(untitled) (gjstudies, 2025)
This research introduces a novel computational framework that integrates ecological network theory with financial forecasting to model the relationship between corporate environmental strategy and financial performance. Departing from traditionalregression-based approaches in sustainability accounting, we develop a dynamic systemmodel that treats environmental initiatives as nodes in a complex adaptive network,with financial outcomes emerging from the interaction topology. Our methodologyemploys a hybrid of agent-based modeling and time-series analysis, calibrated witha unique dataset spanning 15 years across multiple industries. The model capturesnon-linear feedback loops, threshold effects, and time-lagged impacts that conventional methods overlook. We identify three distinct network archetypes—linear, huband-spoke, and fully connected—that differentially mediate the strategy-performancerelationship. Results demonstrate that the efficacy of environmental investments depends critically on their structural position within the corporate strategy network, withcentrally coordinated initiatives yielding 28% higher long-term financial returns thandecentralized approaches. Furthermore, we reveal a previously undocumented ’sustainability inflection point’ where cumulative environmental investments trigger disproportionate financial gains. This research contributes to both information systemsand strategic management by providing a computational tool for optimizing environmental strategy portfolios and by offering a new theoretical lens through which to viewthe business case for sustainability.