Development Priorities for the Regional Innovation System Based on the Best Available Technologies

: At the present stage one of the most important factors in the economic growth of Russian regions is the production processes modernization based on the best available technologies (hereinafter—BAT), ensuring the reduction of the negative impact on the environment in cost-effective ways. The most important conditions for the successful implementation of BAT at regional enterprises is the creation of general institutional conditions at the all-Russian level and the supply of enterprises with domestic technologies meeting the criteria of the best available technology. Over the past several years, large-scale work has been carried out in Russia to amend legislation, prepare BAT reference documents, and form the institutional conditions for their implementation. However, sustainable outcomes in speciﬁc regions will require the further development of regional innovation systems, consistent with the needs of local enterprises in BAT. The article proposes a general regional mechanism for managing the sustainable development of an innovation system based on BAT. For its successful implementation, a set of practical recommendations for the Rostov region has been formed. Within the framework of the innovation regional infrastructure it was proposed to create a new institute for the implementation of BAT—the Regional BAT competence center, priority areas for the development of potential and promising BAT in the region were identiﬁed, a general pattern of interaction of the Regional BAT competence center with other participants in the process was developed.

Introduction
Best Available Technologies are "technologies and arrangements that minimize the impact on the environment as a whole and that are not costly to implement" [1].
In the countries of the European Union, this tool, combined with integrated environmental permits and BAT Guides, is one of the most effective ways to reduce the negative impact of production activities on the environment in cost-effective ways.
At the same time, in non-European countries, the application of the BAT-based approach is complicated by the technical incompatibility of European and non-European technologies and technological processes, as well as the different levels of financial capabilities of European and non-European enterprises.
In this regard, there is a sufficient number of papers by foreign and Russian scientists covering various aspects of ways to solve this practical problem of BAT implementation.
In particular, the first way is the adaptation of European BATs to the specific conditions of particular countries.
Within the framework of this direction, a number of works are devoted to the development of methods for determining and selecting the best available technologies for the purposes and adaptation of European technologies to the economies of non-European countries.
For example, Roger Dijkmans proposes multi-stage procedure of the BATs sectorial selection, which allows assessing the technologies in terms of technological feasibility, economic efficiency and ecological parameters that influence the environment and its components [2].
The first step in the procedure proposed by Roger Dijkmans is evaluation of candidate's technologies for BAT status in terms of their technical capability [2]. This allows us to immediately weed out all existing technologies, the practical implementation of which in this industry in this country isn't possible. It also guarantees the technological compatibility of technologies that received BAT status as a result of selection with technologies from non-European countries.
However, this does not guarantee the mandatory availability of compatible technologies in the event that the introduction of all considered European candidate technologies turns out to be technically impossible. If this situation turns out to be real, technologies of local origin will be required, taking into account the specifics of the development of a particular industry in a particular country. Then, on the whole, the approach proposed by the author will work only if there are local candidate technologies that compete with each other and with the corresponding European technologies. In addition, the expert nature of the proposed assessment, a lot of will depend on the composition and qualifications of experts making a decision to include the technology in the BAT, which may lead to ineffective decisions at first.
Schollenberger H., Treitz M., Geldermann J. consider the necessity to adapt the BAT transfer technology into the industrially developed countries, in particular, to identify the main criteria due to the differences in economic, legal and technical conditions [3].
The authors note that by reason of these differences, technologies recognized as BAT in European economies won't necessarily match BAT requirements in emerging economies, and vice versa. Therefore, they have developed a flexible approach to defining BAT, taking into account regional characteristics and preferences of individual countries. As in the previous case, the proposed method includes different criteria and combines technological, economic and environmental parameters. Specific national conditions can be taken into account through the use of individual emission limit values as well as country-specific criterion weights from which the country-specific BAT metrics are determined.
In fact, like the previous authors, Schollenberger H., Treitz M., Geldermann J. tried to build a decision-making algorithm to assess the possibilities of transferring environmentally friendly technology for use in a specific country. The presented method for determining which country to use BAT can be applied by political decision makers and local authorities for accrediting BAT, as well as by technology providers.
At the same time, its practical application is complicated in determining the weight coefficients which used to take into account the regional and national specifics of the possibilities of introducing foreign BAT.
The work by Mavrotas G., Georgopoulou E., Mirasgedis S., Sarafidis Y., Lalas D., Hontou V., Gakis N. is devoted to adapting and technological compatibility of the foreign best available technologies while introducing them into the developing countries [4].
Sectoral aspects of the BAT introduction are considered in the article by L. Loyon, C.H. Burton, T. Misselbrook, J. Webb, F.X. Philippe, M. Aguilar, M. Doreau, M. Hassouna, T. Veldkamp, J.Y. Dourmad, A. Bonmati, E. Grimm, S.G. Sommer [5] (on the example of animal husbandry). The authors note the insufficient completeness and inconsistency of BAT in the field of animal husbandry (in particular, pig and poultry farming), united within the EU. There are highlighted such problems in terms of their adaptation in other countries, such as the complexity of regulation, as well as counterproductiveness in relation to other goals (for example, the exchange of pollution). This requires a comprehensive approach to evaluating different BAT methods to develop effective farm-specific strategies.
The authors also conclude that the achievement of a significant result for the environment in animal husbandry based on European BAT in other countries is associated with significant costs for farmers and a decrease in the economic results of farms, and does not take into account the conditions of farming in these countries. In this connection, the interest of farmers in their implementation requires additional incentives.
Thus, in general, the adaptation of European BAT into the practical activities of enterprises and farms in other countries is associated with significant difficulties.
These difficulties are also associated with direct decision-making by specific enterprises and regulatory bodies of these countries on the introduction of certain BAT in the production process.
In this regard, another way to adapt foreign BAT is to develop tools to justify the introduction of BAT at the level of enterprises and regulatory authorities.
For example, A. Cikankowitz, V. Laforest in their article propose the methodology to assist the manufacturers, authorities while implementing the enterprise operating license renewal procedures focused on evaluating the existing BAT methods and ensuring the basis for assessment of production units and management processes [6].
Damien Evrard, Jonathan Villot, Chadad Armiyaou, Rodolphe Gaucher, Sofia Bouhrizi, Valerie Laforest propose the integrated method to multi-criteria assessment of the reference installations to introduce the BAT based on mathematical and statistical methods [7,8].
The article by D. Huybrechts, A. Derden, L. Van den Abeele, S. Vander Aa., T. Smets analyzes the possibility of implementing the principles of sustainable supply chain management as the basis for the definition of BAT, which can become a driving force for the ecology of global value chains [9].
The process of introducing BAT in Russia is also widely studied by Russian scientists. Here we should highlight the article by T.V. Nevalenov, O.N. Lazdin, which analyzes the role of the voluntary environmental certification system in the process of accelerating the introduction of the best available technologies [10]. Also, T. V. Guseva, Ya. P. Molchanova, M.V. Begak, A.V. Mironov pay special attention to the development of energy management systems in Russia, as well as to the problem of personnel training to ensure the transition to BAT [11].
Many  [12] also consider the introduction of BAT in various sectors of the Russian economy-nickel industry, lead production, precious metals, mining, etc.
Thus, the adaptation of foreign technologies to specific national systems is always associated with a significant difficulties described above. The second, and in the opinion of the authors of this article, more beneficial for non-European countries, the direction of introducing BAT in them is to stimulate the subjects of national (local, regional) innovation systems for the development of potential, promising BAT and BAT itself. In this case, they will initially correspond to all technical, economic and environmental conditions of specific national economies and their subjects. This direction is practically not represented in the scientific literature.
It should be borne in mind that the introduction of BAT at enterprises is not only the greening of production.
For countries and regions in general, in particular for the Rostov region, these are opportunities for restructuring the economy and increasing the share of industries with high added value through the modernization and expansion of production, the transition to energy-efficient technologies, improving conditions and increasing labor productivity, increasing the competitiveness of enterprises. region. And in the long term-the possibility of entering new domestic and foreign markets for innovative environmentally friendly technologies in the field of potential BAT and strengthening the innovative sector of the economy. All these are the characteristics of development. Investment in these spheres will provide a significant multiplier effect for the economy and social sphere of the region in the future. Regional authorities and investors may be highly interested in motivating enterprises to participate in the development and implementation of potential and promising BATs [13]. First of all, the level of institutional conditions for the implementation of BAT at the federal level is extremely important for the regions. In recent years, large-scale work much has been done in Russia to create a full-fledged institutional environment for the implementation of BAT.
The list of areas of BAT application was approved, criteria were determined and methodological recommendations were approved for defining technology as the best available technology, a clear legislative consolidation of this term was ensured, measures were envisaged to stimulate enterprises to implement BAT [13].
Since 2019, the national project "Ecology" has been implemented in Russia and its regions, within the framework of which measures are provided for the implementation of BAT. By the start of the project the Bureau of the best available technologies [14] (www. burondt.ru accessed on 16 October 2021) had been formed [15].
Also on the basis of the Federal Agency for Technical Regulation and Metrology (abbreviated as Rosstandart)-the federal executive authority that performs functions for the provision of public services, state property management in the field of technical regulation, standardization and ensuring the uniformity of measurements [16] Rosstandart has a specialized technical committee for standardization "The best available technologies".
Rosstandart is a base for a specialized technical committee for standardization of "Best available technologies". For the development of information and technical reference books, technical working groups have been formed (BAT Bureau website). Rosstandart approved and published BAT reference books [17,18]. Currently, there are already positive examples of such implementation at Russian industrial enterprises [19].
In addition, the most important condition for the successful implementation of BAT at enterprises in the regions is to provide them with domestic technologies developed on a scale necessary to meet the criteria of the best available technology.
In this regard, a mechanism for the development of innovative systems based on BAT should be launched at the regional level, the aim of which is to synchronize the functioning ofboth innovative institutions and institutions for regulating the rational use of natural resources, which currently operate in a relatively isolated manner. The general outline of such a mechanism with a detailed description is presented by the authors in a number of previous works [20][21][22][23]. Its main result is the expansion of the potential and promising BAT flow in the region for their subsequent introduction to the relevant reference books on BAT and their implementation at the regional enterprises, through the development of interaction of all participants in the process of creating and implementing such technologies and providing a system of incentives.
The general description of the mechanism as a whole can be applied to any Russian region. However, its practical implementation in a specific region will objectively have its own specifics, due to the peculiarities of the existing innovation infrastructure, the specialization of the innovation sector, measures to support investors and innovators developed in the region, as well as the structure of the production sector, which forms the demand for BAT.
In this regard, the purpose of this article is to develop additional practical recommendations for the Rostov region aimed at implementing a mechanism for the development of a regional innovation system based on BAT. In this regard, the Rostov region needs additional practical recommendations aimed at implementing the proposed mechanism for the development of a regional innovation system based on BAT. Recommendations for additional support for enterprises implementing BAT at the regional level, creating additional preferential instruments to attract private investment for research and development activities in the field of creating potential and promising BAT in the region, separately for small and medium-sized enterprises, the introduction of these measures and tools to the current system of business and investor support institutions in the region were presented in a number of previous articles by the authors [13,21,22].
In this article, in order to achieve the above research goal, the following tasks will be solved (research plan): -provide a general description of the proposed mechanism for the development of a regional innovation system based on BAT; -define the concepts and give a comparative characteristic of the actual BAT, potential and prospective BAT; -to form recommendations on the creation of a new institute for the introduction of BAT in the Rostov region-the Regional Center of BAT Competencies (including general methodological recommendations on its structure, the scheme of interaction of specialists of the Regional Center of BAT Competencies with other participants in the process and the identification of the most promising areas for the development of potential and promising BAT in the region); -to develop the basics of the program for the implementation of the RCC on BAT in the region; -to determine its possible impact on the main indicators of innovative development of the Rostov region and to identify priority areas of specialization of the region for the development of potential BAT and subsequent implementation of BAT.
In this article the authors focus on the recommendations for the creation of a new institution for the implementation of BAT in the Rostov region-the Regional BAT competence center, including general methodological recommendations on its structure, the patterns of interaction between the specialists of the Regional BAT competence center with other participants and identification of the most promising areas for development of potential and promising BAT in the region. These recommendations were created taking into account the needs of Rostov region enterprises in BAT, as well as the participation of the Rostov region in the development of the world-class Southern Research and Educational Center.

