Month: December 2020

BALANCED SCORECARD AS A TOOL FOR EVALUATING THE INVESTMENT ATTRACTIVENESS OF REGIONS COMPRISING THE ARCTIC ZONE OF THE RUSSIAN FEDERATION

Vladimir MYAKSHIN

Professor,Northern (Arctic) Federal University,Russia,Arkhangelsk,Naberezhnaya Severnnoy Dviny17

mcshin@yandex.ru

Vladimir PETROV

Professor, St. Petersburg State Forestry University, Russia, St. Petersburg, Institutsky pereulok 5

wladimirpetrov@mail.ru

Abstract

Prerequisite to sound investment decision-making is the availability of reliable, objective information on earlier investments and which economic sectors they have benefitted, as well as methods allowing for multi-faceted analysis of investment performance. This study aims to elaborate a balanced scorecard to reflect the performance of and the trends in the investment activity ongoing in the regions that comprise the Arctic Zone of the Russian Federation. Methodologically, the study relies on a systemic, balanced approach; balanced scorecard concept; and foreign and domestic practices of estimating regional investment attractiveness. The study is novel in that it has achieved a customized balanced scorecard that allows for analyzing the RF Arctic regions’ investment attractiveness from various perspectives, while also allowing to identify these regions’ major investment-related challenges and promising investment opportunities. Further, the study contributes to the scientific soundness of strategies that seek better investment image. Among key outcomes of this study is the economic model that uses the said balanced scorecard to measure the RF Arctic regions’ investment attractiveness with regard to investment stakeholders (public authorities, investors, population). The outcomes of this study are expected to be used as guidance by the public authorities in the RF Arctic regions when shaping local investment policies. The prospects of this study lie in further improvement of the contents and the structure of the balanced scorecard as the Russian economy progresses in its development and, hence, improved models will be required for measuring its regions’ investment appeal.

Keywords: The Arctic Zone of the Russian Federation, investment activity, investment risks, investment climate, investment policy, investment attractiveness, important investment aspects, estimation of investment attractiveness, balanced scorecard, regional economic system.

JEL classification: D29, L50, L52, L90

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MODELING LOGISTIC ENTERPRISE RE-LOCATION DECISION BY A NESTED LOGIT MODEL

Y Nguyen CAO

Dr.Eng Vietnam-Japan Research and Development Center, Department of Transport Economics,University of Transport and Communications,Cau Giay, No.03 Lang Thuong, Dong Da, Ha Noi, Viet Nam

ynguyencao82@utc.edu.vn

Abstract

This paper develops a model to analyze decisions regarding the relocation process for logistics enterprise by using discrete choice models. In this framework, two decision points in the relocation process are assumed and maintained in the micro-simulation modeling. The first decision, move or non move, is modeled by using a binary logit form with outcome the probability of moving. The second decision, choosing the destination location, is modeled by a mixed logit model incorporating spatial effects with the outcome of the conditional probability of choosing a zone. This study also applied the relocation decision structure of each logistics enterprise by nested logit model to find out the best model. In case study, the logistics enterprise relocation decision model has acceptable performance by the nested logit model. However, the nested logit model has to follow the IID Gumbel distribution holds within each nest. Therefore, nested logit model cannot take into account the various tastes among alternatives in the random part of utility function to improve the implementation of the model. The proposed model also confirm again the important role of spatial interactions among individual logistics enterprise and among zones in the logistics enterprise relocation decision process. The results indicate that big logistics enterprises have a lower probability of relocating and the migrating enterprises are more attractive in the zone which has a high accessibility. Finally, the population density, number of employees and the average land prices of zone strongly affect on the relocation decision making process of individual logistics enterprises.

Keywords: Mixed Logit Model, Logistics Firm, Re-location Decision Model, Nested Logit Model

JEL classification:

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MONITORING LAND USE / LAND COVER CHANGES USING REMOTE SENSING AND GIS: A CASE STUDY ON KANCHRAPARA MUNICIPALITY AND ITS ADJOINING AREA, WEST BENGAL, INDIA

Somnath DAS

Cartographer (Contractual), Department of Geography, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India.

somu8969@gmail.com

Abhay Sankar SAHU

Assistant Professor, Department of Geography, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India.

sahu.abhaysankar@gmail.com

Abstract

Changes in land use are a very important issue. Through the discussion of land use change it is easy to know the relationship of a person living in that place with that place. This paper demonstrates the land use changes of Kanchrapara Municipality and its adjoining area through the use of some techniques of remote sensing and GIS (Geographical Information System). Two Landsat satellite images with a range of twenty years have been used to apply these techniques. These images were of the Landsat-7 ETM+ (Year 2000) and Landsat-8 OLI-TIRS (Year 2019). ArcGIS 10.5 software has been used for pre-processing of these images. Then the supervised classification method has been used for the classification of those images using QGIS 3.4 software using the maximum likelihood algorithm. Four types of land use categories have been identified by image classification based on satellite images and Google maps. These were water bodies, vegetation coverage area, bare soil and built up area. Of these, only the amount of bare soil has increased (+17.22%) and the rest of land use categories decreased comparatively. The reasons for the increase and decrease of this level were also discussed here. Accuracy assessment has been also done to determine the accuracy of image classification. Where the overall accuracy of two decades was 82.39 % and 83 % respectively, with the Kappa coefficient was 0.75 and 0.76 respectively. Finally, there is a comparative discussion of two decades of land use using change detection techniques.

Keywords: Land use/ land cover changes, Image processing, Accuracy assessment, Kappa Coefficient, Change detection technique

JEL classification: C00, C89, R14, R52

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