Commodity flow model for an exclave region: Rent-seeking in the "transitional period" of the special economic zone

This article focuses on a commodity flow model for an exclave region (CFMER). The CFMER development is aimed at identifying aggregate proportions of the exclave's economy in the transitional period of the special economic zone (SEZ) functioning. The key method of analysis is the comparison of data on the generation of gross regional product and regional foreign economic activities (including export and import of goods and moving goods from/into the Kaliningrad region to other Russian regions). It results in a conceptual CFMER, which is assessed as of 2011. The availability of additional -as compared to a regular region- data on commodity flows in the framework of the SEZ transitional period makes it possible to identify structural disproportions in the economy. It is shown that the introduction of the SEZ transitional period did not result in a change in the conceptual model of the regional economy's functioning merely increasing the opportunities for rent extraction. The authors predict structural imbalances in the exclave economy at the microlevel, in particular, the article analyses the conceptual model of rent extraction in the SEZ transitional period. The CFMER can be used for forecasting the development of exclave's economy under different scenarios of the evolution of SEZ in the Kaliningrad region

Development of electric road transport: simulation modelling

Electric transport is rapidly gaining popularity across the world. It is an example of technological advancement that has multiple consequences for regional economies, both in terms of the adaptation of production, transport and energy systems and their spatial optimization. The experience of leading economic regions, including countries of the Baltic Sea region, shows that electric transport can potentially substitute traditional transport technologies. Based on an authentic model of system dynamics, the authors propose a new approach to simulation modelling of the dissemination of electric vehicles in a given region. The proposed model allows the authors to take into account the key systemic feedback loops between the pool of electric vehicles and the charging infrastructure. In the absence of data required for the econometric methods of demand forecasting, the proposed model can be used for the identification of policies stimulating the consumer demand for electric vehicles in regions and facilitating the development of the electric transport infrastructure. The proposed model has been tested using real and simulated data for the Kaliningrad region, which due to its specific geographical location, is a convenient test-bed for developing simulation models of a regional scale. The proposed simulation model was built via the AnyLogic software. The authors explored the capacity of the model, its assumptions, further development and application. The proposed approach to demand forecasting can be further applied for building hybrid models that include elements of agent modelling and spatial optimization

Evaluating the efficiency of the research sector in Russian regions: a dynamic data envelopment analysis

The nonparametric method of dynamic data envelopment analysis (DDEA) has become increasingly popular for conducting comparative efficiency evaluations. In recent years, dynamic data envelopment analysis (DDEA), a variant of this method, has gained significant attention. This article applies dynamic analysis to evaluate the efficiency of the research sector in Russian regions. Traditional input variables such as the number of research staff and R&D expenditure are considered, while publication and patent metrics serve as output indicators. The analysis covers a substantial time period, spanning from 2009 to 2020. Notably, the proposed evaluation method incorporates publication quality measures as a carry-over variable, in addition to accumulated R&D expenditure. The study employs dynamic data envelopment analysis to compare the obtained results with previous evaluations of the research and technology sector in Russian regions. The findings demonstrate that the proposed method serves as a valuable ranking technique, enhancing existing evaluations of regions' research and technology potential in terms of efficiency. The article concludes by discussing the prospects and limitations of the method in evaluating and forecasting research and technology profiles of regions