster development, which allows developing practical tools for the implementation of cluster policy in the region. The results obtained indicate the diversity and multidimensionality of existing approaches to the development of industrial clusters and the need for their further development. Keywords Industrial districts · Industrial clusters · Economic development · Regional economic · Approaches D. Celetti Department of Historical Geographical and Ancient Sciences, University of Padua, Via del Santo 30, 35135 Padova, Italy e-mail: [email protected] L. Bozhko (B) · R. Avetisyan Higher School of Economics and Construction, Rudny Industrial Institute, 38, 50 Let Oktyabrya St., Rudny 111500, Kazakhstan e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Martinat et al. (eds.), Landmarks for Spatial Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-37349-7_9

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1 Introduction Clusters are the drivers of modern economic development. Regardless of what specialization the clusters have, their activity also has an impact on the general indicators of socio-economic development of the territory. The presence of clusters contributes to the increase of such important parameters of economic development of territories as employment, wages, number of enterprises, their sustainability, and development. At present, it is especially important to study the place and importance of cluster policy in the socio-economic development of regions. Each of them is unique in its own way, combining a variety of resources, technologies, and potential opportunities. That is why it becomes so important to identify existing approaches to the development of clusters, to identify their key aspects and development trends. This allows us to identify general trends in the clustering of the regional economy, to focus on development priorities. Industrial districts, and the broader concept of industrial cluster, have attracted the attention of scholars since the early 1970s. When Giacomo Becattini used the theoretical approaches of Alfred Marshall to uncover the specificities of Italian bottomup, diffused industrialization. Empirical research on Tuscany’s regional economy highlighted social and productive traits that then constituted the basis of further studies, encompassing wider times and spaces, going deeper into single aspects, and questioning as well early Becattini’s findings. The present paper analyzes recent theoretical developments of the definition and conceptualization of industrial districts and clusters, expanding findings and debates from early Italian experiences to recent studies on other regional contexts, as the Eurasian one. It also presents empirical approaches for identifying industrial districts and districts-like agglomerations of firms. The proposed analysis presents up-to-day references to local development studies. It shows how this approach can be fruitfully applied in contexts characterized by diverse historical, structural, and evolutive features. It aims to provide conceptual and practical tools for better understanding of the potentialities of bottom-up industrialization patterns. Results give new understandings of firm’s location patterns within different economic, social, and cultural frameworks. Within this context it shows that Becattini’s early model can be further adapted and generalized to diverse industrial development context in so far that they show bottom-up dynamics framed around connected sectors and promoted by the activity of numerous little and medium entrepreneurs tightly linked with space embedded productive, social, and cultural features.

2 Becattini Conceptual Heritage Most Italian studies on industrial districts and clusters developed within the framework of Giacomo Becattini heritage.1 1

The concept of clusters is broader than that of industrial district. If the latter, in fact, identifies tightly related networks of small and medium companies working within circumscribed space, clusters also entail situation where large firms coexist with smaller ones. Cfr. Garcìa-Lillo (2018), Columer (2016), Zeitlin (2008).

