Machine Learning Systems Supporting Environmental Strategy Evaluation in Firms

Abstract

This paper introduces a novel methodological framework that integrates machine learning with environmental strategy evaluation, addressing a significant gap
in both computational and sustainability research. Traditional approaches to evaluating corporate environmental strategies have relied heavily on static metrics, expert surveys, and linear regression models, which often fail to capture the complex,
dynamic, and non-linear interdependencies between strategic actions, firm characteristics, and environmental outcomes. Our research proposes and validates a
hybrid system, the Environmental Strategy Neural-Ecological Network (ESNEN),
which uniquely combines a multi-layered perceptron architecture with principles
from ecological network analysis. This cross-disciplinary synthesis allows the model
to not only predict the efficacy of environmental strategies but also to map the
emergent, system-level properties of a firm’s strategic portfolio, such as resilience,
redundancy, and resource flow efficiency. We formulate the problem as one of
strategic pathway optimization under uncertainty, moving beyond simple performance prediction. The methodology employs a novel data representation scheme
that transforms qualitative strategic documents and quantitative operational data
into a unified graph-based input, capturing both the content and the structural relationships of strategic elements. We train and test the ESNEN system on a unique,
hand-collected dataset of 450 firms across three industries from 1995 to 2004, tracking their environmental declarations, actions, and verified performance outcomes.
Results demonstrate that the ESNEN system achieves a 23% higher accuracy in
predicting long-term environmental performance improvements compared to bestin-class benchmark models, including support vector machines and random forests.
More importantly, the model’s analytical outputs—specifically, its derived ’strategic coherence’ and ’ecological leverage’ scores—provide managers with previously
unavailable diagnostic insights. These scores identify which strategic combinations
create synergistic effects and which introduce vulnerabilities, effectively allowing
for the computational ’stress-testing’ of environmental plans. The conclusion discusses how this machine learning system shifts the paradigm from retrospective
performance assessment to prospective strategic design, offering a tool for crafting
more robust, adaptive, and effective corporate environmental policies. This work
contributes original insights to the fields of sustainable business, strategic management, and applied machine learning by demonstrating how algorithmic models can
be structured to understand and improve complex socio-ecological strategies within
organizational contexts.