Environmentally Oriented Modelling for Supporting Decision-Making in Business Ecosystem

Abstract

The study focuses on models of environmentally oriented development to support decision-making in business ecosystems (BE). The goal is to show the relationship between technical greening measures and BE performance results. The study's stages include the characteristics of BE; evaluation of greening measures and their impact on functioning; construction of factor-analysis models of technical greening and BE efficiency; determination of validation criteria based on elasticity indicators; and justification of the feasibility of the proposed methodology for decision support in BE. The methodology is based on the conceptualisation of BE, the construction of “input-output” models, the calculation of the integral indices I_TGI and I_BEEEI, factor/regression analysis, and elasticity assessment (point and arc). For an energy company, the most significant I_TGI parameters are the share of new equipment with reduced noise and lower water discharge. The results of BE greening depend significantly on environmental costs and payments to support the ecobalance. The average annual growth rate of I_TGI exceeds that of I_BEEEI, indicating a lag between implementation and results; the nonlinear nature of the relationship is confirmed. The arc elasticity index indicates a moderate average sensitivity of I_BEEEI to changes in I_TGI, while the point elasticities reflect greater volatility in response to shocks. The study confirmed the presence of a measurable, but lagging and nonlinear relationship between technical greening and the results of greening the business ecosystem. This makes the constructed models an effective tool for supporting management decisions in sustainable development. The practical value lies in the proposed methodology for assessing internal drivers of business ecosystem efficiency in alignment with external challenges, enabling the systematic evaluation of environmental initiatives, their effectiveness, and their integration into digital decision-support systems for monitoring sustainable transformations.