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The Italian economist, juxtaposing Marshallian theories to its own on empirical analysis of Tuscany industrialization patterns, formulated a rich explanatory model of local economic development. On this basis, he actually formalized a first definition of industrial district (Bacattini, 1979). Industrial districts, following Bacattini’s approach and its further developments, are characterized by the coexistence within a limited space of numerous firms working on the same sectors, or in different sectors of the same production chain (Becattini et al., 2009). Among firms, exist sets of material and immaterial links creating informal unions of both competitive and collaborative relations (Buciuni & Pisano, 2018). Namely, these networks constitute the main factor of districts’ competitive advantage (Celetti, 2019). The latter aspect implies strong links with specific spaces, as depositories of material and immaterial sources of both social, economic and cultural identity, and competitiveness (Celetti, 2019). These premises supported theories that regarded industrial districts as relevant factors of local economic development, of wealth creation and fairer income distribution, of long-term resilience, and of effective compensation of the many misbalances induced by globalization processes (Becattini et al., 2009; Camagni & Capello, 2013; Ciccarelli & Fachin, 2017). Namely these assumptions, and the systematizations formulated by Becattini himself (Becattini, 2000), led scholars to go deeper into single aspects of districts and clusters’ economy, verifying, first of all, their influence in creating higher level of welfare in comparison to non-district—areas; the contribution in opening the way to convergence processes; or their effectiveness in building up higher resilience degrees (Busato & Corò’, 2011; Coro’ & Grandinetti, 2010). The analysis of the transformation within traditional industrial districts induced by European monetary unification has highlighted how successful firms managed to position in high added value niches within global value chains, without losing or lessening their tight with local networks of producers (Coro’, 1998; Id., 1999; Caroli & Fratocchi, 2000; Coro’, 2003; Id., 2004; Toschi, 2012). Processes of delocalization, widely involving, above all, during the 1990s, not only major contractors, but also medium and small firms, seemed to question districts inner factory threatened by the loss of capital, know-how, employment. This option showed however its limits and in the last two decades local spaces emerged again as a source of competitive advantage in a measure to compensate lower production costs (Bettiol, 2015; Fortis, 1998; Lees-Maffei & Fallan, 2014). Districts’ economies emerged as invaluable sources of innovation, creation, and transmission of knowledge and know-how on which built effective responses to an ever complex and changing world (Ricciuti, 2013; Marini, 2011). They also gained importance in the relation between companies holding global brands and local subcontractors, whose social and spatial embeddedness are considered sources of value transmitted all along the value chains in terms of confidence, tradition, and reputation (Rebellotti, 2003). Recent crises, as those induced by European monetary unification in the late 1990s, by the financial turmoil of 2008, or, more recently by 2014 and 2022 events and the pandemic, have renewed the debate on districts’ resilience.

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On the one hand it has been stressed their capacity to promptly adapt to new markets’ landscape, using innovative organizational and marketing approaches, or exploiting information technology to bypass transport and communication costs (Amatori, 2017; Guerrieri, 2001; Sansovito, 2020; Zoltan, 1999). The latter emerges as a strategic opportunity namely for small and medium companies, as it opens original production approaches (and reinforces relations and links with offshore clients and markets (Bandini, 2018; Bruzzo, 2020; Sufian, 2021; Hilmersson, 2016; Manzo, 2015; Santarelli, 2003). This last aspect is finally linked with views on the benefit of traditional industrial clusters, especially if directly linked with the creation of products of consumption, of cultural tourism as a feature of the wider conceptual framework pictured by experience economy studies (Musso, 2016; Pine, 1999; Timofeeva, 2019). Delocalization and offshoring strategies determined in any case losses in terms of firms, personal and social links (Leoncini & Montresor, 2008). Low capitalization levels limit the possibility of technological innovation, investment in production and marketing. The limited recourse to professional managers is also seen as a fragility (Arora et al., 2001). Medium terms results appear however extremely complex, showing, more than differences between small and medium firms on the one side, and bigger ones on the other, different reactions among similar companies. Successful ones managed to adapt and transform themselves, launched innovative approaches to production and marketing, conquered high-quality market niches, and used more efficiently scarce resources, as time, labor, or energy (De Chiara, 2017; Galbadon-Estevan & Ybarria, 2017; Goodman et al., 2016; Simeoni et al., 2018). However, the approaches they could reach, and respond to, the rising market of luxury, and semi-luxury, items. They marketed, together with their produce, immaterial values often linked to old artisanal roots of traditional industrial districts (Carli & Morrison, 2018; Lombardi, 2016; Turgel & Ulyanova, 2019). Quality, in particular, emerges namely as the outcome of a complex set of factors where product’s functionalities juxtapose, in the act of fruition, with history, landscapes, and broad perceptions of territorial peculiarities (Altamura & Pisani, 2019; Anastasia & Corò, 2011; Busato & Corò’, 2011; Coro’ & Grandinetti, 2010; Corò & Gurisatti, 2016; Perrino, 2020; Volpe et al., 2012). Following these interpretations, industrial districts are seen ever more embedded in specific spaces, whose boundaries, though overlap different territorial administrative units; retain strong cultural homogeneity and visibility (Coro’ & Dalla, 2015; De Ottati, 2018; Dyba, 2020; Indovina, 1990; Yanagisako, 2018). The latter aspect include the role of the brand “Made in Italy”— and of local brands as the well-known “Murano artistic glass”—as a world-wide recognized mark assuring upper-class design, aesthetic, and quality (Celetti, 2009b; Gilmore & Pine, 2007; Lacquement & Chevalier, 2016; Lees-Maffei & Fallan, 2014). Spaces emerge therefore as one of the main factors allowing analysis and understanding of nowadays industrial districts’ development, limits, and potentialities (Lacquement & Chevalier, 2016). It confirms not only as a source of assets for maintaining and enhancing firms competitiveness in the world market, but also as an object of study per se, whose characters can fully be grasped only by juxtaposing

