ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Networked Evolutionary Game Analysis of Low-Carbon Technology Diffusion
Provisionally accepted
- Jiangsu University, Zhenjiang, China
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Developing and deploying low carbon technology is essential for alleviating energy poverty and mitigating environmental pressures, with enterprises serving as critical actors in this transition. To explore the determinants of low carbon technology adoption and diffusion among enterprises, this study constructs a complex network evolutionary game model that integrates behavioral mechanisms and incentive structures. The model examines how behavioral factors, including herd behavior and organizational inertia, together with incentive factors such as policy, economic, social, and technological drivers, shape the diffusion dynamics. The results reveal three key findings: (1) Direct policy incentives and demand-side drivers, such as subsidies and consumer green preferences, exert a stronger and more immediate influence on low-carbon technology adoption than indirect regulatory measures, particularly in the early stages of diffusion. (2) Behavioral factors exhibit asymmetric effects: herd behavior can impede early adoption when participation is low, whereas moderate organizational inertia stabilizes long-term adoption once diffusion takes hold. (3) Policy incentives, market demand, and social supervision interact in a nonlinear and partially substitutable manner, indicating that coordinated policy mixes can significantly accelerate diffusion across enterprise networks. The results suggest that policy and demand-side drivers play a dominant role in accelerating LCT diffusion, while behavioral and social factors primarily influence the timing and stability of adoption. Overall, the study provides an analytical perspective on the role of LCT diffusion in supporting enterprise-level green transformation.
Keywords: Behavioral influences, Benefits, Enterprise, evolutionary game, Low-carbon technology diffusion
Received: 25 Nov 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Sun, Du and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jianguo Du
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