Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Shinkevich, Alexey I.; Ershova, Irina; Galimulina, Farida F.; Yarlychenko, Alla A.

doi:10.3390/math9233055

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…-Innovative mesosystems algorithm for sustainable development priority areas identification in industry based on decision trees construction [18];…”

Section: Discussionmentioning

confidence: 99%

The estimate of innovative development of construction industry in the Kazakhstan

et al. 2023

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In this paper, based on OECD statistical data for 1995-2018 and the Input-Output model, a systematic study of the industrial and innovative development of the construction sector of the Republic of Kazakhstan was carried out. As, for the Produce / selling and Purchase / buying, the Gross value added and Final demand, the Import and Export transactions of structural agents, estimates are given and the bands of the left-hand critical, the left-sided moderate, the equilibrium, the right-sided moderate, the right-sided critical are confidence intervals constructed. Further, based on the erogeneity of the value of the standard deviation, the distributions of the absolute frequencies of the flows of transactions along the bands of confidence intervals are estimated, which, in turn, allow us to assess the state and condition of equilibrium of the industrial and innovative development of the construction industry of the Republic of Kazakhstan.

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“…-Innovative mesosystems algorithm for sustainable development priority areas identification in industry based on decision trees construction [18];…”

Section: Discussionmentioning

confidence: 99%

The estimate of innovative development of construction industry in the Kazakhstan

et al. 2023

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“…We continue to explore methodological issues. Our research background covers the study of statistical tools and the possibility of their application to assess the sustainable development of socioeconomic systems [37][38][39]. This study proposes a new approach to assessing the sustainable development of industry based on the combined sequential application of factor and discriminant analysis.…”

Section: Introductionmentioning

confidence: 99%

Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis

2022

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The problem of sustainable development is one of the central issues on the agenda of the global community. However, it is difficult to assess the pace and quality of sustainable development of individual economic systems—in particular, industry—due to the lack of a unified methodological approach. In this regard, the following research goal was formulated—to develop and test a methodology for forecasting sustainable development by using statistical tools. The achievement of the goal was facilitated by the application of formalization methods, factor analysis, discriminant analysis, the method of weighted sum of the criteria, and the method of comparison. The results of the study are new scientific and practical solutions that develop the ability to diagnose economic systems for the transition to environmentally friendly production. Firstly, methodological solutions are proposed to assess the nature of the transition of industry to sustainable development (low, medium, or high rate). The methodology is based on the proposed aggregated indicator of sustainable industrial development based on the results of factor analysis (by the method of principal components). As a result, the patterns of sustainable development of the extractive and manufacturing sectors of the Russian economy are revealed. Secondly, integral indicators of economic, environmental and social factors of sustainable development are calculated, and classification functions for each type of industrial transition to sustainable development (low, medium, or high) are formed through discriminant analysis. Scenarios of industrial development are developed, taking into account the multidirectional trajectories of the socioeconomic development of the country. Thirdly, the DFD model of the process of scenario forecasting of sustainable industrial development is formalized, reflecting the movement of data flows necessary for forecasting sustainable industrial development. It is revealed that the manufacturing industry is expected to maintain a low rate of transition to sustainable development. On the contrary, for the extractive industry, if efforts and resources are concentrated on environmental innovations, average transition rates are predicted. The uniqueness of the proposed approach lies in combining two types of multivariate statistical analysis and taking into account the indicators that characterize the contribution of industrial enterprises to sustainable development.

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Proliferations in Algorithmic Control: Review of the Phenomenon and Its Implications

Dutta,

Pramanik,

Rajan

et al. 2023

Transfer, Diffusion and Adoption of Next-Generation Digital Technologies

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Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Cited by 9 publications

References 34 publications

The estimate of innovative development of construction industry in the Kazakhstan

The estimate of innovative development of construction industry in the Kazakhstan

Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis

Proliferations in Algorithmic Control: Review of the Phenomenon and Its Implications

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