Green Competitiveness Forecasting as an Instrument for Sustainable Business Transformation
Green Competitiveness Forecasting as an Instrument for Sustainable Business Transformation

Summary/Abstract: Modern conditions for transforming the business environment in the context of imple- menting sustainable development goals, decarbonising the economy, and ensuring cli- mate neutrality determine the need to implement effective measures at the level of indi- vidual enterprises. External challenges determine the urgency of strengthening enterpris- es' green competitive advantages and forming an integral level of green competitiveness. The paper offers a methodological basis for the long-term forecasting of enterprises' green competitiveness levels. The key object of the research is a set of companies repre- senting the machine building, agriculture, and food industries, as strategic branches of Ukraine’s economy. The period of 2020–2028 was chosen as the interval for foresight. The application of autoregressive integrated ARIMA modelling by means of the non-linear least squares method made it possible to determine transformation vectors, marketing determinants, and target orientations for increasing the integral level of green competi- tiveness. The assessment of the results showed that the leading enterprises in the optimis- tic scenario are PJSC "SVF Agrotron" (agro-industrial complex), where the predictive value of green competitiveness is 0.84; PJSC "Wimm-Bill-Dunn Ukraine" (food indus- try), for which the predictive value is 0.73; and PJSC "Motor Sich" (mechanical engineer- ing), for which the predictive value of green competitiveness is 0.74. The results of the evaluation revealed the leading enterprises according to the optimistic scenario for the agro-industrial complex, food industry, and mechanical engineering. The research sub- stantiates the claim that the priorities of the strategy for increasing the level of green com- petitiveness and the relevant determinants of its implementation should be determined considering the transformation scenarios of its level.

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