CLUSTER COOPERATION MECHANISMS EVALUATION FOR WASTE PROCESSING OF THE FOREST COMPLEX OF SIBERIA

The study subject is the approaches development for assessing coopera� on in a cluster. Study purpose: a selec� on of tools to assess the characteris� cs of coopera� ve � es in a cluster for processing waste from a forest complex in Yenisei Siberia. Methods: Case analysis, ques� onnaire, survey, expert assessment, and a method of pair comparisons. Results: The following results have been substan� ated: The emerging cluster for waste processing from the Yenisei Siberian forest complex; the situa� onal factors; industry specializa� on; the goals of crea� ng a cluster, and the interests of par� cipants. As the cluster evolves, the direc� ons and coopera� on level of cluster members are the subject to change. The obstacles ranking for coopera� on in the cluster is carried out as well. An aggregated indicator of the intensity of coopera� ve � es, based on fi ve criteria, is proposed, which makes it possible to evaluate both industrial and innova� ve coopera� on. In addi� on, it is proposed to determine the weigh� ng factors situa� onally, depending on the target orienta� on of the cluster. Summary: it is shown that the desired value of aggregated coopera� on indicator should be formed in the coordinate system, i.e. “innova� on – sustainability”. Evalua� on and regula� on of the indicator level can serve as a tool for making and implemen� ng strategic decisions on cluster development priori� es.


INTRODUCTION
The fourth industrial revolu� on has given us a phenomenon of a circular economy (Cooke, 2012a;2012b;Perkins, 2003;Safarzyńska and van den Bergh, 2010;Sauvé et al., 2016;Schroeder et al., 2018), that is, economies with closed or "green" produc� on chains in which waste is minimal or absent. The business models search and implementa� on that allow implemen� ng the circular economy principles at the micro level in the global scien� fi c community seems to be a complex and relevant research task (Kallis and Norgaard, 2010;Lacy and Rutqvist, 2015;Roos, 2014). For Russia, a country with signifi cant resource poten� al, the task of building a circular economy is even more complex and requires the search for new organiza� onal and managerial solu� ons that meet the goals of maintaining sustainable development and moving produc� on to a new technological level. This formula� on of the issue is especially relevant for nature-exploi� ng industries and resource regions, including the forest complex of the Yenisei Siberia.
It is interes� ng to note that over 60% of the Russian forested area is concentrated in Siberia. Forests not only provide wood processing industries with raw materials, but also create signifi cant export opportuni� es that are underu� lized due to insuffi cient implementa� on of technologies for deep and waste wood processing. Forests also perform social and environmental func� ons, crea� ng condi� ons for the sustainable development of forest areas. The "anchor" enterprises closure of the region's forest complex in the post--perestroika period has become a limi� ng factor in the development of enterprise coopera� on, product diversifi ca� on, mul� -purpose forest management, and the introduc� on of new technologies.
In the forest sector today, it is impossible to ignore the trends in the circular economy forma� on, whose main task is "design restora� on" (Ellen MacArthur Founda� on, 2012), based on management methods that do not reduce the re-genera� ve capacity of ecosystems (Schroeder et al., 2018), and the forest sector profi tability. In the forest complex, recycling can "introduce an innova� ve component" (Rubinskaya et al., 2016), and waste should be considered as "raw materials, economic effi ciency, and environmental safety, which can be signifi cantly higher than the primary raw materials" (Rubinskaya et al., 2016).
The "not to make it worse" mo� va� on is diffi cult to implement if industry actors are not involved in coopera� ve interac� on, also within clusters. Russia's resource redundancy complicates the problem. In Europe, with incomparably lower forest poten� al, according to the Helen MacArthur Founda� on, the introduc� on of circular economy principles by 2030 will allow "reducing net resource costs by 600 billion Euros, annually increase resource produc� vity to 3% per year, and receive an annual net income of 1.8 trillion Euros" (Ellen MacArthur Founda� on, 2015). According to es� mates of the same fund, only 6% of the world's resources is recycled.