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qualitative analysis, to quantitative, diachronic reconstruction of firms dislocation in given territories. This last approach, in fact, gives insights on the dislocation of firms of different sectors in space and time, building clear pictures of the emergence and transformation of industrial districts, as well as of their comparative influence on local development patterns (Celetti).

3 Empirical Approaches Along with theoretical approaches based on qualitative assumptions, industrial cluster can be analyzed exploiting methodologies and instruments of economic statistics, and, in particular, of location (or concentration) and specialization indexes. Location index describes disposition of specific factors, highlighting cases of abnormal concentration comparing the data of single territories with regional or national averages. The index provides therefore evaluation on sectorial specialization of local economic systems. Index values significantly higher than the unity show high degrees of specialization in specific sectors, in relation to the regional or national average, as the coefficient is calculated comparing the proportion of a chosen parameter, for example, the number of firms in a single municipality, with the value of the same parameter at upper territorial level. Along with location index, space disposition of firms can be spotted using specialization index, a coefficient of dissimilarity that varies between zero, when the analyzed territory shows for the chosen parameter (for example the number of firms, or of employees in a given sector identical of the of surrounding areas or of upper administrative units) and one in case of highest possible specialization, as, for example, if all the firms or employees are working in the same sector. Both indicators provide useful insights on the structure of local economy with a focus on firms’ specialization. They can therefore be used for identifying industrial districts and clusters, territory with high concentration level in single branches being likely to host this kind of productive structures (Guarini, 1996; Pinkovetskaya, 2015).

4 Industrial Cluster in the Eurasian Space The existing approaches to cluster development identified by modern scientists can be divided into seven groups. It should be noted that the identified groups, developing the early theories of Becattini and his followers are quite diverse and multidimensional. The first group of papers is devoted to the issues of determining the essence of clusters and cluster policy, assessing its impact on regional economy’s development. Variants of definitions were proposed by Markov et al. (2017); they concluded, “Cluster policy is used as a generalized name for various ways of supporting and creating network associations of enterprises.” Systematization of the approaches of foreign scientists to the definition of the concept of “cluster” was carried out

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by Kokareva (2008), who concludes that a cluster in general is a spatial form of organization of productive forces, factors of production, and social (economic and social) relations, which has the following characteristics: geographic localization; specialization of manufacturing firms; diversity and plurality of participants, their interdependence and complementarity; competition and cooperation. Furre (2008) emphasizes the importance of the impact of clusters on economy and, in this regard, the need for a full study of such a concept as cluster policy and its components. The author himself defines a cluster as “a geographically close group of interconnected companies and associated institutions in a particular area, related by common features and complementarities.“ The author refers to the cluster policy as all policies that fall into one of three categories: those aimed at creating, mobilizing or strengthening a specific cluster; using clusters to improve efficiency; aimed at creating optimal environment for development and creation of clusters. Reflections on cluster policy and its role in economic development are contained in the works of Brakman and Marrewijk (2013), Corrado et al. (2005) identified a number of reasons why cluster policy is not always effective. Among them they point at the lack of a precise definition of the concept of a “cluster”; difficulties in limiting the cluster in space; Porter’s model is only partial. Corrado et al. (2005) used a methodology that allows endogenous selection of regional clusters using a multidimensional stationarity criterion, where the number and composition of clusters are determined by applying pairwise criteria of regional differences in production volumes per capita in time. The research by Kozonogova et al. (2018) is of special interest, in which the author conducts economic and mathematical assessment of the impact of the cluster policy on the quality of solving the problems set by the Government as they follow: improving the quality of life in the territory where the cluster is located; contribution to attracting investments to the territory; development of small and medium-sized businesses; development of international scientific and technical cooperation. As a result, it was proved that the fact of cluster existence in the territory is reflected in the amount of wages in it; with increase in the number of clusters, the level of investment in fixed assets also increases. Ketels (2013) in his research proves that cluster development is closely related to changes in development of indicators of the region. The article presents opinions of foreign authors on the most significant criteria affecting cluster policy. Among them, there is location of the cluster, its specialization, size, etc. The aim of the research, conducted by Di Maria and Costalonga (2004) was to find common theoretical foundations of the cluster concept in economics and the concepts of internationalization of business activity. The author notes that internationalization of clusters opens up wide opportunities for reorganization of innovation processes in the regions, based on new forms of division of labor and cooperation among cluster members from different countries of the world. The example of Italian industrial regions has proven that clusters open their borders through the expansion of production and distribution chains, both nationally and internationally. There are two main scenarios for the development of clusters’ internationalization; they are production and commercial. Kutsenko and Meissner (2013), developing ideas on

