Pub Date : 2019-07-11DOI: 10.15826/RECON.2019.5.2.010
A. Tolmachev, E. V. Sinitsyn, D. A. Brusyanin
This article describes a probabilistic mathematical model which can be used to analyse traffic flows in a road network. This model allows us to calculate the probability of distribution of vehicles in a regional road network or an urban street network. In the model, the movement of cars is treated as a Markov process. This makes it possible to formulate an equation determining the probability of finding cars at key points of the road network such as street intersections, parking lots or other places where cars concentrate. For a regional road network, we can use cities as such key points. This model enables us, for instance, to use the analogues of Kirchhoff First Law (Ohm's Law) for calculation of traffic flows. This calculation is based on the similarity of a real road network and resistance in an electrical circuit. The traffic flow is an analogue of the electric current, the resistance of the section between the control points is the time required to move from one key point to another, and the voltage is the difference in the number of cars at these points. In this case, well-known methods for calculating complex electrical circuits can be used to calculate traffic flows in a real road network. The proposed model was used to calculate the critical load for a road network and compare road networks in various regions of the Ural Federal District.
{"title":"Transport system modelling based on analogies between road networks and electrical circuits","authors":"A. Tolmachev, E. V. Sinitsyn, D. A. Brusyanin","doi":"10.15826/RECON.2019.5.2.010","DOIUrl":"https://doi.org/10.15826/RECON.2019.5.2.010","url":null,"abstract":"This article describes a probabilistic mathematical model which can be used to analyse traffic flows in a road network. This model allows us to calculate the probability of distribution of vehicles in a regional road network or an urban street network. In the model, the movement of cars is treated as a Markov process. This makes it possible to formulate an equation determining the probability of finding cars at key points of the road network such as street intersections, parking lots or other places where cars concentrate. For a regional road network, we can use cities as such key points. This model enables us, for instance, to use the analogues of Kirchhoff First Law (Ohm's Law) for calculation of traffic flows. This calculation is based on the similarity of a real road network and resistance in an electrical circuit. The traffic flow is an analogue of the electric current, the resistance of the section between the control points is the time required to move from one key point to another, and the voltage is the difference in the number of cars at these points. In this case, well-known methods for calculating complex electrical circuits can be used to calculate traffic flows in a real road network. The proposed model was used to calculate the critical load for a road network and compare road networks in various regions of the Ural Federal District.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49660724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-11DOI: 10.15826/RECON.2019.5.2.007
Qiuji Chen
In order to address vital environmental issues, China and Russia have established a set of cooperation mechanisms, such as the Sub-Committee on Environmental Protection of the Regular Meeting of the Prime Ministers of China and Russia. There is currently a multi-level environmental cooperation system between the two countries. In recent years, China and Russia have strengthened their ecological cooperation and have achieved certain results in the conservation of cross-border water resources and establishment of transboundary nature reserves. There are still, however, many problems to handle such as the discrepancies in legislation and the limited character of investment each of the countries is willing to make into environmental protection. Therefore, as the article shows, it is necessary to formulate a unified regulatory framework; to establish a resource protection zone; to enhance joint monitoring of the water quality in transboundary rivers as well as soil and air quality in adjacent areas; and, finally, to raise public awareness in both countries of environmental security and nature conservation. In 2017, Russia hosted the Year of Ecology, which was a good opportunity for both countries to promote information exchange and cooperation in the sphere of joint monitoring and governance, environmental legislation, and ecological education.
{"title":"Sino-Russian environmental cooperation: past, present, and future","authors":"Qiuji Chen","doi":"10.15826/RECON.2019.5.2.007","DOIUrl":"https://doi.org/10.15826/RECON.2019.5.2.007","url":null,"abstract":"In order to address vital environmental issues, China and Russia have established a set of cooperation mechanisms, such as the Sub-Committee on Environmental Protection of the Regular Meeting of the Prime Ministers of China and Russia. There is currently a multi-level environmental cooperation system between the two countries. In recent years, China and Russia have strengthened their ecological cooperation and have achieved certain results in the conservation of cross-border water resources and establishment of transboundary nature reserves. There are still, however, many problems to handle such as the discrepancies in legislation and the limited character of investment each of the countries is willing to make into environmental protection. Therefore, as the article shows, it is necessary to formulate a unified regulatory framework; to establish a resource protection zone; to enhance joint monitoring of the water quality in transboundary rivers as well as soil and air quality in adjacent areas; and, finally, to raise public awareness in both countries of environmental security and nature conservation. In 2017, Russia hosted the Year of Ecology, which was a good opportunity for both countries to promote information exchange and cooperation in the sphere of joint monitoring and governance, environmental legislation, and ecological education.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42922541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-11DOI: 10.15826/RECON.2019.5.2.006
E. Agbozo, B. K. Asamoah
E-government systems are a part of the general process of digital transformation in the public sector: countries with efficient e-government manage to reduce the administrative burden on private citizens and businesses and to improve government performance, transparency and accountability. This article brings to light the connection between the development of e-government systems and such factors as the rule of law and control of corruption. The study relies on a path model, which was built and statistically tested by using linear regression analysis to authenticate the veracity of the model’s components. The model uses three indicators adopted from the World Bank’s Governance Indicator project – the rule of law, control of corruption, and government effectiveness. The data to measure the e-Government Development Index (EGDI) in fifteen countries was provided by the e-Government 2016 Survey conducted by the United Nations. The findings reveal a positive complementary relationship between the rule of law in a country and the development of an e-government system, which enhances the government’s effectiveness. The article describes a shift towards a more citizen-centric e-government implementation strategy, which can be recommended in particular to policy-makers in developing economies. The proposed model can be recommended as a measurement tool to assess effective governance in any given country.