Clustering can be seen as a form of circular economy in crea� ng and developing technological chains; the accumulated Russian (Vasilieva et al., 2017;Kozhukhov et al., 2017;Rezanov, 2016;Smorodinskaya, 2014) and foreign experience (Fløysand et al., 2012;Haviernikova et al., 2016;Luhas et al., 2019;Njøs and Jakobsen, 2016) is a clear confi rma� on of this. It is noted that the solu� on to the problem of weak territorial enterprises coopera� on of the Tomsk region forest complex is clustering, which will allow establishing the integrated wood processing (Kozhukhov et al., 2017). In Vasilieva et al. (2017), signifi cant cluster groups for the Krasnoyarsk Territory were evaluated on the basis of localiza� on and connec� vity indicators, and a conclusion about the high clustering poten� al of the � mber industry complex of the region was drawn, as one of the direc� ons of the cluster core forma� on in the forestry complex is called the coopera� on of processing industries, including the use of waste (Rezanov, 2016). No� ng that "coopera� on becomes the main mechanism for systems harmonizing"; Smorodinskaya N. V. shows the dependence of cluster innova� on on the coope-ra� ve � es organiza� on; moreover, the deployment of the "triple helix" of innova� ve interac� on is possible in tradi� onal industries (Smorodinskaya, 2014).
A number of authors compare the eff ects of clustering when they are deployed from top to bo� om and from bottom to top, which aff ects the characteris� cs of par� cipants coopera� on (Fløysand et al., 2012), provides an assessment of cluster coopera� on risks (Haviernikova et al., 2016), and discusses the innova� ve eff ects of coopera� on development in related industries and knowledge coopera� on (Njøs and Jakobsen, 2016). The work that is devoted to the Finnish forest complex study (Luhas et al., 2019), where the cluster concept has been successfully implemented, has been given a review of the coopera� ve (or network) cluster eff ects that are valuable to this study. Cluster crea� on produc� vity assessment requires some a� en� on, regardless of the cluster ini� a� ve subjects, stage determina� on and cluster development prospects. The hybrid nature of this supra-organiza� onal forma� on, with specifi c goals and coordina� on mechanisms, creates diffi cul� es for targeted cluster management, because in essence it is the management of coopera� ve � es.
The level and mechanisms of par� cipant coopera� on that require analysis and evalua� on, including the spa� al-temporal context and the prospects for co-evolu� onary development of the Yenisei Siberia regions, are the greatest research interests. The value of this study is the development of methodological approaches for assessing the level of coopera-� on, taking into account the cluster confi gura� on, stages of its development, the need for smart specializa� on, and the overcoming of the technological stagna� on of the industry, followed by the use of coopera� on indicators in making strategic decisions in regula� ng the development of the cluster and introducing the principles of a circular economy at the micro level.
The study purpose is to fi nd adequate tools for assessing the development of a cluster, primarily taking into account the direc� ons, obstacles and the level of par� cipants' coo-pera� on, which would make it possible to make strategic decisions in the direc� on of building closed technological chains. The hypothesis of this study is the assump� on that a set of direc� ons and tools for regula� ng rela� onships in a cluster depends on industry specializa� on, cluster confi gu-ra� on, and the level of intra-cluster coopera� on of par� cipants.

METHODS
The systemic, situa� onal and evolu� onary approaches form the methodological basis of this study. Among the theore� cal concepts necessary for a deep study of forma-� on, development and coopera� on in a cluster evalua� on issues, taking into account its target orienta� on, within the framework of this study, are the concept of a quad-helix (or triple helix) (Carayannis and Grigoroudis, 2016;Smorodinskaya, 2011), the system-integra� on theory (Kleiner et al., 2008), the economic systems sustainability theory (Melnikova and Bezrukikh, 2017a;Melnikova and Bezrukikh, 2017b), and the circular economy concept (Accenture, 2014;Ellen MacArthur Founda� on, 2015;Roos, 2014;Sauvé et al., 2016;Schroeder et al., 2018).
The system-integra� on theory by G. Kleiner off ers a universal typology of economic systems, based on determining the boundedness / unboundedness of the system in � me and space, and taking into account the posi� on of the researcher (Kleiner et al., 2008). For the clustering ini� ator, the cluster appears to be a project-type system, limited in � me and space, while in the study of coopera� on, the cluster is a medium-type system. Emphasizing the growing popularity of the evolu� onary approach to the study of clusters (using the � me factor), a number of authors note the lack of a� en� on to "local factors (or space factors), neglect of mul� -scalar infl uences, and human factor underes� ma� on" (Trippl et al., 2015). Taking into account "local factors", such as resources, interests of local residents, economic problems of territories, etc., implies the situa� onal nature of each cluster, which makes it necessary to look for typing op-portuni� es in the characteris� cs of coopera� ve � es.