Analysis of Works and Problem Statement
Analysis of the scientific literature on the issues under consideration shows that there is a sufficient number of scientific studies by Russian and foreign scientists devoted to the certain components of the mechanism proposed in the article: both in the field of innovation development, stimulation of innovation and the formation of effective innovative institutions, in the field of environmentally friendly, energy efficient technologies, on the one hand, and in terms of improving the mechanism for introducing BAT in various countries, including procedures for their selection, solving problems of compatibility of foreign technologies with national technological systems, on the other hand.
Among the scientific publications on the development of innovative activities, the work of Smol M., Kulczycka J. [24], devoted to the problem of the development of innovations in the European raw materials sector, should be highlighted. Palage K., Lundmark R., Söderholm P. examine the innovative effects of renewable energy policies and their interactions (using the solar photovoltaic industry as an example) [25]. Pitelis A.T., Vasilakos N., Chalvatzis K., Pitelis C.N. analyze the possibilities of industrial policy to stimulate the transfer of technological innovation in the field of renewable energy to the OECD and to the regions of the European Union [26]. Zemtsov, S.P., Chernov, A.V. focus on the reasons for the different growth rates of different high-tech companies in Russia [27].
The financial aspects of turning the innovations into clean energy production in OECD countries are analyzed in the article by Al Mamun M., Sohag K., Shahbaz M., Hammoudeh S. [28]. The regional aspect of the development of innovations and the effectiveness of indirect support of innovations in different regions is mentioned by Klímová V. [29]. An article by Böhringer C., Cuntz, A., Harhoff D., Asane-Otoo E. [30] is devoted to the analysis of econometric methods for studying the impact of climate policy on the development of renewable energy sources.
On the other hand, there are quite a few articles devoted specifically to the problem of BAT implementation: -sectoral aspects of BAT implementation, including stimulation and preparation of BAT reference documents, are considered by a number of Russian and foreign scientists: Dijkmans R.  [31], Zakondyrin A. [32,33], Garbarino E., Orveillon G., Saveyn Hans GM 161 [34], C. Magomedov, I. Guseva [35], and others; -works devoted to decision-making on the implementation of BAT at the level of enterprises and regulatory authorities are presented by such scholars as Cikankowitz A., Laforest V. [6], Evrard D., Villot J., Armiyaou C., Gaucher R, Bouhrizi S., Laforest V.
At the same time, despite a fairly large amount of publications concerning the abovementioned issues, there is little research on combining and synchronizing the activities of innovation systems' subjects and environmental management systems' subjects in regulating the implementation of BAT (both on state and regional levels). There are no applications for specific Russian regions, including for the Rostov region.
Therefore, research to create practical recommendations for the development of innovation systems in Russian regions based on the best available technologies would without a doubt be useful.
Applications for specific regions to create new institutions for BAT implementation, adapted to the specifics and structure of their economy, the actual need of enterprises for BAT and the potential of innovation subjects, in particular for the Rostov region, are of great importance.
The lack of concrete results in this sphere in terms of practical use by Russian regions made us focus our research on making recommendations for the development of innovative systems based on the best available technologies. Specific regional applications on creating new institutions for BAT implementation, taking into account the specifics and structure of their economy, the actual need of enterprises for BAT and the potential of subjects of innovation, for the Rostov region in particular has assumed a particular urgency.

Solution Method
As research methods general scientific methods, including observation and abstraction, deduction and induction, economic, logical and comparative analysis, generalization of factual and theoretical material were used.
When identifying priority areas for the development of potential and promising BAT in the region (in the Rostov region), methods of grouping data, normative and systemic methods, methods of tabular and graphical data visualization were used.
Proposals for the creation of new institutes for the implementation of BAT in the Rostov region (Regional BAT Competence Center, BAT Process Factory) were formed using the method of analogies. As a basis for the analogy, the region's experience in the creation of similar institutions in the field of labor productivity, ensuring the introduction of lean technologies tools at the enterprises of the region, is used. In general, both BAT and lean technologies are aimed at increasing production efficiency and productivity of labor and resources, but in different ways. In this connection, the general methodological approaches to these institutes' activities (the availability of expert professionals accompanying the process of introducing technology at enterprises, their selection, personnel training, free of charge services provided by the center and the BAT Process Factory, etc.) can be adopted to the BAT implementation. At the same time, when drawing an analogy, it is necessary to take into account that in contrast to lean technologies, the introduction of BAT into the production process may a major investment and approval of regulatory authorities [13]. In addition, it should be emphasized that the analogy in this case applies only to the ways in which the experts of the RCC on BAT interact with enterprises and innovative organizations that will be involved in the process of developing and implementing BAT. The analogy is also applicable in terms of the principles of the formation of the RCC staff, as well as the financing of its activities and the services of RCC experts completely free of charge for enterprises (experts are included in the staff as permanent employees at the main place of work). In terms of the specifics of the technologies being implemented, the analogy between lean manufacturing technologies and BAT is inapplicable, since the latter differ significantly. In this regard, the experts themselves, their qualifications, and field of activity will be subject to requirements that differ from lean manufacturing experts.
When forming the general scheme of interaction between the Regional BAT Competence Center and other participants of BAT introducing processes at regional enterprises, as well as developing potential and promising BATs, a systematic approach was used, which makes it possible to highlight the main elements and participants of a common system of interaction, as well as to determine the relations between them.
When determining the effect of the introduction into the practice of regional management of a mechanism based on the creation of the RCC for BAT, a program-target approach was used, including the formation of the main indicators of a possible program for the introduction of BAT in the region, their target values, and determining their impact on the overall indicators of innovative development of the region.
Public materials of the Federal State Statistics Service of the Russian Federation and its Rostov regional subdivision, documents of the Ministry of Science and Higher Education on innovation infrastructure and the main indicators of innovative activity of the constituent entities of the Russian Federation, including the Rostov region, public records of official web portal of the executive authorities of the Russian Federation and Rostov region, as well as scientific articles by Russian and foreign economists on the issues under consideration, and longstanding scientific and practical experience and generalizations of the authors are the information and empirical basis of the materials and findings presented in the article.
The regulatory framework of the study is based on the regulatory legal acts of the Russian Federation, its regions, regulatory legal acts of the Rostov region, programs, guidelines and policy documents concerning the regulation of innovative activities and the introduction of BAT in the Russian Federation and regions.

