| Issue |
E3S Web of Conf.
Volume 536, 2024
2024 6th International Conference on Environmental Prevention and Pollution Control Technologies (EPPCT 2024)
|
|
|---|---|---|
| Article Number | 01025 | |
| Number of page(s) | 5 | |
| Section | Environmental Planning Management and Ecological Construction | |
| DOI | https://doi.org/10.1051/e3sconf/202453601025 | |
| Published online | 10 June 2024 | |
How to promote GBTs innovation through multi-agent collaboration: An evolutionary game system simulation approach
1 School of Management Engineering, Qingdao University of Technology, 266525 Qingdao, China
2 School of Business, Jinan University, 250022 Jinan, China
3 School of Economics and Management, Lanzhou University of Technology, 730050 Lanzhou, China
a email:muxiaoyin@126.com
b* Corresponding author: 15254452089@163.com
c email:507849374@qq.com
d email:maminglu-117@hotmail.com
e email:laruifang@163.com
f email:yaowenyan99@163.com
g email:2088076466@qq.com
h email:977011807@qq.com
Facing the serious pollution and carbon emission problem, China actively develops and applies GBTs (green building technologies) to realize the dual-carbon target, while the innovation of GBTs is deeply influenced by multiple subjects and factors. This study constructs a tripartite evolutionary game model of GACEs-ARIs, analyzes the inner mechanism of the collaborative innovation process of GBTs through simulation. It is found that: (1) GA is the most crucial subject in the process of collaborative innovation, which plays the role of coordinating the decision-making of the rest of subjects; (2) The trust degree and the benefit distribution coefficient are the influencing factors that play an important role in determining the start and continuation of collaborative innovation; (3) To actively promote the collaborative innovation of GBTs, GA should play a good role as an intermediary to ensure that the trust between the two sides is at a high level, and negotiate a reasonable distribution ratio, actively mobilizing the cooperation of CEs and ARIs by incentives and penalties.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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