Based on the evolu� onary approach (Østergaard and Park, 2015), it was revealed that narrow industry specializa-� on impedes upda� ng (Cooke, 2012a;Cooke, 2012b;Mar-� n, 2011;Njøs and Jakobsen, 2016). The conclusion in which the cluster environment develops and the level of trust between the cluster enterprises grows and "the area of coope-ra� on and the methods for iden� fying its direc� ons change" (Kostenko, 2016) is also based on the methodological basis of the evolu� onary approach and is extremely important in the context of this study.
The correc� on vector of cluster projects implementa� on environment and areas of external coopera� on is set by the concept of quadruple helix (Carayannis and Grigoroudis, 2016), which assumes a coordinated interac� on between society, state, business, and science (Smorodinskaya, 2011;Smorodinskaya. 2014;Shestak and Tyutyunnik, 2017). Taking into account the importance of the environmental mo-� ves of the clustering process in the forestry complex and the severity of the environmental problems in this tradi� onal industry, untwis� ng the quad-spiral of interac� on in the cluster is a prerequisite for the innova� ve transforma� on of the industry, achieving "green" development goals. Cluster coopera� on and innova� on guidelines should not undermine the sustainability of individual cluster members, both in percep� on and in reality.
The most modern views on the rela� onship between specializa� on and innova� on are refl ected in the concept of "smart specializa� on" (European Commission, 2014), which refers to the coherence of industrial, innova� on and educa-� onal policies. In order to achieve meaningful cluster development, the conceptualiza� on and debugging of coopera-� ve cluster interac� ons is necessary. The concept of "smart specializa� on", despite its a� rac� veness, is diffi cult to implement and requires "the development of new sophis� cated technologies based on local capabili� es" (Balland et al. , 2018), and therefore, the development of external rela� ons for the cluster based on the concept of a quad-helix.
The system-integra� on theory (Kleiner et al., 2008) allows considering the co-dependence of economic systems and the content of coopera� ve � es, including business models sustainability (Melnikova and Bezrukikh, 2017;Melnikova and Bezrukikh, 2017) of individual par� cipants and cluster interac� on as a whole. The cluster strategic goal is formulated as the task of managing such target interac� on parameters in the cluster as innova� on, sustainability, environmental and social orienta� on, the ra� o of specializa� on and diversifi ca� on, localiza� on boundaries, and the intensity of coopera� on. The main a� en� on is paid to assessing the intensity of coopera� on, which means a certain level of balance between the independence of par� cipants and their coordinated co-evolu� on.
The informa� on base for this work includes research by domes� c and foreign authors, ques� onnaires and data surveys of par� cipants in the emerging cluster and experts, and the case analysis results of clusters at diff erent stages of development. Twelve cases were selected, involving foreign experience as well, and the preference was given to clusters in nature-exploi� ng industries; paradoxical examples were not ruled out and the correc� on of research tasks and refor-ma� ng of analysis parameters were allowed. The ques� onnaire results were processed with the method of pairwise comparisons.
The study algorithm includes the following steps: clarifying the content of "cluster" concept and the meaning of coopera� on in the cluster based on literature analysis; se-lec� on of cases for a qualita� ve analysis of cluster evolu� on; considera� on of the co-dependence content dialec� cs and the level of coopera� on and cluster evolu� on direc� ons; analysis of the impact of coopera� on parameters in the cluster; manifesta� on of innova� ve eff ects in cluster development; the study of the mo� va� onal fi eld of par� cipa� on in the cluster; obstacles and preferred pa� erns of interac� on; the importance component of knowledge in coopera� on and the level of informal contacts for cluster development; designing an integrated indicator for assessing the intensity of coopera� on in a cluster, taking into account the compa-ra� ve importance of obstacles to cluster development; and coopera� on management in the cluster to process the forest complex waste.