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approaches to analyzing the impact of clustering on territories development, emphasize that the cluster approach makes innovation policy more systemic, coordinating measures aimed at supporting various participants in comprehensive efforts, and connecting the most promising localized industries. The second group of studies is devoted to selection of criteria to classify clusters. The research of Nikonova (2016) should be noted. The study is an analytical review of a modern package of documents, definitions, main institutional factors and concepts that determine cluster policy. In his research Kutsenko (2015) analyzes characteristic features of a successful cluster (quality of the urban environment; critical mass of specialized companies; domination of private initiative; internal competition and openness; presence of specialized independent governing bodies and active working groups; formalization of rights, responsibilities and decision-making mechanisms; implementation of joint innovative projects and formation of a belt of innovative startups around large companies or universities), correspondence of pilot innovation clusters to these characteristics. The third group of studies is devoted to issues of supporting regional development and stimulating cluster initiatives. In particular, they are research papers of Emets and Purgin (2015), Vishnyakova (2015), Emelyanova (2016), Lisitsa (2010), Mikhailova and Ilyina (2017), Neucheva (2011), Turgel et al. (2016, 2018), Pyankova (2015), Skryl (2016), Krutikova et al. (2017), Shvetsova (2016), Myasnikova (2018). In these works, the concepts of special economic zones, zones of territorial development, and territories of advanced socio-economic development, history of creation and evolution are considered, and questions of their functioning, problems and prospects of creation are raised. Experience of application in various constituent entities of the Russian Federation, results, negative and positive features are also considered. Another important array is the works of Kassenova (2013), Nevmatulina (2015), Kovaleva (2014), Supataeva (2017), Tulupova et al. (2015), Daribekova and Alizada (2018), Vlasova and Vechkinzova (2014), Kireyeva et al. (2022), dedicated to the analysis of trends and prospects for organizational mechanisms development of territorial economic systems in the Republic of Kazakhstan. Responsibility of the SEC for industrial zones development, technoparks, special economic zones, specifics of special economic zones management, and mechanisms of their development are considered. Assessment of the impact of national cluster policy, the age of the cluster, benchmarks for clusters development in neighboring regions, and aggregate level of regional innovation potential on the number and quality of cluster initiatives was carried out by Kutsenko et al. (2017). Certain issues of cluster policy were considered in the works of Delgado et al. (2014), Beshimbaev (2004), Kostenko (2016), Karlsson (2008), Kolchinskaya et al. (2018), Islankina (2015), Akhmetovna, et al. (2018), Engel and I. del-Palacio (2009). In the fourth group of studies, it was noted that clusters of research companies and scientific institutes have a number of positive externalities. For example, as noted by Broekel and others (2018) firms being in R&D clusters receive additional subsidies, and also are provided with a better position in knowledge networks at the country level through subsidies for joint research. Another study by Broekel