{"title":"The role of e-government systems in ensuring government effectiveness and control of corruption","authors":"E. Agbozo, B. K. Asamoah","doi":"10.15826/RECON.2019.5.2.006","DOIUrl":"https://doi.org/10.15826/RECON.2019.5.2.006","url":null,"abstract":"E-government systems are a part of the general process of digital transformation in the public sector: countries with efficient e-government manage to reduce the administrative burden on private citizens and businesses and to improve government performance, transparency and accountability. This article brings to light the connection between the development of e-government systems and such factors as the rule of law and control of corruption. The study relies on a path model, which was built and statistically tested by using linear regression analysis to authenticate the veracity of the model’s components. The model uses three indicators adopted from the World Bank’s Governance Indicator project – the rule of law, control of corruption, and government effectiveness. The data to measure the e-Government Development Index (EGDI) in fifteen countries was provided by the e-Government 2016 Survey conducted by the United Nations. The findings reveal a positive complementary relationship between the rule of law in a country and the development of an e-government system, which enhances the government’s effectiveness. The article describes a shift towards a more citizen-centric e-government implementation strategy, which can be recommended in particular to policy-makers in developing economies. The proposed model can be recommended as a measurement tool to assess effective governance in any given country.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42676239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-11DOI: 10.15826/RECON.2019.5.2.009
E. Polina, I. Solovyeva
Innovative development of territories is strategically important for the prosperity of any country. This article aims at describing original methodology for comprehensive assessment of innovative development of Russian regions. The proposed model takes into account specific features of innovative activity of regions and identifies growth potential and resources of territories, taking into account not only the innovation environment, but also areas of innovative activity. The study relies on the statistical data provided by the Central Statistical Database and the Unified Interdepartmental Information and Statistical System. In the course of processing and analyzing data, the index method, the multidimensional average method, factor-index analysis and other statistical data processing methods are used. The research involves ranking Russian regions according to their levels of innovative development and further dividing them into groups of powerful, strong, medium and weak innovators. We also analyzed the dynamics of innovation in the regions by looking at the changes in their ranking positions. The research findings brought to light the uneven development of Russian regions. The proposed assessment toolkit can be further used for drawing individual profiles for regions and formulating recommendations and guidelines for these regions’ development by taking into consideration their strengths and weaknesses.The results of this study have theoretical and practical significance and can be used as a tool for management of innovative activities both at the level of individual territories and at the national level.
{"title":"Methodology for comprehensive assessment of regional innovative development","authors":"E. Polina, I. Solovyeva","doi":"10.15826/RECON.2019.5.2.009","DOIUrl":"https://doi.org/10.15826/RECON.2019.5.2.009","url":null,"abstract":"Innovative development of territories is strategically important for the prosperity of any country. This article aims at describing original methodology for comprehensive assessment of innovative development of Russian regions. The proposed model takes into account specific features of innovative activity of regions and identifies growth potential and resources of territories, taking into account not only the innovation environment, but also areas of innovative activity. The study relies on the statistical data provided by the Central Statistical Database and the Unified Interdepartmental Information and Statistical System. In the course of processing and analyzing data, the index method, the multidimensional average method, factor-index analysis and other statistical data processing methods are used. The research involves ranking Russian regions according to their levels of innovative development and further dividing them into groups of powerful, strong, medium and weak innovators. We also analyzed the dynamics of innovation in the regions by looking at the changes in their ranking positions. The research findings brought to light the uneven development of Russian regions. The proposed assessment toolkit can be further used for drawing individual profiles for regions and formulating recommendations and guidelines for these regions’ development by taking into consideration their strengths and weaknesses.The results of this study have theoretical and practical significance and can be used as a tool for management of innovative activities both at the level of individual territories and at the national level.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43187891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-11DOI: 10.15826/RECON.2019.5.2.008
I. Turgel, L. Bozhko, E. Zinovyeva
The article aims to study the theoretical and empirical foundations of combining free economic zones with industrial clusters. The theoretical foundation is provided by the concept of a cumulative and circular process and the theory of ‘new economic geography’. The empirical part deals with the creation of cluster-type economic zones in Russia and Kazakhstan. The symbiosis of special economic zones (SEZs) and clusters is expected to enhance export potential and act as a powerful catalyst for national innovative development. Establishment of clusters within the framework of the existing SEZs can bring to these zones highly efficient projects for manufacturing export-oriented products. Methodologically, the research relies on systemic and structural-functional approaches, the logical method and the method of formalization as well as on the comparative and grouping methods applied to analyze SEZs. The study also provides a general overview of the SEZs and clusters operating in Russia and Kazakhstan and indicates their main types and characteristics. The practical significance of this research is that its findings can be used to devise recommendations for improving economic performance of both countries, attracting new technologies and investments and addressing social and economic problems of the regions.