RESULTS
A content analysis of the cluster concept allows iden�fying a number of defi ni� ons, such as interconnec� on, in-terac� on, interdependence, and complementarity, that are present (together or separately) in all considered defi ni� ons of the cluster and can be reduced to the concept of "con-nec� vity" and the processes of coopera� on in the cluster. The concept cluster varies according to the topic of interest to a par� cular author (Chernova, 2014). Depending on the research tasks being solved, the range of methodological approaches used in the study of clusters changes, i.e. the emphasis shi� s from, for example, industry to territorial aspects, and vice versa (Kolesnikov and Khazaliya, 2016). Nevertheless, the issues of cluster par� cipants' coopera� on are inherent in all studies, regardless of their focus.
The following defi ni� on of a cluster seems to be most appropriate, focusing on the interac� on of par� cipants, i.e. "a cluster is a set of organiza� ons and ins� tu� ons interac� ng in a certain fi eld of ac� vity, compe� � on and coopera� on, which leads to an increase in the compe� � veness of each of them due to factors such as the aggregate effi ciency (or exchange of knowledge and informa� on, and network eff ects), training and economies of scale" (Kolesnikov and Khazaliya, 2016). The most important aspect of the projected cluster is the innova� ve component and understanding of the cluster as a form "moderniza� on of the territory's economy and a factor of its sustainable compe� � veness" (Komov and Yakovenko, 2016).
The connec� ons between the elements of the system (in this case, the cluster) are the memory that stores the past of the system (Thurner et al., 2018). The cluster a� rac� veness and the technology used in it lead to the accumula� on of a cri� cal number of par� cipants (Arthur, 1994). As a result, the level of demand increases (Luhas et al., 2019;Safarzyńska and van den Bergh, 2010) and the business models standar-diza� on con� nues within the cluster. The iner� a in cluster development also increases when entering foreign markets (Kallis and Norgaard, 2010). Iner� a (as opposed to innova-� on) is understood as the absence of qualita� ve changes in the products and technologies of the cluster, the innova� on occurrence a� enua� on, the decrease in the synergis� c effect of coopera� on and the drop in effi ciency up to the collapse of cluster interac� on. As noted in Perkins (2003), an increase in the intensity of coopera� ve � es over a certain level will impede the introduc� on of new technologies. The mechanism of technological blocking of produc� on diversifi -ca� on takes place as well (Luhas et al., 2019;Perkins, 2003).
In the course of studying the clusters development (Vasilieva et al., 2017;Kozhukhov et al., 2017;Mantsaeva and Delikova, 2016;Rezanov, 2016;Smorodinskaya, 2014;Balland et al., 2018;Ketels et al., 2012;Luhas et al., 2019;Østergaard and Park, 2015), a conclusion was drawn regarding the sectoral focus of coopera� ve � es. If the cluster is formed by enterprises of related industries, then it s� mulates innova� on at the cluster enterprises and produc� vity growth in the region, while in the case of narrow industry specializa� on, produc� vity at cluster enterprises increases, but innova� on is blocked (Aarstad et al., 2016). A number of other studies (Cooke, 2012a;2012b;European Commission, 2014) confi rm the fact that "specializa� on works against innova� on". It is noted that the content of coopera� ve in-terac� ons should include, to one degree or another, the exchange of knowledge (Li, 2018) between cluster par� cipants and external stakeholders.
As the case analysis showed, the prac� cal interest in clusters is due to both the expansion of their support from na� onal and regional authori� es, and their economic role as drivers of compe� � veness, innova� on, and economic growth (Haviernikova et al., 2016;Păuna, 2015). The cluster's main features, along with the concentra� on of opera� ons in a limited area and innova� ve ac� vity, are recognized as the existence of stable � es between par� cipants in coopera� ve interac� ons. Achieving the goal of upda� ng the territorial and sectoral structure of the � mber industry complex, introducing new resource-saving technologies and recycling technologies (Mokhirev et al., 2015) requires the develop-ment of coopera� on in related industries, thus leading to the growth of the importance of coopera� on rela� ons that are external to the cluster. Cross-sectoral knowledge transfusion and knowledge coopera� on are needed.