Results
The general theoretical basis of the mechanism for managing the sustainable development of regional innovation systems based on the best available technologies was presented in a number of previous articles by the authors [21][22][23].
A brief description is given here for a better understanding of further recommendations. The proposed mechanism is built at the junction of two systems currently operating in Russian regions which are relatively isolated from each other-that is regulation of rational environmental management based on BAT (as a sector where BAT is being introduced), and regional innovation systems as a sector where potential/promising BAT and their first approbation and implementation is taking place (Figure 1).

Figure 1.
Outline representation of the mechanism for managing sustainable development of regional innovation systems based on BAT.
One of the key elements of the mechanism is BAT Reference Books, which are formed on the basis of technologies that at the moment have the best effects among the scientific and technological advances in terms of environmental impact indicators.
It is also extremely important that the implementation of BAT should be carried out at a financial level affordable for the enterprise. BAT has mandatory parameters of economic efficiency, which should also be the best. It allowed for the improvement of both environmental effective and economic efficient technologies. Thus, the mechanism for regulating the rational use of natural resources based on BAT and their reference books actually regulates their consumption sector, where direct demand is generated by enterprises, users of natural resources [13].
According to the diagram shown in Figure 1, regional innovation systems become "suppliers" of BAT in this case. The main goal of the proposed mechanism creation is to increase the number of potential BAT of domestic origin and to form a technological basis for constant modernization and greening of economic activities, and as a result, to reduce the overall negative impact on the environment [13].
The concept of a "potential BAT" was introduced for a more accurate understanding of the process such a technology "moves" from the point of the first research and development to its direct creation, experimental implementation, testing and to its recognition as the "best available" and already included in the BAT reference book, i.e., to justify the connection between the two segments of the mechanism presented in Figure 1.
A potential BAT is understood as an innovative technology that has BAT properties (improved result in terms of environmental impact parameters in the industry; economic efficiency; financial affordability; the possibility of production scaling), claiming to be included in the relevant industry BAT reference book in the future, and being at various stages of innovative development (from research to implementation) [13]. The introduction of this concept was required to define BAT as a priority research and development sphere for innovative enterprises and regional development institutions. It is the flow of such potential BAT that should be increased so that there would be competition between them, and in the end one of them would become the best.
The concept of "promising technologies" has come into use in Russian BAT implementation practice originated. It stands for technologies that have not yet been widely spread, and there is no evidence of industrial implementation of which at two facilities (enterprises) operating in the Russian Federation. In this case, the technology is recommended to be included in the list of promising technologies. As a rule, such technologies include technologies in relation to which research and development work is still being carried out or their experimental-industrial implementation is on. This concept, being essentially close to the "potential BAT", nevertheless has a significant difference-promising technologies are already included in the list of technologies, and when introduced at two Russian enterprises, they will be able to graduate to the BAT category status. Potential BAT are technologies at any stage of development, just aiming to be included in the list of promising technologies or BAT reference books [13].
Comparative characteristics of these types of BAT aiming at a more accurate understanding of their role in the mechanism for managing the sustainable development of regional innovation systems based on BAT are presented in Table 1. Thus, by introducing the specified types of BAT, it is possible to ensure the link between the innovation sector itself and the BAT implementation sector.
However, for the effective implementation of the mechanism proposed, it is necessary to provide institutional support for the interaction of all participants in the development and implementation of all types of BAT in the region, as well as their subsequent possible implementation at enterprises in other regions as well as at foreign enterprises in the future.
Concerning Russian regions, including the Rostov region, such institutional support can be implemented through the creation of a specialized structure-the Regional Center of Competence in BAT (hereinafter-RCC in BAT), which can be established on the basis of one of the existing institutions (for example, on the basis of the Innovation Agency of the Rostov Region).
The place of the RCC in BAT in the general scheme of interaction of participants in the process of introducing various types of BAT in the Rostov region can be schematically represented as follows (Figure 2). This structure can be created by analogy with the Regional Center of Competences in Labor Efficiency, already operating in the Russian regions within the framework of the national project "Labor Productivity", thus its successful experience in organizing their work would be helpful. RCC in BAT experts could provide free consulting services and project support for enterprises implementing programs to improve environmental efficiency with the introduction of existing BAT, as well as implementing developments in the field of potential and promising BAT. It is also be appropriate to include the creation of a "Factory of BAT processes" (also by analogy with the national project "Labor Productivity") under the RCC in BAT activities. It could become a place where the teams of such enterprises can be trained in tools for creating and implementing BAT for free [13].
RCC in BAT activities at the first stages (the first 4-5 years of the BAT implementation program in the region) are financed from the regional budget (if such a decision is made at the regional level). For enterprises introducing various types of BAT into their production process, the RCC in BAT experts' services should be free of charge, including support of investment projects for modernization based on various types of BAT and enterprise's staff teams training on gaining the experience and their further independent work on BAT implementation of at the enterprise. Partial self-financing could potentially be envisaged through fee-based services to enterprises that achieved satisfactory results from modernization on the basis of BAT in the first years.
An increase in the number of developed new technologies and BAT would meet the demand of local companies for innovative developments. So, innovation activity in the Rostov region is characterized by the following indicators (Table 2). As the data presented in Table 2 show the Rostov region is marked with a significant-by two orders of magnitude-excess of the number of used advanced technologies in comparison with the developed ones. At the same time, the total number of developed technologies as a whole is approximately at the same level, and the number of technologies used is constantly growing. It proves that there is local enterprises' strong need and willingness to implement these changes.
The introduction into the practice of regional management of a mechanism based on the construction of the RCC on BAT will allow to obtain a dual effect: -in terms of reducing the negative impact on the environment, modernizing and improving the energy efficiency of the production of the largest polluter enterprises through the introduction of BAT; -in terms of improving the indicators of innovation activity in the region. In particular, the work of the RCC on BAT will directly affect to the greatest extent the number of advanced technologies developed in the region, the number of patents granted for inventions. To a lesser extent, this may affect the number of advanced technologies used, but there will be a replacement of foreign technologies used with domestic technologies. In general, this will contribute to the growth of the share of innovative goods, works, services in the total volume of goods shipped, works performed, services, as well as the volume of innovative goods, works, services in the region.
In the case of the development of a program-target document at the regional level for the introduction of BAT, it can be based on the following main target indicators and program activities (Table 3). The program indicators are presented based on: -the minimum staff of the RCC for NDT, including 8 people (including 1 head). The experts of the RCC on BAT can be industry specialists-technologists in the priority sectors for the enterprises of the region for the introduction of BAT. At the same time, 4 experts will be involved in working with enterprises implementing already existing BAT (based on approved BAT Reference Books), 4-with enterprises acting as platforms for the pilot implementation of promising and potential BAT based on the developments of regional innovative organizations; -conducting by one expert of the RCC on BAT no more than two enterprises at the same time, and the requirements for supporting projects on the introduction of BAT at one enterprise simultaneously by two experts; -the duration of projects on the introduction of BAT, potential and promising BAT in 3 years, the first two of which are accompanied directly by experts of the RCC on BAT in the region, the third is implemented by enterprises independently, with the fragmentary involvement of experts, if necessary; -training from each company participating in a team of 10 people. It is also assumed that in the first year of the program implementation, the recruitment of the RCC team on NDT will take place, they will develop training programs for future participating enterprises and innovative organizations.
If the proposed minimum indicators of the program are fulfilled, the number of developed advanced production technologies in the region will grow by about 50% over the first five years of its implementation, while trends in all other factors (the general economic situation, measures to support innovative organizations, budget policy, etc.) remain unchanged.
The impact on other indicators of innovative development of the region, presented in Table 2, will be less significant and generally difficult to directly measure. For a more significant result at the regional level, it is necessary to increase the staff of experts by 2-3 times. In the case of a larger and longer-term development of events, the implemented measures will generally contribute to the improvement of almost all the indicators in Table 2, including the number of patents, the volume of innovative products and its share in the total volume of goods shipped, works performed, services. In general, a more precise determination of the impact of the implementation of the proposed measures on the future indicators of innovative development of the region requires further research.
Each of the regions of Russia specializes in its own innovative areas that can be used to develop BAT.
The priority areas of Rostov region specialization in potential BAT development, followed by their roll-out, are determined by the world-class Research and Educational Center of the Interregional Scientific and Educational Center of the South of Russia (hereinafter-the Southern REC), initiated by the Rostov and Volgograd regions and Krasnodar Territory. The Southern REC's list of activities includes three main areas: AgroTech, (creation of agricultural machinery and equipment for agriculture; creation of technologies for the production, storage and transportation of agricultural products, as well as reducing its losses, development of technologies for managing soil fertility and reducing the negative impact of man-made factors on the environment, rational waste management), FoodDesign (development of healthy food products, personalized nutrition, development of advanced gastronomy and culinary business, ensuring the adaptation of advanced food production technologies to the needs of consumers in the logic of preserving health and sustainability, as well as the creation of technologies and equipment for processing agricultural products, storage, transportation, packaging of food products), AquaTrack (development of improved methodologies for water resources management based on science and technology; introduction of new industrial intensive biotechnologies based on genetic research, disease prevention, targeted delivery of vaccines, new highly efficient, intelligent production management systems based on digital technologies; ensuring the sustainability of water resources (materials provided by the Ministry of Economic Development of the Rostov Region).

