Computational Model Library

Learning-based Protection Motivation Theory Climate-Adaptive Behaviour model (LPMT-CAB) (1.0.0)

The aim of this model is to study the dynamic propagation of individual climate adaptive behaviours in different scenarios within the analytical framework of conservation motivation theory, focusing on the impact of social and experiential learning on the adoption of climate adaptive behaviours by coastal farmers.
Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

Release Notes

Model for paper submission

Associated Publications

Learning-based Protection Motivation Theory Climate-Adaptive Behaviour model (LPMT-CAB) 1.0.0

Submitted by Wenhan Feng Published Apr 09, 2026 Last modified Apr 09, 2026

The aim of this model is to study the dynamic propagation of individual climate adaptive behaviours in different scenarios within the analytical framework of conservation motivation theory, focusing on the impact of social and experiential learning on the adoption of climate adaptive behaviours by coastal farmers.
Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

Release Notes

Model for paper submission

Cite this Model

Wenhan Feng (2026, April 09). “Learning-based Protection Motivation Theory Climate-Adaptive Behaviour model (LPMT-CAB)” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/f94cb062-26d7-4a58-a746-d02105575e3c/releases/1.0.0/

References

Model for paper “Promoting climate resilience through learning-based behavioural change: Insights from an agent-based model of a coastal farming community in Guangxi, China” in Environmental Science & Policy, Volume 179, May 2026, 104375, https://doi.org/10.1016/j.envsci.2026.104375

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Contributors
Wenhan Feng
DOI
No assigned DOI
Model Version
1.0.0
License
AFL-3.0
Programming Language
NetLogo
Software Framework
NetlLogo
Operating System
Windows
Publish Date
Thursday, April 09, 2026
Last Updated
Thursday, April 09, 2026
Downloads 1
Peer Review
Unreviewed
Version Submitter First published Last modified Status
1.0.0 Wenhan Feng Thu Apr 9 08:04:45 2026 Thu Apr 9 08:04:47 2026 Published

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