Another landmark of the projected cluster for the waste processing should be the forma� on of a quad-spiral interac-� on between society, state, science and business. A round table held in Krasnoyarsk in July 2019 confi rmed this, as issues related to the processing of forest resources and their wastes were discussed. The par� cipants were representa�ves of all quad-helix actors, including the government repre-senta� ves of the Krasnoyarsk Territory, such as Ministry of Economic Development, Ministry of Ecology, and Ministry of Forest, public organiza� ons (i.e. four environmental and professional organiza� ons), thirty-six legal business en� � es, and three higher educa� on ins� tu� ons. Five ques� ons were discussed publicly and fourteen ques� ons were included in the ques� onnaire that was issued to each par� cipant in the round table. For the par� cipants of this event, the most in-teres� ng issues are joint projects to enter the world market and receive state support; the need for interac� on and product innova� on is recognized as well. Due to the awareness of the par� cipants regarding the signifi cant accumulated volumes of forest complex waste, the possibility of introducing circular supply models (Accenture, 2014) and restoring resources, using the poten� al of forests subjected to fi res and pests, is being examined.
Regardless of the cluster structure, the fl ow of knowledge and informa� on is an essen� al element in the coopera� on of cluster structures and cluster elements. The informa� on factor is understood quite widely, including informal com-munica� on between cluster members (Vatne, 2011). The degree of cluster members' connectedness and the level of external coopera� ve � es are es� mated by the number of contacts per year (Balland et al., 2018). The frequency of cluster managers contac� ng in Europe with other people in various sectors decreases in the following order: other cluster members, government agencies, research ins� tutes, educa� onal organiza� ons, other clusters, interna� onal markets, and fi nancial ins� tu� ons.
The results of the survey of poten� al par� cipants of the waste recycling cluster showed a diff erent picture. They displayed the greater importance of contacts with fi nancial ins� tu� ons and foreign companies, and the lower importance of contacts with educa� onal and scien� fi c organiza� ons and other clusters. An assessment of informal contacts frequency within a cluster (once every two or three months) is of par� cular value at the stage of cluster forma� on and can be an indicator of the cluster members' mo� va� on. As for the interac� on between enterprises, the preference is given to property rela� ons and technological considera-� ons, rather than to rela� onal arrangements. There is also the lack of understanding of the importance of contacts with educa� onal and scien� fi c organiza� ons, with other clusters and the public. Even less valuable are the rela� onships with society.
Also during the survey, 87 managers/chief specialists of enterprises were surveyed and a list of coopera� on obstacles was revealed. Based on the list, obstacles were ranked using pairwise comparisons; the results are presented in Table 1. Pairwise comparisons of coopera� on obstacles were carried out on a 5-point scale, where 5 (1/5) points are respec� vely the highest (least) signifi cance of the obstacle, 4 (1/4) points have a signifi cantly diff erent meaning of obstacles, 3 (1/3) points have an accordingly high (low) signifi cance of an obstacle, 2 (1/2) points have an insignifi cantly diff erent signifi cance of obstacles, and 1 point, in which the signifi cance of two obstacles is equal. Next, the matrix was transformed into a normalized one, row-average matrices were determined for each obstacle, summed up by es� mates of 10 experts, and the ranks were determined on this basis. In conclusion, the quad-spirals of innova� ve interac� on are not formed yet. While comparing the results of the cluster par� cipants' ques� onnaire, the public survey and the expert community, it can be argued that pair interac� ons are debugged only in pairs such as "society and state", "state and educa� on", and "municipality, as the representa� ve of the local community interest in business". The ideas of business, government, science and the public regarding the direc� ons of development of forest waste processing may diff er signifi cantly. This increases the importance of managing coopera� on in the cluster, which can be carried out based on assessing the cluster coopera� on intensity. Volume 15, Number 1, 2020, pp. 70-79 DOI: 10.20985/1980-5160.2020.v15n1.1620 In prac� ce, a coopera� on coeffi cient is used in order to assess coopera� ve interac� ons in a cluster. It shows the volume of semi-fi nished products, components, etc., received from the outside, to the total costs of the enterprise for the manufacture of marketable products. However, for a more accurate assessment of the situa� on in the cluster, it is proposed to use the integral indicator of coopera� on (Ki.i.c), combining several coopera� on criteria, including a share of the output cost, costs share, jobs share, intellectual property share, and share of fi xed capital investments used in the framework of the cluster. The list of indicators is determined by the need to harmonize the interests of par-� cipants in external and internal coopera� on for the cluster and refl ects the need for circular supplies (indicators # 1 and 2), increased employment sustainability (# 3), diff usion of knowledge (# 4), and accumula� on of investment resources within the cluster (# 5). Weights will refl ect the specifi cs of the targets / obstacles to the development of the cluster, which may change over � me. Since the targets are blurred at the stage of crea� ng the cluster, the level of weigh� ng coeffi cients was determined based on previously obtained points of obstacles signifi cance as criteria, with the involvement of the same experts. The total data for calcula� ng the integral indicator is presented in Table 2.

