Green Technology Game and Data-Driven Parameter Identification in the Digital Economy

Xiaofeng Li () and Qun Zhao
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Xiaofeng Li: School of Economics and Law, College of Science & Technology, Ningbo University, Ningbo 315300, China
Qun Zhao: School of Economics and Law, College of Science & Technology, Ningbo University, Ningbo 315300, China

Mathematics, 2025, vol. 13, issue 14, 1-15

Abstract: The digital economy presents multiple challenges to the promotion of green technologies, including behavioral uncertainty among firms, heterogeneous technological choices, and disparities in policy incentive strength. This study develops a tripartite evolutionary game model encompassing government, production enterprises, and technology suppliers to systematically explore the strategic evolution mechanisms underlying green technology adoption. A three-dimensional nonlinear dynamic system is constructed using replicator dynamics, and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is applied to identify key cost and benefit parameters for firms. Simulation results exhibit a strong match between the estimated parameters and simulated data, highlighting the model’s identifiability and explanatory capacity. In addition, the stability of eight pure strategy equilibrium points is examined through Jacobian analysis, revealing the evolutionary trajectories and local stability features across various strategic configurations. These findings offer theoretical guidance for optimizing green policy design and identifying behavioral pathways, while establishing a foundation for data-driven modeling of dynamic evolutionary processes.

Keywords: green technologies; evolutionary game model; nonlinear dynamic system; BFGS algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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