Conclusions
As priority areas for the development of BAT in the Rostov region (determined on the basis of the List of Activities of the Southern REC, according to materials provided by the Ministry of Economic Development of the Rostov Region), the following activities can be considered: -transition to environmentally friendly and resource-saving energy, increase of hydrocarbon raw materials extraction and deep processing efficiency, development of new sources, energy's transportation and storage methods; -transition to highly productive and environmentally friendly agricultural and aquaculture, development and implementation of systems for the rational use of chemical and biological protection of agricultural plants and animals.
Concentration of efforts on stimulating innovations in these areas, with their upgrade to the status of promising BAT followed by subsequent introduction to BAT Reference Books will provide innovative enterprises and organizations with strategic perspectives for going beyond the region and the country as a whole.
The development of Russian regional innovation systems based on the best available technologies can have a multiplier effect. This will not only ensure a reduction of negative environmental impact of large industrial and agro-industrial enterprises, but will also contribute to the constant modernization and increase in the energy efficiency of production, as well as will enhance the innovation actors in the region.
The implementation of the proposed incentives for the establishment Regional BAT competence center will allow the Rostov region to join the overall process of BAT implementation at the federal level, as well as more effectively implement the Southern REC establishment policy. In the case of consistent implementation of the measures proposed, enterprises participating in this process will have opportunities to increase their export potential, while innovative organizations-to increase technological upgrading of innovative activities, reduce their risks and enter the latest technology markets both in other regions and abroad.


On the enrichment of time series with textual data for forecasting agricultural commodity prices

Graphical abstract

Introduction
Time-series data are commonly applicable for future price predictions in most applications and researches [1] . Traditionally, parametric and linear models have usually been explored for time-series forecasting [2][3][4][5] . Introduced by [6] , the ARIMA model has been one of the most popular approaches for time-series forecasting in different application domains. However, ARIMA-based models do not provide good predictions in more complex scenarios related to the financial market [7] .
In order to overcome the limitations of the parametric models, non-parametric models have been proposed [8][9][10][11][12] . In particular, Machine Learning (ML) models have shown promising results with data-driven time-series forecasting models [13] . Artificial Neural Networks and Support Vector Regression are examples of non-parametric models that use only historical data to learn the stochastic dependency between the past and the future [14][15][16][17] . Nevertheless, existing studies usually learn forecasting models exploring only trends and seasonality behavior of the historical time-series.
Regarding forecasts related to the financial market and commodities is a process challenge that involves stochastic and non-deterministic aspects. For example, the factors that influence the agricultural commodity include several variables that affect prices [18] . In addition to weather information, the factors can be categorized: i) Historical and recent market data; ii) Domestic demand and supply; iii) International demand and supply; iv) Macroeconomics; and v) Political factors. The first three factors are usually contained in time-series data. However, the last factors are more complex and subjective, generally available implicitly in texts extracted from news, social networks, and reports from different knowledge areas.
Text mining techniques have been used in studies to select text features and incorporate them into time-series [11,19,20] . The general idea is to extract a structured representation of the texts and associate them with price time-series. However, there are some limitations when applying vector space model representations of texts to prediction tasks. One of the main problems is the curse of dimensionality and sparse representations, as learning models with high-dimensional representations can be complex [21] .
In order to research alternatives to these limitations, we consider a set of finite terms extracted from texts to enrich time-series with external factors available in textual information. In this work, models forecasting were used for regression tasks using three representations: Time-Series (TS), timeseries Enriched with Domain-specific terms (TSED), and only Domain-Specific Terms (DST).

Related works
Due to the variety of related works, the author divides them into three categories [22] : i) methods based only on technical information from time-series features, ii) methods based only on textual features, and iii) hybrid methods that combine textual features and technical information from timeseries. This work scope is interested in hybrid methods, combining time-series and textual features to improve forecasting models. In this sense, Table 1 presents works related to different regression tasks. The column time-series (TS Domain) represents the temporal dependence and the domain of the data; the textual representation is the vector model used to enrich the predictive task; the training vs test presents the amplitude of the data in the experimental evaluation, and the Sliding Window (SW) represents the evaluation strategy used. The works presented in Table 1 explore domain technical information to combine or analyze time-series observations. They are notably different in evaluating the test and training set, vector representation of texts, and semantic resources combined with time-series. The studies [11,26] are our publications previous to this work. It is observed that the representation models and the prediction algorithms used are different. In addition, the data sources of time-series and texts are different in this work. In general, the hybrid models presented an increase in performance compared to timeseries forecasting models. However, they have limitations, such as the curse of dimensionality and textual representations without considering important domain words. Thus, this work presents a representation of time-series enriched with specific domain characteristics for forecasting the daily prices of agricultural commodities.

Methods
This section presents the proposed method TSED, a representation of time-series combined with features extracted from a vector representation of texts. Fig. 1 illustrates the steps performed in the method.

Pre-processing
A time-series S of size m is defined as an ordered sequence of observations, i.e., S = (s 1 , s 2 , . . . , s m ) , where s t ∈ R d represents an observation s at time t with d features. In the learning stage of a forecasting model, we consider different sizes u extracted from the time-series S, process called cross validation for times-series ( Fig. 3 ). Thus, each step of forecasting we define a sequences S u = (s 1 , . . . , s u ) , where u indicates the time period of the last observation of the time-series. Each sequence S u is associated with a forecast target value y u + h , where h is the number of steps ahead, known as single-step ahead forecast with forecast horizon ( h ).
We present an approach to obtain a representation for the time-series, which considers the occurrence of specific words/terms (list of thirty-three words) in texts from the agricultural domain that can influence the time-series. Given a sequence S u , we enrich this sequence with a vector representation of texts (BoW) that calculates the occurrence of domain words in the period S u . First, we identify via time alignment all textual documents related to the sequence ( S u ) and their respective representations in the vectorial space, as defined in Eq. (1) (Keywords Set).
where KS is a subset of texts ( Q) with a text per day ( T ), and u indicates the number of days for the Frequency-Inverse Document Frequency (TF-IDF) was used to reflect how important a word is in the document collection. Then, the feature representation associated with the sequence is computed as an average vector from the document vectors, as defined in Eq. (2) (Keywords Features): The enriched representation is formed by the vector concatenation between the observations of the time-series and the Keywords features, T K(u ) = S(u ) KF (u ) . Thus, we can use an enriched training into the regression models, as presented in the next section.

Regression models
After obtaining combined representations of the time-series and texts, indicating more qualitative information from the domain, the process continues to obtain regression models. In this work, we consider that non-linear regression models are more appropriate due to the chaotic nature of the time-series that requires textual information to reduce uncertainty. In this sense, we explored the Histogram-based Gradient Boosting Regression Tree (HGBR), Support Vector Regression (SVR), Random Forest Regressor (RF), and Bagging Regressor (BR). These four models has obtains promising results in several time-series forecasting works [9 , 10 , 29 30] .
A model is presented to a non-linear SVR forecast function to estimate a time-series [31] . In this work, the optimization process is done by estimating the multipliers α j and α * j , which represents the minimized objective function Eq. (4) .
where K is the kernel, defines a margin of tolerance where there is no given penalty for forecasting errors; and C is a previously defined positive constant that controls the penalty for observations that exceed the margin; which also helps to avoid excessive overfitting. The most common kernels are Polynomial, RBF, and Sigmoid. In this work, we consider Kernel RBF to have obtained the best results in the initial experiments.
Histogram-based Gradient Boosting Regression (HGBR) is inspired by LightGBM [32] and is a technique for training faster decision trees used in the gradient boosting ensemble. Model HGBR can be interpreted as: where F m is built on a stagewise fashion, and each F is (LightGBM) a decision tree that executes M times using T K attributes. Random Forrest is an algorithm that handles large volumes of data within a relatively short computation time [33] .
Random Forests (RF) for regression are formed by growing trees depending on a random vector such that the tree predictor h ( TK , ) . The output values are numerical, and we assume that the training set is independently drawn from the random vector Y, T K distribution. The mean-squared generalization error for any numerical predictor h (T K ) is: The random forest predictor is formed by taking the average over k of the trees h ( TK , k ) . We kept the recommended 1 number of trees (k = 100) . In order to reduce the size of the model, we changed the maximum tree depth parameters to four.
The Bagging Regressor (BR) is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregates their predictions (either by voting or by averaging) to form a final prediction [34] . Assume we have a procedure for using learning set to form a predictor ϕ(T K, k ) , were is learning set (y n , T K n ) . So, BR can be defined as: where (B ) is base estimator to fit on random subsets of the dataset T K, ϕ is predictor with repeated bootstrap samples, and a v is average all predictors ϕ (T K, ϕ (B ) ) . In this work, we consider the SVR as the basis of the estimator and the number of estimators ( B = 10 ). The presented regression models were used to investigate the effectiveness of incorporating domain-specific terms in time-series prediction tasks.