{"title":"Cluster approach to organization of special economic zones in Russia and Kazakhstan","authors":"I. Turgel, L. Bozhko, E. Zinovyeva","doi":"10.15826/RECON.2019.5.2.008","DOIUrl":"https://doi.org/10.15826/RECON.2019.5.2.008","url":null,"abstract":"The article aims to study the theoretical and empirical foundations of combining free economic zones with industrial clusters. The theoretical foundation is provided by the concept of a cumulative and circular process and the theory of ‘new economic geography’. The empirical part deals with the creation of cluster-type economic zones in Russia and Kazakhstan. The symbiosis of special economic zones (SEZs) and clusters is expected to enhance export potential and act as a powerful catalyst for national innovative development. Establishment of clusters within the framework of the existing SEZs can bring to these zones highly efficient projects for manufacturing export-oriented products. Methodologically, the research relies on systemic and structural-functional approaches, the logical method and the method of formalization as well as on the comparative and grouping methods applied to analyze SEZs. The study also provides a general overview of the SEZs and clusters operating in Russia and Kazakhstan and indicates their main types and characteristics. The practical significance of this research is that its findings can be used to devise recommendations for improving economic performance of both countries, attracting new technologies and investments and addressing social and economic problems of the regions.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45513191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.15826/recon.2022.8.1.004
A. Mikhaylova, E. Timushev
Relevance. Institutions of a fiscal system play a significant role in regional credit ratings. This is reflected in the low creditworthiness of Russian regions from the international perspective. Research objective. The paper discusses the role of the institutional factors in the credit ratings assigned to Russian regions by Russian and international agencies. Data and Methods. The study analyzes the rating methodology adopted by Russian and international credit rating agencies and tests the presence of the institutional factors by conducting a formal regression analysis based on the data from the budgetary systems of Russia and the United States. Results. We demonstrate that international agencies value institutional factors, while Russian agencies use formal quantitative indicators. By applying comparative regression analysis to the economic and fiscal indicators of Russian regions and U.S. states, we found that The Big Three (Fitch Ratings, S&P Global Ratings and Moody's Investors Service) rate Russian regions lower than U.S. states, although the formal indicators between the two fiscal systems at the regional level do not differ as much. Conclusions. We conclude that the lower creditworthiness of Russian regions in the international perspective reflects the weakness of the institutions in the Russian budgetary system. Practically, the assessment of regional creditworthiness in Russia by the international agencies highlights the areas of intergovernmental fiscal relations that need improvement, most notably the insufficient tax and spending autonomy of local and regional governments.
{"title":"The impact of institutions on regional credit ratings in Russia","authors":"A. Mikhaylova, E. Timushev","doi":"10.15826/recon.2022.8.1.004","DOIUrl":"https://doi.org/10.15826/recon.2022.8.1.004","url":null,"abstract":"Relevance. Institutions of a fiscal system play a significant role in regional credit ratings. This is reflected in the low creditworthiness of Russian regions from the international perspective. Research objective. The paper discusses the role of the institutional factors in the credit ratings assigned to Russian regions by Russian and international agencies. Data and Methods. The study analyzes the rating methodology adopted by Russian and international credit rating agencies and tests the presence of the institutional factors by conducting a formal regression analysis based on the data from the budgetary systems of Russia and the United States. Results. We demonstrate that international agencies value institutional factors, while Russian agencies use formal quantitative indicators. By applying comparative regression analysis to the economic and fiscal indicators of Russian regions and U.S. states, we found that The Big Three (Fitch Ratings, S&P Global Ratings and Moody's Investors Service) rate Russian regions lower than U.S. states, although the formal indicators between the two fiscal systems at the regional level do not differ as much. Conclusions. We conclude that the lower creditworthiness of Russian regions in the international perspective reflects the weakness of the institutions in the Russian budgetary system. Practically, the assessment of regional creditworthiness in Russia by the international agencies highlights the areas of intergovernmental fiscal relations that need improvement, most notably the insufficient tax and spending autonomy of local and regional governments.","PeriodicalId":33206,"journal":{"name":"REconomy","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67262627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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