S&G Journal
The coopera� on indicator calcula� on is worked out by mul� plying the specifi c gravity of the corresponding indicator and its individual value for enterprises with their subsequent addi� on (equa� on 1): (1) where W i -indicator weight; К i -iindicator used in calcula� ons.
The presented indicators do not contradict the updated requirements of the legisla� on of the Russian Federa� on (The Government of the Russian Federa� on, 2016b). They have been established in order to provide state support to clusters, while allowing us to evaluate not only the industrial, but also the innova� ve knowledge-based coopera� on. The calcula� on of indicators 1 and 2 is not carried out for all par� cipants in the coopera� on, but taking into account the par� cipant's posi� on in the value chain (i.e. the choice and weight of indicators 1 and 2).

DISCUSSIONS
Summarizing the features of cluster policy that allow s� -mula� ng innova� on and region renewal (Njøs and Jakobsen, 2016), it is noted that it should support the development of external and internal coopera� on of the cluster, the infl ux of new knowledge, the provision of specialized business services and the crea� on of infrastructure for collec� ve innova-� on, as well as regional localiza� on value chains (Fløysand et al., 2012). There is an opinion that the assessment of cluster connec� vity is necessary at its local (as opposed to global) scale (Rezanov, 2016). In our opinion, the need to assess the level of coopera� on is inherent in any type of cluster; the diff erences are in the approaches used and the informa� on available.
The norma� ve level of the coopera� on indicator may vary depending on the target orienta� on and development strategy of the cluster. The value of the indicator 0.4-0.6 corresponds to the strategy of specializa� on, a situa� on where the main goal is to tap into economies of scale and reduce produc� on costs for a limited range of products. In the cluster evolu� on process, goals will change; product diversifi ca-� on through innova� on will be a priority. In this case, the level of coopera� on should be 0.2-0.4, and its actual level should be calculated according to the methodological re-commenda� ons (Abashkin et al., 2017). On the share of cluster members mutual supplies in the formed forest clusters, the averages are 0.15-0.2 (The Government of the Russian Federa� on, 2016a) in the Tomsk region -0.26 (Kozhukhov et al., 2017). Thus, the prevailing condi� ons for the interac� on of forest cluster par� cipants support a diversifi ca� on strategy, fragmenta� on of goods supply and the introduc� on of new technologies.
The study analyzed the ac� vi� es of 87 � mber enterprises of the Yenisei Siberia, which could poten� ally form the basis of clusters in the � mber industry. The calcula� on of the proposed integral coopera� on indicator has been worked out, its level equal to 0.23 supports the forma� on of the cluster.
Of course, the need for analy� cal tools is not limited to assessing the level of coopera� on. Therefore, in Mantsaeva and Delikova (2016), a system of indicators has been proposed for assessing the prospects of cluster forma� on in the region, with a division into quan� ta� ve and qualita� ve. The set of indicators is determined by the proper� es of cluster structures and allows not only assessing the possibility of cluster forma� on, but also "monitoring the state of the cluster at a certain stage of development" (Mantsaeva and Delikova, 2016).
Agreeing with the need to study the evolu� on of the cluster, it is believed that the list of quan� ta� ve indicators, along with territorial proximity, industry effi ciency for the regional economy, innova� ve ac� vity, and export opportuni� es, should also include a quan� ta� ve measure of the coopera-� ve � es intensity. As noted in Vasilieva et al. (2017), "intercompany coopera� on (...) develops along the en� re value chain based on compe� � ve forms, rather than integra� on within the framework of a single property, and is accom-panied by "blurring" the fi rms' boundaries", which, in turn, complicate the study of the phenomenon of coopera� ve connec� ons.