Setup for experiment evaluation
This section presents evaluations of experiments using four regression models to compare the predictive performance of three representations: time-series (TS), time-series Enriched with Domainspecific Terms (TSED), and Domain Specific Terms only (DST). For assessing model performances and validity, the Mean Absolute Percentage Error (MAPE) statistical indicator was used.
The time-series data source used in this experiment is from the Chicago Board of Trade (CBOT), available at CME 2 Group's website. Fig. 2 presents soybean prices series. We use the textual data extracted from the website Soybean & Corn Advisor 3 . Since 2009, the website has provided daily news and information on soybean and corn production related to the South American growth cycles, climate, infrastructure, land use, ethanol, and alternative fuel production. Fig. 2 presents three examples of abrupt fluctuations in price series. By empirically analyzing the periods of price series that change a trend (high/low) or abrupt fluctuations in a few days, we observe a high occurrence of keywords in the news. Table 2 describes domain-specific keywords to enrich  predictive tasks, the dataset period, the size of time-series datasets, and information about textual data. As shown in Table 2 , the number of days in the time-series is different from the number of news items. Therefore, the term "no news" was considered for training and testing on days when there was no news on the site to maintain alignment between time series and texts.
To evaluate the proposed model, we use the Mean Absolute Percentage Error, presented in the Eq. (8) .
where n is the number of testing samples, y (i ) is the actual value of each dataset, and y (i ) is the forecasting value of the corresponding futures price data.

Experiments and results
Considering the representation of the enriched time-series, expressed in Eq. (3) , Fig. 3 illustrates how the method was applied in this work.
The cross-validation for time-series was used to evaluate the proposed model in the experimental evaluation. This strategy is mostly used in time-series forecasting contexts [35] . The first training step was performed with 30% of the data ( F 1 ), and at each cross-validation iteration, a day is added to the training to predict the next step ahead. The variable y in Eq. (8) represents the forecast of commodity prices h days ahead, and n represents approximately 1230 forecasts (daily) performed in the test stage. As presented in Section 3 , four regression models were used to compare the predictive performance of representations. The Table 3 shows the set of hyperparameters used 1 .
After performing several structured experiments with different configurations, the hyperparameters of Table 3 were defined. Thus, Table 4 presents the MAPE values obtained in the forecast steps. In the experimental evaluation, five sizes of h were considered, that is, predicting one to five-time steps ahead. Values in bold are the smallest MAPE values of the regression model, and underlined are the smallest values of each representation (TS, TSED, DST). Fig. 4 shows the graph    ,022 1,010 7,611 1,382 1,352 7,560 1,696 1,660 7,568 1,947 1,908 7,528 2,147 2,104 7,506  RF  1,108 1,107 1,082 1,437 1,434 10,725 1,733 1,728 10,683 1,967 1,964 10,638 2,150 2, According to the results presented in Table 4 , the corn price forecast considering the TS representation, obtained the lowest MAPE values (values in bold) in almost all configurations ( h ). For example, analyzing the results of h = 1 , the SVR model with the TS representation had the lowest MAPE value with 1,145%, the RF had the lowest value for the TSED representation with 1,168%, and the SVR model had the lowest MAPE for DST representation with 6,056%. This pattern of the lowest  MAPE value of the regression models for each representation is repeated for other forecast horizons ( h ). Analyzing the results of the soybean price forecast in Table 4 , the HGBR model obtained the lowest MAPE value for the TS and TSED representations for h = 1 , with values 0.982% and 0.997%, respectively. This pattern of the lowest MAPE value of the forecast models for each representation is not repeated for other forecast horizons h . However, the SVR model obtained the lowest MAPE values for the DST representation in all h horizons, with values 7,611%, 7,560%, 7,568%, 7,528% and 7,506%, respectively.

Discussion
As presented in the experiments and results section ( Fig. 4 ), DST representation predictions obtained an average of the price series. Thus, in this discussion, we will focus on analyzing the results of the TS and TSED representations that performed best (ie, results obtained from the underlined values of Table 4 ). In addition, Table 5 shows the number of days the representations had a lower MAPE value than the others.
Analyzing the results of corn in Table 5 , TS representation obtained 547 predictions in which the MAPE value was lower than the TSED, 418 predictions in which the TSED obtained a better result compared to the TS, and 272 in which both representations obtained equal values for the horizon ( h = 1 ). During the test phase, some predictions obtained the MAPE value equal to zero (0%), represented by dots (red and blue) in Fig. 4 . In this case, TS and TSED representations obtained 69 and 57 very accurate predictions, respectively. The best performance of TS about TSED is repeated with a 16,7% superiority average in all forecast horizons ( h ).  (3), export (2), harvest(2), high(1), import (7), large (2), planting(1), price(1), rains(1), record (2), sales(2), soybean (18) (1), export(4), good(1), import(4), increase(1), large(1), low(1), production(1), record(1), soybean (1) The results of the soybean price forecast in Table 5 are similar to the corn results, where the TS representation obtained a more significant number of daily forecasts in all forecast horizons h . However, the superiority of TS over TSED is lower, with an average value of 7.6%. On the other hand, the number of predictions in which the TSED MAPE values were equal to the TS obtained a lower number.
We investigated the frequency of terms extracted from the texts and included in the time-series regarding the forecast days with a MAPE error equal to zero. The proposed representation performed well on days with abrupt intraday fluctuations in the price series. The Table 6 presents examples for h = 1 , where the date represents the day of publication of the news/headline and data prediction; the values in percentage represent the intraday oscillation; and the frequency that domain words occur in the news.
According to the data presented in Table 6 , the words corn, export, increase, and production have frequencies of 1, 3, 1, and 4, respectively, in the news published on 01/30/2020. Therefore, these words were used as resources in the TSED vector representation for a corn price forecast on 01/31/2020. The Term Frequency -Inverse Document Frequency (TF-IDF) measure was used to measure the importance of the word about text documents. The TF-IDF value is a weighting factor that increases proportionately as the number of occurrences in a document increases. Thus, words with high frequency in the texts had higher values, and words with little occurrence had lower values in the TSED representation. However, the TSED representation is based on independent words and does not express word relationships, text syntax, or semantics.
We also investigated the performance of the price forecast for the TS representation on the dates mentioned in Table 6 . The TS representation did not perform well for the mentioned days. Furthermore, on the days when the TS representation performed better than the TSED, three situations often occurred: i) there was no news published on the dates; ii) they did not have much frequency of domain keywords; iii) the news content did not accurately represent the domain of the application. Regarding the last two, representation models that consider the semantics, linguistic structure, and context of texts can be proposed to mitigate this limitation, such as neural language models.

Conclusion
Existing models have demonstrated a gained accuracy in predicting time-series. However, many studies do not consider external factors like market sentiment, politics, and other aspects. This work presented a time-series representation model enriched by Domain-Specific Expressions (TSED) to investigate these limitations. The proposed model was built from the matrix attribute-value representation, concatenated with the time-series, and applied in four regression models. Experimental results have demonstrated that ST representations perform better in most configurations. However, the TSED representation in some scenarios had better predictions than the TS.
In general, time-series representation models that consider textual information will hardly perform better at all prediction stages. However, the proposed model can be an alternative to help predict abrupt oscillations in time-series. Furthermore, enriched representations can contribute to the explicability of predictive models (black box). Future work can be carried out to extract more details from the texts, such as named entities, causal relationships, and techniques that consider semantic aspects to enrich the time-series. These techniques can help predict abrupt changes in time-series and explain predictive models.

Declaration of Competing Interest
The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.


THRESHOLD EFFECTS OF OIL PRICE AND OIL EXPORT ON TRADE BALANCE IN AFRICA

Article history Received: October 11, 2021 Revised: December 29, 2021 Accepted: December 30, 2021 The impact of various macroeconomics variables on trade deficit has been studied in large OPEC (e.g., United Arab Emirates and Kingdom of Saudi Arabia); however other African countries are still needed to be studied. This study uses the transmission oil price (OP) changes to various economic sectors to examine the threshold effects of OP and oil export on trade balance in African OPEC members (Algeria, Angola, Libya and Nigeria). This study applied Pedroni cointegration test to establish the cointegration relationship among different macroeconomics variables by using three different proxies of OP. The dynamic panel models were used to examine the long-run impact of OP changes and threshold analysis. The study found that increase in OP and oil export positively encouraged import while exchange rate depreciation is significantly discouraged import. The study found that the threshold effect of oil export on the trade balance, when oil export is above a certain threshold, the impact is higher than below threshold. The aforementioned countries have to take into account that there is a threshold level and can increase oil export to improve the trade balance.

