Predictive Models Linking Environmental Strategy and Financial Performance Outcomes

Abstract

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 traditional
regression-based approaches in sustainability accounting, we develop a dynamic system
model that treats environmental initiatives as nodes in a complex adaptive network,
with financial outcomes emerging from the interaction topology. Our methodology
employs a hybrid of agent-based modeling and time-series analysis, calibrated with
a unique dataset spanning 15 years across multiple industries. The model captures
non-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-performance
relationship. Results demonstrate that the efficacy of environmental investments depends critically on their structural position within the corporate strategy network, with
centrally coordinated initiatives yielding 28% higher long-term financial returns than
decentralized approaches. Furthermore, we reveal a previously undocumented ’sustainability inflection point’ where cumulative environmental investments trigger disproportionate financial gains. This research contributes to both information systems
and strategic management by providing a computational tool for optimizing environmental strategy portfolios and by offering a new theoretical lens through which to view
the business case for sustainability.