Forecasting the environmental and economic indicators of the enterprise, taking into account their mutual proportionality in dynamics for the purposes of sustainable development

Journal title RIVISTA DI STUDI SULLA SOSTENIBILITA'
Author/s Svetlana L. Lozhkina, Olga M. Gusarova, Olga I. Mamrukova, Svetlana Yu. Sivakova, Vladislav A. Lozhkin
Publishing Year 2022 Issue 2021/2
Language English Pages 19 P. 319-332 File size 180 KB
DOI 10.3280/RISS2021-002021
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The study proposes the construction of a statistical model of the company’s eco-logical and economic state, using the synthesis of econometrics and multidimen-sional forecasting methods, which provide the correlation of the obtained indica-tors, taking into account mutual proportionality in relation to the dynamic charac-teristics of environmental sustainability factors within the framework of the sus-tainable development paradigm. A combined model is implemented with a step-by-step modification of the matrix predictor into a regression-matrix one. Using the method of least squares, the study shows the codependency of the ecological and economic indicators of the sustainability of the enterprise: the cost of capital repairs of fixed assets for environmental protection; labor payment expenditures, including deductions for social needs of employees involved in environmental pro-tection; operating environmental costs; sales revenue; profitability of production and sales. The obtained regression coefficient showed a high degree of dependence of the closeness of the relationship of the considered environmental and economic indicators of sustainability (more than 98%), which proves their codependency and the need for their balanced use, since the value of at least one of the indica-tors outside the optimal range allows us to state a high probability of financial damage for enterprises. Using a combined model based on a matrix predictor, the study demonstrates the calculation of the predicted values of environmental and economic indicators of sustainability for 2021.The peculiarity of this model is to take into account the lagging (up to 2 years) values of factors and their growth over the previous period (for 1 year). The proposed model contains a mechanism of multivariate expectations, which allows us to consider various options for the future. Also, this model allows the automation of the forecasting process, which is reflected in the implementation of the trend of transition to the digital space in the implementation of sustainable development goals.

Keywords: forecasting, environmental and economic indicators, combination, matrix predictor, sustainable development.

  1. Bobylev S.N. and Zakharov V.M. (2009). Crisis: economics and ecologyic resource. Institute for Sustainable Development of the Russian Federation, TsEPR. M.: LLC «Typography Levko». 84 p.
  2. Bobylev S.N. (2011). Modernization and sustainable development: monograph. M.: Economics. 294 p.
  3. Burmatova O.P. (2017). Strategy for Sustainable Development of the Region: Environmental Scenarios. 6th Central European Conference in Regional Science – CERS, 2017: Engines of Urban and Regional Development: conference proceeding. 20-22 September, 2017, Slovak Republic. Ekonomicka fak. Univerzita Mateja Bela v Banskej Bystrici. Banska Bystrica, 1-11.
  4. Costanza R. and Daly H.E. (1992). Natural Capital and Sustainable Development. Conservation Biology, Mar., 6(1): 37-46. -- Retrieved from: https://www.jstor.org/stable/2385849.
  5. Danilov-Danilyan V.I. and Reif I.E. (2016). Biosphere and civilization. M.: OOO «Encyclopedia Publishing House». 432 p. -- Retrieved from: http://nature-and-people-media.ru/d/d-daniljan-rejf_biosfera_i_civilizacija-internet-v.pdf.
  6. Danilov-Danilyan V.I., Losev K.S. and Reif I.E. (2005). Facing the Main Challenge of Civilization: A View from Russia. M.: INFRA-M., 224 p. -- Retrieved from: http://lit.lib.ru/r/rejf_i_e/peredglawnymwyzowomciwilizacii.shtml.
  7. Ding S.S. & Cook R.D. (2014). Dimension Folding PCA and PFC for Matrixvalued Predictors. Statistica Sinica, 24(1): 463-492. -- Retrieved from: http://www3.stat.sinica.edu.tw/sstest/oldpdf/A24n124.pdf.
  8. Forrester J. (1978). World dynamics. M.: Science. 167 s.
  9. Hoffman K.G. and Motkin G.A. (1985). Economic problems of nature management. M.: Science.
  10. Hoffman K.G. (1998). Economics of nature management (from the scientific heritage). M.: Editorial URSS.
  11. Lozhkina S.L., Novikov A.A., Chepkasova E.A. and Novikova E.V. (2020). Assessment of investment attractiveness for regional sustainable development using methods of economic, statistical and factor analysis. Rivista di Studi sulla Sostenibilità, 2: 65-81. -- Retrieved from: https://www.francoangeli.it/Riviste/ SchedaRivista.aspx?IDArticolo=67911&Tipo=Articolo%20PDF&lingua=en& idRivista=168.
  12. Meadows D., Randers J. and Meadows D. (2007). Limits to Growth. 30 years later: monograph. Per. from English E. S. Oganesyan; ed. N.P. Tarasova. 3rd ed. M.: ICC Akademkniga, 342 p. -- Retrieved from: http://зеленыевузы.рф/wp-content/uploads/2018/09/Пределы-роста.-30-лет-спустя.pdf.
  13. Munch S.B., Poynor V. and Arriaza J.L. (2016). Circumventing Structural Uncertainty: A Bayesian Perspective on Nonlinear Forecasting for Ecology. Ecological Complexity, 32: 134-143. -- Retrieved from: https://www.sciencedirect.com/science/article/abs/pii/S1476945X16300708.
  14. Nalimov V.V. (1971). Experiment theory. Moscow: Nauka.
  15. Nalimov V.V. (1983). Analysis of the foundations of the ecological forecast. Pattern analysis as a weakened version of the forecast. Man and the biosphere, 8: 31-47.
  16. Pecchei A. (1985). Human qualities. M.: Progress. 312 p.
  17. Russian Statistical Yearbook 2019 (2019). Stat. book. Rosstat. -- Retrieved from: https://rosstat.gov.ru/storage/mediabank/Ejegodnik_2019.pdf.
  18. Ryumina E.V. (2000). Analysis of environmental and economic interactions. M.: Science.
  19. Samarsky A.A. (1979). What is a Computational Experiment?. Science and Life, 3: 27-33.
  20. Stiglitz J., Amartya S. and Fitoussi J-P. (2016). Misappreciating Our Lives: Why Doesn’t GDP Make Sense?: Report of the Commission on Measuring Economic Performance and Social Progress. Per. from English I. Kushnareva M., Publishing house of the Institute of Gaidar, 216 p.
  21. Williams C.C. and Millington A.C. (2004). The Diverse and Contested Meaning of Sustainable Development. The Geographical Journal, 170(2): 99-104.
  22. Zade L. (1976). The concept of a linguistic variable and its application to the concept of approximate solutions. 165 p.

Svetlana L. Lozhkina, Olga M. Gusarova, Olga I. Mamrukova, Svetlana Yu. Sivakova, Vladislav A. Lozhkin, Forecasting the environmental and economic indicators of the enterprise, taking into account their mutual proportionality in dynamics for the purposes of sustainable development in "RIVISTA DI STUDI SULLA SOSTENIBILITA'" 2/2021, pp 319-332, DOI: 10.3280/RISS2021-002021