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(2019) notes that subsidies for collaborative research activities between local firms have the greatest effect in the least innovative regions, while subsidizing interactions with large universities is more important for the most innovative regions. Moreover, support for research projects without cooperation has a negative effect. Egbetokun et al. (2014) indicate that the choice of partners for scientific interaction is not accidental, and synergy from R&D alliances is the greatest if firms have sufficient ability to “absorb” (understand) their partners, but at the same time they present sufficient novelty for them to complement pool of their competencies and bring new ideas. Thus, it is necessary to stimulate cooperation in the field of R&D very carefully, assessing the extent to which companies complement each other and are able to learn from each other at the same time. The fifth group of studies is devoted to the problems of modernizing the SEZ from the perspective of the cluster approach. The cluster theory has been actively developing since the 90 s. XX century, its founder is considered to be the Nobel Prize Laureate Porter (1998), who identified such main features of the cluster as territorial specialization, competition, and cooperation. Marshall (2009) laid foundations of the theory of geographical clustering of firms. According to Marshall, geographical proximity of firms, which he described as an “industrial area,” creates externalities, which he called “savings from agglomeration (or localization).” This arise from integration of the labor market, interaction of knowledge, specialization, and are associated with economic benefits for member firms in the form of access to specialized human resources and skills, lower costs, knowledge transfer, and increased productivity. Porter emphasizes the role of these advantages in increasing productivity and competitiveness of firms, regions, and countries in his theory of industrial clusters. In its cluster concept, the most important focus is on the “competitiveness” (of firms, industries, regions, and countries) in the global economy. The openness of firms and industries to foreign competition is considered as a driving force for the formation and development of the cluster. Free economic zones have clear common features with Porter’s clusters. Theoretical foundations of the cluster approach in organization of free economic zones are reflected in the works of, Amiti and Javorcik (2008), Mathews (2010), and Krugman (1991). Neo-Orthodox approach ignores the role of agglomeration economy, suggesting that a free economic zone itself provides a platform for attracting export-oriented foreign direct investment (FDI), offering favorable investment climate and there is no need to combine it with clusters. Free economic zones are, in fact, geographically concentrated, state-supported agglomerations of internationally competitive enterprises with a number of advantages, including efficient infrastructure, a favorable business environment, few regulatory restrictions, and a minimum of bureaucracy. The role of free economic zones in generating savings from agglomeration and its advantages is ignored in the existing literature, largely due to the assumption that free economic zones are commercial enclaves with small internal connections, where cheap labor is used for low-quality production. However, international experience shows that zones evolve and their characteristics change over time. They are getting bigger, and now they are better

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integrated into the economy, producing more technological and capital-intensive products. In this regard, there is a need to move to a new theoretical paradigm based on clustering and agglomeration economy in order to capture potential benefits of free economic zones. This will expand our understanding of their benefits and underlying mechanisms. Thus, there are strong arguments in economics for creating free economic zones within a cluster approach. The sixth group of studies is devoted to the environmental component in clusters’ development. To assess the role of environmental protection in the system of global sustainable development goals, it is proposed to use the provisions contained in the works of Daly (1990), Redclift (2002), and Elliott (2012). To understand the specifics of environmental policy implementation in industrial regions, the works of Blewitt (2014), Korhonen, (2014), and Wu (2020) are important. In this case, the authors focus on how the global sustainable development goals are modified when trying to integrate them into development policies of individual industries and regional policies. To analyze the changing role of environmental issues in the system of values of the population of post-Soviet countries, we used the provisions contained in the works of Pishchulov (2016) and Sidorov (2004). Specifics of national goals formation of environmental policy are revealed in the works of Voloshinskaya et al. (2016), Tetior (2013), Gorelov (2013). The works of Turgel et al. (2018), Grebeneva et al. (2018), Adilbekova and Sultanova (2018) are important for assessing the scale of negative anthropogenic impact on the environment. A comparative analysis of various aspects of environmental policy in the industrial regions of Russia and Kazakhstan was performed in the works of Turgel et al. (2019). The seventh group of studies are works of Alaverdyan et al. (2018), Hajduk (2016), Han et al. (2019); it reveals the role of clusters in smart regions development, and smart regions are considered as accelerators of clusters’ development. First, research papers of D’Auria et al. (2018) are devoted to the analysis of state’s role in the development of smart territory projects, which reflect the impact of implementation of the smart cities concept on social and economic situation of local communities. Angelidou (2014), Nikitaeva et al. (2022) examine factors that influence the choice of a model for supporting smart regions, and characterize options for these projects development. Meijer and Bolivar (2016) demonstrate how national and local policies for smart territories projects are interrelated. There are works where authors analyze how smart cities’ concept implementation allows us to develop global and national goals. Thus, Turgel et al. (2019) consider the possibility of using smart city technologies to achieve global goals in the field of environmental protection. Praharaj et al. (2019), Urdabayev and Utkelbay (2021) justify the need to support smart city projects to achieve country’s innovative development goals. In recent years, a number of studies point to the institutional component as the main cause of socio-economic development. The main task is to choose a trajectory—a sequence of institutions, meeting certain requirements and having a chance of success. Secondly, researchers emphasize that digitalization leads to optimization of management processes, increasing competitiveness in all sectors of economy (Alaverdyan et al., 2018). In the works devoted to industry and industrial research,