INTRODUCTION
The global falling in oil price (OP) witnessed in the middle of 2014 brought severe issues to the African countries to maintain favorable trade balance (TB), especially in oilexporting countries. Occasionally OPEC (The Organization of the Petroleum Exporting Countries) members are discussing on the quantity of oil production/export shared to each member. In the earlier years, most of the African OPEC members are trying to maximize their oil production by getting a higher quota of exports. Moreover, the increase of oil export (OX) does improve their TB, recently it's seemed to be changed. An increase in oil production/export was unsuccessful in improving the TB, and this has caused a drop in OP. The oil exporting countries voluntarily agreed to reduce the oil production/export in order to have higher OP. The African OPEC members are uncertain about whether will benefit from an increase in OP or an increase in OX. Which strategy will successfully improve the overall TB? The response of exchange rate policy towards OP shock depends on how the economy relies on oil export. If the economy heavily relies on OX, a decrease in OP will lead to a decrease in oil revenue. Hence, the policy makers may devaluate their currency in order to make her export more attractive. However, the outcome on TB will be ambiguous because the decrease in oil revenue will lead to a decline in the TB. In the theory, a devaluation of currency will result in an increase in export and decrease in import, therefore this increases the TB. But in the oil-dependent economy, devaluation of currency seems to be insignificant for export as the devaluation cannot influence the OP. Due to the high dependency on oil, about 90 percent of their foreign earnings and oil exports consist of more than 80 percent of their total exports. While oilimporting countries falling in oil prices will improve their current account balance reported by (World Bank, 2014;Baffes et al., 2015). The main factor for the slowdown is weak in OP in the world market. The way these shocks can be absorbed is to associate with the effective monetary policy intervention. Although the adopting appropriate monetary tools that will stabilize the economy out of external and internal disequilibrium. The decision faces an argument among policy-makers on the effectiveness of those instruments in controlling the overall economic activities. Mostly this depends on the nature of the economy, it also depends on how the economy relies on the oil export (Feussi, 2013). External shocks can be transmitted into various economic channels such as via inflation, trade and output. Most of the oil exporting countries in African has been facing difficulties during decreasing OP shock. The central authority always delays responding when OP drops due to the uncertainty and some assume that the relationship is linear. Thus, the policymakers have treated the shock indifference between the negative and positive changes. The increase in oil prices controlled the weakening of GDP, while the decline does not accelerate the economic activities. Several compensations were explained by the economists (Mork, 1989;Lee et al., 1995;Hamilton, 1996). Their explanations addressed the presence of an inadequate specification of the models and stressed to restore OP, macroeconomic relationship, nonlinear specifications. Stress and the uncertainty of the financial markets are the sources of this asymmetry (Ferderer, 1996). Among the others, response of the monetary policy of OP fluctuation is the major cause of asymmetry (Balke et al., 2002;Brown and Yucel, 2002). The oil supply has become relatively exogenous, which significantly affects the price of oil fluctuations; this explanation fits the world experience in the 1970s. Alternatively, flexible oil supply case impersonates another case without oil supply fluctuations. The reason behind this is obvious if the supply of oil responds endogenously to determine the organization of petroleum exporting countries (OPEC), which is the quantity to supply, the world oil supply will respond. Similarly, the relative price of oil is expected to regulate (Backus and Crucini, 2000). In the literature, there are different views of evaluating the impact of OX changes and OP. For instance, the quota system shared among the OPEC members could not be depicted by the conventional supply-price nexus (Watkins and Strelfel, 1998). Moreover, a contradictory relationship was found in previous literature between OPEC production and OP (Ramcharran, 2002). In the same year, Krichene (2002) found that the supply price elasticity for oil production is low and the following year Cleveland and Kaufmann (2003) noted that the oil supply rise lead to the decrease in OP. In contrast, Kaufmann et al. (2008) observed that the positive relationship between real prices and oil supply. The study observed the selected variables among the OPEC member countries. Figure 1 reveals the association between import, export and trade balance (TB) for the African OPEC Members from 1970 -2013. Generally, the total export and total import of these countries increased over the years. Periodically, these countries are experienced balance in trade whereby the trade balances fluctuated around zero. However, these countries experienced a trade surplus since 2000. This can be observed as oil price (OP) increased from the year 2000 to 2008 in which the trade surplus of these countries increased substantially. This can be observed during the falling of OP in 2008 to 2009; all these countries experienced a decrease in TB, but with different severity. Nigeria has the smallest drop as compared to other countries. Later, the OP increased from 2009 to 2011 in which the TB gradually increased for Algeria, Angola and Nigeria, except Libya. Again, Nigeria has the largest improvement in TB as compared to other countries. Libya and Algeria have a different pattern. The TB of Nigeria dropped in 2014 due to the falling of OP. However, Algeria had a sharp decrease in the TB in 2014. These asymmetric responses of TB may be caused by different policy responses towards OP shock. The response of exchange rate (E) policy towards OP shock depends on the changes in OP. A decrease in OP may push the policymakers to devaluate their currency in order to discourage import and encourage export. In order to allow the nominal E to depreciate, the aim is to lessen the demand for foreign products and reduce pressure on the current reserves. As OP dropped, the oil exporting countries' economies that are depending on the importing of finish goods for their domestic consumption are distorted due to the short supply of foreign currency. The extra demand for foreign currency pushes to devalue the domestic currency, while the oil prices were at the peak, the currency was devalued. These declarations clearly showed the possibility of non-linearity effects of OP. These oil exporting countries normally depend on importing finished goods for their domestic consumption. These countries would be inferior when their currencies were devalued (Jibrilla, 2010), especially those that are in the cartel, which has required production quota. As it can be witnessed, Algeria, Angola, Libya and Nigerian currencies were depreciated around the 1990s and those that improvement was not shown in the TB. Figure 2 illustrates how the OP changes influence E and TB in Algeria, Angola, Libya and Nigerian. These countries had a different response to the E and TB when OP changes. The TB followed the movement of OP changes, especially in Libya, except during civil war than other countries. The more complex relationship occurred during 2008 when the TB of those countries dropped more than the proportional changes in OP, particularly in Algeria, Libya, Angola and Nigeria. Based on the response of the E of those countries during the period, Libya had a more stable E than other countries, followed by Angola, Algeria and Nigeria. In general, from the period of investigation, the E showed more depreciated than appreciation even when the OP increased.   Figure 4 the relationship between TB and OP of African OPEC members. The existing argument in the OP changes literature is allowing giving more insight, contrast and good understanding the process on how the OP shocks have been previously observed. There are many different methods have been conducted that applied in the previous researches in order to investigate how economic variable indicators are affected by the OP shock. Among of these arguments, especially the OP shock impacts are symmetry or asymmetry still debated and to what extent oil prices are endogenous. The greatest contribution of this paper is the considering the theoretical framework of how OP is transmitted into a different macroeconomic variable with the monetary policy response. Most of the early studies of OP shock are usually judged based on the standard linear time series methods, Granger causality which may leave certain properties of OP changes unexplained. These methods can examine the shortrun and long-run relationships in a linear way.  Therefore, the method does not have the power to capture the possibility of nonlinearities. This research adopted recent advance methods of the well-known ARDL model developed by Pesaran and Shin (1998); Pesaran et al. (2001). These methods are developed to capture both long-run and shortrun asymmetries called non-linear ARDL cointegration approach (NARDL) by Shin et al. (2014). The test of asymmetric adjustment by Enders and Siklos, (2001) were applied for the testing of asymmetric adjustment. In the panel cointegration Tests by Pedroni (2004;, the MG and PMG proposed by Pesaran and Smith (1995) and Pesaran et al. (1999), dynamic OLS by Kao and Chiang (1999) and FMOLS developed by Pedroni (2000) are best way to asses asymmetric supply responses.