The percep� on of risks accompanies cluster coopera� on and in many respects depreciates cluster ini� a� ves in the eyes of poten� al par� cipants. An understanding in terms of the crisis phenomena causes in clusters is necessary. Thus, Østergaard C. R. and Park E. see the reasons for the clusters decline in technological lag and the exit of key fi rms from the cluster (Østergaard and Park, 2015). Technological blocking (Perkins, 2003) o� en occurs with an excessively high level of coopera� on, which remains to be assessed, and the output of anchor companies in the cluster is o� en due to a low level of coopera� on. Understanding the degree of cluster members' co-dependence through a coopera� on level assessment will allow us to predict a decline, and even prevent it with an increase in the intensity and diversity of coopera-� on.
In general, the results of this study are confi rmed by the work carried out by the Ministry of Economic Development of the Russian Federa� on, the Russian Venture Company and the Higher School of Economics on cluster policy issues. These development ins� tu� ons have concluded that when evalua� ng the ac� vity of clusters, it is necessary to use quan-� ta� ve indicators of coopera� ve � es (Abashkin et al., 2017).

CONCLUSION
The proposed methodology for assessing the development level of coopera� on mechanisms in a forest waste recycling cluster allows a specialized cluster organiza� on to make informed management decisions to increase the cluster eff ec� veness, i.e. the par� cipants' set forma� on around the product and select carriers of the raw material resource. The methodology makes it possible not only to manage the cluster's ac� vi� es based on the obtained analy� cal data, but also to formulate a forecast for its development, as well as to assess the possibility of "bo� lenecks" in the cluster's technological chain.
To maintain a balance between innova� veness and sustainability of cluster interac� on, built-in mechanisms are needed to increase innova� on ac� vity, which one way or another are determined by the level of coopera� on. A low level of coopera� on limits the feasibility of innova� ve ideas, which is too high, and creates the eff ect of technological blocking. Note that, in the absence of a type of "knowled-

Indicators
The indicator calculati ng formula Weights 1. The produc� on share indicator (К product. ) a К product. = V fc. product. / V total product. V fc. product. -the volume of industrial products, raw materials, materials and components, work and services of a produc� on nature, produced / performed by members of the industrial cluster and used by other par� cipants; V total product. -total volume of marketable products and services of cluster members.
0.30 2. Cost share indicator (К costs ) b К costs = V fc. costs / V total costs , V fc. costs -cost volume for industrial products, raw materials, materials and components, work and services of a produc� on nature, purchased from cluster members; V total costs -total cost of cluster members. К workplaces = N fc. workplaces / N total. workplaces , N fc. workplaces -the number of workplaces in the framework of industrial cluster; N total. workplaces -total number of jobs in the cluster enterprises. 0.20 4. Intellectual property (К intellect. property ) К intellect. property = N fc. intellect. property / N total intellect. property , N fc. intellect. property -the number of patents and cer� fi cates for intellectual property used by par� cipants in the framework of the industrial cluster; N fc. intellect. property -total number of patents and cer� fi cates for intellectual property used by cluster members.
0.15 5. investment propor-� on (К invest ) К invest = V fc. invest / V total invest, V fc. invest -investments volume in fi xed assets in the framework of industrial cluster; V total invest -total investment in fi xed assets of cluster enterprises 0.10 a For par� cipants who do not carry out fi nal produc� on of industrial products. b For par� cipants engaged in the fi nal produc� on of industrial products (The Government of the Russian Federa� on, 2016a; 2016b). Volume 15, Number 1, 2020, pp. 70-79 DOI: 10.20985/1980-5160.2020.v15n1.1620 ge" of coopera� on among cluster members, the most likely scenario for the development of the Yenisei Siberian forestry complex is associated with catching up moderniza� on and reproduc� on of the industry's technological backwardness, which will not allow for solving environmental and social problems specifi c to the cluster.

S&G Journal
The use of the proposed integral indicator for assessing the intensity of coopera� ve � es has certain limita� ons associated with the jus� fi ca� on of weight coeffi cients. In this work, the ra� onale is based on criteria to overcome obstacles for the coopera� on development; for formed clusters, expert es� mates of weight coeffi cients should be based on the criteria of the cluster interac� on goals.
fi nitsii ponya� ya ["Cluster" as a complex organiza� onal and economic system: approaches to the defi ni� on of a concept]. Izves� yaTulGU. NaukioZemle [News of Tula State University.