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it is proved that in accordance with the concept of “industry 4.0,” the impact of digital technologies on efficiency and sustainability of the industrial sector will grow (Hajduk, 2016). Third, at the micro level, research shows that digitalization is fundamentally changing a business ecosystem, offering unprecedented opportunities for entrepreneurship. Capabilities of digital platforms have a positive impact on the economic effects of companies. Based on the results of empirical research, it can be concluded that society as a whole is ready and has the necessary skills to use the Internet and use it in everyday activities. Thus, having considered the seven selected approaches to the task, it can be noted that there is quite a lot of experience in studying cluster policy at the national and regional levels, and cluster classification options have been developed. At the same time, the issues of improving the policy of state regulation of accelerated clustering of regions remain insufficiently studied. As part of the analysis of foreign experience in development of regional clusters, the most general models of cluster formation in the region were formed, combined by geographical features. Formation specifics of these models is associated with historically established industries and macro-regions of specialization, with a developed system of supporting industries, service companies and specialized institutions, presence of close relationships, and global challenges facing enterprises participating in these clusters.

5 Conclusions The concept of industrial district emerged from Giacomo Becattini’s analysis led in 1970s on Tuscany’s regional development. Seeking to explain the impressive development of Tuscany’s manufacturing system after the Second World War, he looked at the juxtaposition of social, cultural, and productive factors within a productive landscape mainly formed by little and medium firms specialized in a single branch of production within delimited spaces as a relevant, and distinctive, source of competitive advantage. He reinterpreted in this sense pioneering interpretation of Alfred Marshall on the nature of industrial districts, personal networks, territorially embedded material, and immaterial values. It gradually formulated a model that helped understanding transformations going well behind the Tuscany case, to encompass many successful local industrialization paths, mainly localized in Northeastern and Central Italian regions. It emerged that those factors that Becattini pinpointed for explaining Tuscany’s achievement were reproduced, though always adapted to local features, in other areas, periods and social contexts. Starting from these premises, question if the model might be generalized even beyond Italian, and Western European experiences, testing, in particular, it is applicability to the wider Eurasian context. Broadening the analysis from definition of industrial district to that of industrial cluster, it critically reviewed scientific literature and methodological approaches to the study of this form of local industrial development with a focus both on Italian

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and Eurasian experiences. From the analysis it emerged that specialization in single branch of production within well-delimited territorial boundaries generates conditions for sustainable local development, as it forms productive (networks of firms working in the same or correlated branches), cultural (know-how, experience, shared values), and institutional (schools, public and private association, links with regional and national administrations) conditions. In turn, under these conditions build effective competitive advantage in the globalized economy. The article proves that original Becattini’s conception, though enlarged in the broader definition of industrial cluster, can well be used as a framework for studying the current transformation of local economies also in contexts substantially diverse from the regional landscape of North-Eastern and Central Italian industrialization (Cfr. Beccattini, 2015). The followers of Becattini’s early theories developed existing cluster approaches, proposing a variety of concepts, highlighting the rational basis of cluster policy, as well as the areas and conditions in which the application of this tool is justified. Having considered the seven selected approaches to the task, it can be noted that there is quite a lot of experience in studying cluster policy at the national and regional levels, and cluster classification options have been developed. At the same time, the issues of improving the policy of state regulation of accelerated clustering of regions remain insufficiently studied. In addition, in the implementation of cluster initiatives, along with the state is actively involved business and academia, which makes it even more difficult to assess the context of the formation of certain clusters: with the help of the state or in spite of them. As part of the analysis of Eurasian experience in the development of industrial clusters, the most general models of cluster formation in the region were formed, combined by geographical features. Formation specifics of these models is associated with historically established industries and macro-regions of specialization, with a developed system of supporting industries, service companies and specialized institutions, presence of close relationships, and global challenges facing enterprises participating in these clusters.

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