Research is scanty in the literature of oil price (OP) and trade balance (TB) nexus, among others Le and Chang (2013) and Schubert (2014), which studied the association between OP shocks and TB. Merchandise TB is effect by oil demand and supply fluctuations, the magnitude of the shock can be higher based on its origin, also heavily dependent on the reaction of the non-oil TB (Kilian et al., 2009). Oil users have to pay more due to hikes in oil prices which may affect to decrease their export revenues (profit) for energy-intensive goods and services. In the short-run their current account will be automatically declined while in the long-run, the trade deficit will be decreased due to improvement in the non-oil TB. Meanwhile, policy adjustments may further amplify these effects (Kilian, et al., 2009). Moreover, escalations in the oil prices positively benefit those producers who used energy intensive as an input in their production and manufacturing process. If the countries have the ability to produce more and export to oil producing countries since that time that has enough fund. Oil demand and supply fluctuations have effects on merchandise TB, to what extent, will be determined based on the source and nature of the shock, and is highly depended on the reaction of the non-oil TB (Kilian et al., 2009). In short, lower exports and higher imports are associated with increases in oil prices, which in turn can lower the current account balance (Abdelaziz et al., 2008;Chortareas et al., 2011), or even worsening the TB (Qianqian, 2011). An increase in world oil prices in oil-exporting countries improve the TB and hence higher current account surplus (Adebiyi et al., 2010). For instance, Yildirim and Arifli (2021) find that oil price shocks adversely impact on a small oil-exporting economy. They found that decline in OP deteriorates TB, increases inflation, causes a currency depreciation and falls economic activity. Oil prices have key role to play in examining exchange rate (E) movements (Golub, 1983;Krugman, 1983). It's also understood from the theory of exchange rate when the OP rises exporters of crude oil are taking advantage in appreciation of their currency and vice versa (Sheehan and Kelly, 1983). Countries that are importers experience depreciation due to the high patroness of foreign products into their economy resulted in more demand for foreign currency. Therefore, rises in oil prices is a favorable development of oil producing countries, while falling in OP for the buyer's countries (Jiménez-Rodríguez and Sánchez, 2005;Setser, 2007). Theoretical relationship between E and TB has long been established (Kreuger, 1983). The trade deficit might worsen if the local currency appreciates and usually countries do opposite. Thus, countries devalue their currencies to improve TB by having more comparative advantages and international competitiveness (Bahmani-oskooee, 2001; Bahmani-oskooee and Fariditavana, 2015). The theoretical argument stated that an oil producing nations could experience appreciation in their E movement during increases in oil prices and depreciation when OP dropped (Golub, 1983;Krugman, 1983;Corden, 1984;De Grauwe, 1996;Zhou, 1995;Chaudhuri and Daniel, 1998;Akram, 2004;Aliyu, 2009;). An OP shock adjusts the oil TB, the E fluctuated in other to stabilize and balancing the economy, or the non-oil TB adjusts to stabilize net foreign assets. Therefore, when positive oil shock occurred in an oil producing nation, the E appreciates while the reverse when OP dropped (Babatunde, 2015). The relationship of currency devaluation is not linear rather Jshape and added the response of devaluation on the TB into two different dimensions. Because of the depreciation of the domestic currency, the TB will respond harmfully in the shortrun, while the TB will improve in the long-run after the devaluation. These ideas give the possibility of the mechanism it was claimed that in the short-run TB respond negatively is due to price effect (Bahmani-oskooee, 1985). The elasticity model has explained the possibility of the theoretical relationship between TB and E (Kreuger, 1983). The TB will eventually improve to better level compare to before devaluation (Backus et al., 1994;Bahmani-oskooee and Fariditavana, 2015;Rose and Yellen, 1989). Although there is another condition that has to fulfill before this theory hold by Marshall-Lerner if the PEDmx >1 then the depreciation of local currency will improve the TB deficit while if the price elasticity of export and import is less than one PEDmx <1 then the devaluation of currency will be worsening in the TB (Bahmanioskooee, 1985). Le and Chang (2013) investigate the role OP on trade imbalance in 3 Asian countries which are signified different distinct characteristics considering of oil product are chosen as oil importer: Japan, oil refinery: Singapore and oil exporter: Malaysia. They used monthly data from January 1999 to November 2011 and applied Toda and Yamamoto, 1995 causality approach and generalized impulse response. They found the evidence of short-run dynamics and the longrun causal nexus between oil prices to both trade balances, namely oil and non-oil trade balances. In the research of Edwards and Levy (2005) were able to found an asymmetric response in 183 countries panel analysis used Generalize Movement of Moment method, output response small during positive shock while during negative is more. Hansen and Seo (2001) found similar results that confirm the strong evidence of threshold effect in 100 to 250 countries in two regimes. Chen and Hsu (2012) in their panel analysis consist of 84 developing and developed countries used annual data were utilized within the range of 1984 to 2008. They found that vigorous and robust evidence that flows of international trade have a negative effect on trade when OP shock is significant, conclude that OP shocks hurt globalization in general. Hassan and Zaman (2012) applied ARDL to analyze the annual data ranging from 1975 to 2010 and revealed that the fluctuation of international OP disturbs the TB have created serious noticed among the policy makers around the globe which has dynamic effects in Pakistan with the negative and significant relationship. Timilsina (2015) applied general equilibrium model include multi-country and multi-sectors of both developing and developed world to examine the impact of OP increases in 25 countries and 28 sub-sectors division. They further noted that OP increases have a significant impact on global trade patterns. Countries with highly depend on energy intensive would hurt more than the in labor intensive countries. Aydın and Acar (2011) developed TurGEM-D and applied to the Turkish's economy revealed that OP fluctuation has a significant impact on major macroeconomic variables namely consumer price, GDP, and TB. Ju et al. (2014) used yearly data from 1983 to 2012 to examine the macroeconomic effect of OP in China found that import and export may be more significantly affected by the severe OP shock. Other findings emerged with similar results that in a situation where simultaneous multiple shocks arise, non-oil balance in general equilibrium affected by oil shocks, different sources of OP shocks are related with a different transmission channel (Bodenstein et al., 2011). With these findings, clearly, indicate the important role of the non-oil TB in order to stabilize the economy after OP fluctuations occurred. Backus and Crucini (2000) found that TB response differently when the OP shock occurred not like other shocks. OP is the main factor effecting the term of trade since 25 years although its quantitative role varies over time. Dynamic general equilibrium model predicts that the economy reacts differently to oil supply fluctuations than to other crises. Bollino (2007) resulted that the higher the OP, the higher the U.S. deficit continues to grow with China. Adewuyi and Akpokodje (2010) used Nigerian annual data from 1973 to 2006 and estimate the data using OLS and GMM. They conclude that trade liberalization has not brought enough trade flows to Nigeria. Lutz and Meyer (2009) found that the advancement in international competitiveness, Germany reduced adverse impacts of increases in energy prices, brings structural change from consumer goods to investment goods. Meanwhile, other findings suggest that despite the encouragement of integration oil prices is opposed it. Chen and Hsu (2012) revealed interesting findings that when oil prices fluctuated, the international trade flows will be dropped, then conclude that OP fluctuation harms globalization. Korhonen and Ledyaeva (2010) show vigorous and robust findings that OP shocks are making oil exporters have more advantage, a positive oil shock leads to increase OP by 50 percent, Russian GDP will improve about 6 percent. Also Le and Chang (2013) categorized countries into three different variety: Malaysia oil exporting, Singapore oil refinery and Japan oil importer used monthly data from January 1999 to November 2011. Found that OP shocks cause the overall trade balances to improve in Malaysia. The positive OP shock seems to increase the oil revenues in order government to finance their expenditure and it contributes to the long-run trade surplus. Furthermore, Sato and Dechezleprêtre (2015) (Allegret et al., 2015) used global VAR model that allowed dependencies between countries. They combined a sample of 30 oil importing and oil producing countries within the period of 1980-2011 and confirmed that the main adjustment mechanism to oil fluctuations is based on the trade channel. Bao (2014) achieved that there are different impacts between the short-run and the long-run coefficient value. For instance, they found that oil prices rise by 1 percent it causes E and TB a reduction of 0.12 percent and 0.79 percent respectively in the long-run. While in the short-run, international E and oil prices are positively affected Vietnam TB which shows that there is a possibility of asymmetric response. Nicita (2013) studied 100 countries in panel analysis used fixed effect model and the data range of 10 years started from 2000 to 2009 short-run has found that E volatility in the short-run is not a major concern. There is a lot of the literature confirmed the evidence of positive relationship between OP and E (Amano and Van Norden, 1998;Chaudhuri and Daniel, 1998;Olomola and Adejumo, 2006;Benassy-Quere et al., 2007;Chen and Chen, 2007;Huang and Guo, 2007;Nikbakht, 2010;Englama et al., 2010;Adeniyi et al., 2012;Beckmann and Czudaj, 2013). Certain literature is found their interaction is negative (Akram, 2004;Huang and Guo, 2007;Ghosh, 2011). Some of the previous literature showed the evidence of a different associationship between OP and E (Amano and Van Norden, 1995;De Grauwe, 1996;Kutan and Wyzan, 2005;Lizardo and Mollick, 2010). Possibly, the different outcomes in the literature could occur by the use of linear models, which indirectly assumed symmetric properties of an OP and exchange rates positive and negative changes. Bouoiyour and Selmi (2015) testified trade performance and E and in Tunisia and Morocco regarding what have experienced so far using ARCH and GARCH model in the period of 1996Q1 to 2009Q4. They find that the effect of price differential volatility on export is greater than the nominal E by a huge margin in terms of the determination of increasing time. The relationship shows more complicated in Morocco it is negative and significant and so beyond greater in Tunisia. Also, Nicita (2013) have found that E instability in the shortrun is not a major concern in his research of 100 countries, panel analysis using fixed effect model and the data range 2000 to 2009 also found E misalignment. Yilmaz (2012) tried to investigate whether E is a shock absorber or shocks on it on in Turkey collected data from 1990 to 2009 used structural vector autoregressive analysis found E volatility during precrisis is actually nominal shock, also the huge proportion of E variability would be contributed to supply and demand shock in the post 2001 crisis era, found that during post-crisis era there is sizeable critical role of E stability, absorbing these shocks and therefore require opposed monetary policy action. Chipili (2014) aimed to investigate the foreign exchange intervention and E volatility in Zambia, data index was employed in the research are in USA dollar, using GARCH model findings reveals that foreign currency intervention is the statistically weak negative impact on E volatility. Another study by Huang and Guo (2007) used Structural vector autoregressive model to determine the role of OP shock on real E in China, there outcome disclose that real OP shock would cause to a slight long-term appreciation of the real E, because China is more reliant on importing oil than their trading partners. Ramcharan (2007) studying 67 developing countries between the year 1980 to 2000 VAR models were adopted and discovered that there is significant indication that E flexibility assists an economy to absorb the shocks. Artis and Ehrmann (2006)

