Assessment of the levers of influence over sustainable development target values of the agricultural sector
Relevance of the research topic. An important trend of the development strategy of the agricultural sector`s labour potential is the establishment of the environment for its formation, which fits sustainable development goals. Human factors of production in the context of sustainable development are characterized by the growing role of education and intangible capital. It acts as a determinant of the national economy`s and economic sectors` growth to ensure life conditions of the present and future generations. Rural areas become a kind of framework for residence, demographic trends and proportions, social interaction, gaining psychophysical, motivational and, to some extent, intellectual parameters for harmonious socio-ecological and economic, i.e. sustainable, development. Thus, the issue of levers` assessment of labour potential management in agricultural sector remains relevant at the present stage of achieving sustainable development goals. Problem statement. A sustainable development paradigm is a combination of economic, social and environmental components represented by a significant number of interconnected factors. Their comprehensive impact determines the ways and dynamics of achieving sustainable development goals. Making managerial decisions referring to the impact on each separate determinant and taking into account their interaction is accompanied by the analysis and processing of a significant set of indicators and requires special data processing methods. Solving this problem is possible when applying artificial neural networks to define parameters, which will be studied in the model. Analysis of recent research and publications. Both Ukrainian (M. Artyukh, L. Voynich, O. Lytvyn, I. Lyashenko, I. Tkach, L. Khrushchev, S. Shumska and V. Yankovy) and foreign scholars (N. Kuzina, S. Pshenichnikova and N. Suvorov), and others [1-6] analyzed the possibility of applying economic and mathematical models for economic growth analysis. The results of our own research on the problem are given in [10-16]. Works written by S. Bogachev, O. Galushko, M. Zgurovsky, S. Kvitka, S. Kozlovsky, J. Kologrivov, K. Rulitskaya, O. Sadovnik, L. Fedulova and A. Shpykulyak demonstrate their attempts to apply economic and mathematical methods and models for forecasting the agricultural sector`s sustainable development. Nevertheless, they did not receive further development and practical use for labour potential management in the context of sustainable development. Selection of unexplored parts of the general problem. Despite some progress in problem solving, there is a need for in-depth study of new approaches aimed at forecasting and modeling the agricultural sector`s targets based on the threefold concept of sustainable development using modern management methods, taking into account environment dynamism and the complexity of positive and negative externalities. Problem statement, research goals. The above-mentioned circumstances determine the rationale to assess the levers of influence over the target sustainable development indicators in the agricultural sector. Method and methodology of research. When doing the research general scientific (analysis and synthesis, abstract-logical, generalization and system analysis) and special (generalized linear regression, artificial neural networks) research methods have been used. Presentation of the main material (results of work). The neural network modelling allowed building a multifactor impact model on the resulting indicator of sustainable development in the agricultural sector, namely labour productivity. The next factors have been identified in the model: average monthly nominal wage; energy security; power-weight ratio; number of trucks (per 100 hectares of sown area and per 1,000 employees); amount of mineral fertilizers applied; amount of applied organic fertilizers for crops; emissions into the atmosphere from stationary and mobile sources of pollution; total waste accumulated at landfills; average index of regional human development; capital investment per capita. The following parameters like average monthly nominal wage, application of mineral fertilizers, number of trucks, and capital investments significantly affect the modelling results. The proposed model allows modelling and forecasting, based not only on previously obtained indicators and their dynamics, but also, which is important in the context of sustainable development, to set targets which facilitates managerial influence not only on the end result but on the process if its achievement as well, including the optimization impact. In addition, the modelling allows to adjust the impact factors, if they are either insignificant, as it has been found out when modelling, or lose value due to technological changes (for example, energy security and power-weight ratio). Conclusions. Based on the proposed multifactor adaptive model of sustainable development in the national agricultural sector, the strategic trends for its labour potential management have been developed. The neural network modelling methodology applied when building a model provided an opportunity to take into account the tasks for long-term sector`s sustainable development while determining and dynamically adjusting priorities, parameters and levers of influence on current industry`s efficiency. The proposed model allows not only modelling and forecasting based on previously obtained indicators and the dynamics of their adjustment, but also to set targets to obtain a number of possible scenarios for system development. It depends on forecasting conditions and parameters. It does not only rise the validity of managerial decisions, but also ensures the timeliness of management object`s adaptation to the ever-changing environmental conditions. Moreover, it provides managerial influence not only on the result, but also on the process of its achievement, including optimization of sustainable development levers.
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