METHODOLOGY
This study applied panel strong balanced data comprising of specific spot oil price (OP) of individual countries (OPC), consumer price index (CPI), import (M), export (X), trade balance (TB), oil export (OX) exchange rate (E), money supply (M2), and gross domestic product (GDP). The sample countries Algeria, Angola, Libya and Nigeria have been based on having shared the same continent, OPEC members etc. The OP data was used individual oil price from that country Algeria (Zarzatine), Angola (Cabinda), Libya (Brega) and Nigeria (Bonny Light). The inflation was proxy with average consumer price index (CPI), import was proxy with total import of goods and services percentage of GDP, export was proxy with the total export of goods and services percentage of GDP, TB was calculated based on total export of goods and services minus total import of goods and services, OX was proxy with the total crude oil export of a particular country, E was proxy with the average official exchange rate against USD, money supply was proxy with M2 USD and economic growth was proxy with GDP current USD. The sample period in this study is based on the availability of data also was converted to natural log.

Data Description
Descriptive statistics and correlation matrix test present the nature of the data of each variable and the relation to others in the oil price (OP) and TB nexus. 14163.14 -7482.445 79461.92 Note: IV=investment, OX = oil export, E=exchange rate, OP = average oil price of Brent, WTI and Dubai, OPEC = OPEC reference oil price, OPC = specific oil price for the countries, CPI = consumer price index, M = import, X = export, TB = trade balance, n = 4, T = 20 and N = 80.

Econometrics Technique
The empirical study examines the impacts of OP on trade balance (TB) in four African OPEC members. From the theoretical framework was adapted from the study of Brown and Yucel (2002). OP transmission channel into TB was extracted and presented as Figure 5 implies that TB is affected by OP, CPI and exchange rate and can be presented by equation (1)  Where: tr* is a vector of import, export and TB cpi is consumer price index and e is the exchange rate.

RESULTS AND DISCUSSION Panel Unit Root Test
This study used five different unit-root test namely Levin et al. (2002), Im et al. (2003), Breitung (1999) and Fisher-ADF and Fisher PP. The tests were firstly carried out with an intercept and secondly with an intercept and linear trend except for Breitung.

Panel Cointegration Results
Based on the unit-root test results, the variables are stationary after converted to first difference, therefore this study further to determine the long-run relationship among the variables in the models of OP and TB. The Pedroni panel cointegrated techniques was used as in the previous objective. OP and oil exports are used interchangeable in each model while import, export and TB are the dependents variables. Table 3 presents  the panel cointegration results for OP and TB nexus and Table  4 present the OX and TB nexus. The cointegration result in both tables reveals that three to four out of seven alternative test statistics in the each model rejected the null hypotheses of no cointegration at the 1% and 5% level of significance, except for one model with trend when import was the dependent variable. So generally, the OP models and the OX models with the three dependents variables (import, export and trade balance) are cointegrated both in the within the dimension and between dimension in African OPEC members Long-run. Table 5, 6 and 7 present the long-run estimated coefficients of each variable in the models and their level of significant in the OP and import, OP and export and OP and TB. The fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimators were used to detect the long-run parameters. This study used three different proxy of oil price OPC, OPEC and OP and estimated with two different methodologies to ensure the robustness of the findings. From Table 5, it can be observed that all the three proxy of OP are positive and significant at 1% in the both estimators. When countries oil price (OPC) was used, the impacts of OP on import become less (0.93 and 0.96) compare with OPEC reference OP (0.94 and 1.04) and the coefficient of OP oil price (0.97 and 1.06). The coefficient of OP oil price is the higher in both estimators. As shown in Table 6, the three proxy of oil price has positively and significantly impacted the exports. Generally, the coefficient of OPC, OPEC and OP in export models are slightly larger than one. Table 7 which is the TB models, the three proxy of oil prices is positive and significant at 10% in FMOLS and 5% in DOLS estimators. The coefficients of OPC, OPEC and OP in the TB models are the lowest compared with the import and export models. Moving to the control variables in both models, the CPI is positive and statistically significant in import model. In the import model with FMOLS estimator, the exchange rate (E) is negative and significant at 5% which shows that exchange depreciation is discouraging import while in the DOLS estimator was positive but statistically insignificant. In export and TB models, FMOLS and DOLS estimators show that the CPI and the E are statistically insignificant. The results imply that the CPI and E are not influencing the export and TB. The results are justified from previous study that oil prices Granger-cause the US dollar exchange rate (Albulescu and Ajmi, 2021). The long-run estimated results of the oil export (OX) models. It can be observed that the long-run coefficients of import, export and TB models are positively correlated with OX at 1% significant level. In both estimators, the results are similar and consistent. Moving to the control variables in both models, the CPI and E are statistically insignificant in import and TB model, while in the export model when FMOLS estimator is used the coefficient of CPI is negative and significant.

Threshold Effect of Oil Price and Oil Export on Trade Balance
Next, this study intended to find out the threshold level for OP and oil export (OX). The justification of choosing the two variables is that this study realized that from the long-run results, the only significant variables influence the trade balances are OP and OX in African OPEC members. Table 8 presents the threshold results. In the first model where OX was used as threshold variable, it is found that there is a threshold effect as the probability for 10.7731 threshold value is significant. The results indicate that the impacts of OX on TB have a different dimension below and above the threshold. The results are in agreement with previous studies (Qasim et al., 2021 andYildirim andArifli, 2021). In general, both the impacts of OX are positively related to trade.       where asterisk (*) shows 10 percent level of significance, asterisks (**) denote 5 percent level of significance and asterisks (***) represent 1 percent significance level. where asterisk (*) shows 10 percent level of significance, asterisks (**) denote 5 percent level of significance and asterisks (***) represent 1 percent significance level. Notes: Figures in parentheses are t-statistic values where asterisk (*) shows 10 percent level of significance, asterisks (**) denote 5 percent level of significance and asterisks (***) represent 1 percent significance level. The dependent variable is the log of trade balance. Standard errors are given in brackets and the bootstrapped p-value in parentheses asterisk (*) shows 10 percent level of significance, asterisks (**) denote 5 percent level of significance and asterisks (***) represent 1 percent significance level. Following Hansen (1999), each regime has to contain at least 5% of all observations. 1000 bootstrap replications were used to obtain the p-values to test for the threshold effects.
But the impact of OX above the threshold is higher than below the threshold point. The results illustrate that the TB in African OPEC members' are more favorable when OX is increases or than decrease. The results can be justified from previous study (Salisu et al., 2021). In explaining the control variables, the CPI and E are insignificant in the threshold model, this is also confirmed and robust the previous results in the FMOLS and DOLS estimations. In the second model where OP was used as threshold variable, it is found that the threshold effect is insignificant. This shows that it not always increases in OP is enhance trade balance surplus while additional OX above a certain level is encouraging trade balance positively.

CONCLUSION AND POLICY IMPLICATIONS
This study examined the impact of oil price (OP) on trade balance (TB) and concluded that the exchange rate (E) depreciation significantly discourages import but insignificant in export and TB counterpart. The results provided more insight on how E works in oil depending countries and how devaluation can make foreign goods more expensive while the price of domestic export (crude oil) is not affected. Hence, the devaluation in oil exporting country currency cannot make its crude oil cheaper. In the threshold analysis, this study concluded that the impact of oil export (OX) on TB has threshold effects when OX is above the threshold and the impact is higher than below the threshold. For countries that are highly dependent on OX, the increase of OX will further improve the TB. For countries that are not highly dependent on OX, the increasing in OX will slightly improve their TB. However, the threshold effects are insignificant when OP was used as threshold variable. This study found that there are robust policy suggestions to the policy makers that heavily rely on OP. This is done in order to gain understanding on the dynamics effects of OP, OX and E policy. The E depreciation or devaluation is an ineffective measure to improve the TB. It's significant in discouraging import while insignificant in encouraging export and TB as the devaluation or depreciation in domestic currency cannot make the domestic crude oil cheaper in the world market. The policymakers have to take into account that there is a threshold level to be considered when OX is above the threshold and the impact is higher than below the threshold. For the countries that are highly dependent on OX, the government can increase OX to improve the TB. However this does not work for countries that are not highly oil dependent.