Is rationality or herd more conducive to promoting farmers to protect wetlands? A hybrid interactive simulation. (October 2022) - Wang, Han; Qiu, Li; Chen, Zhoupeng; Li, Fengqin; Jiang, Peng; Zhang, Anlu; Nie, Xin
Abstract: Within the cultural context of "guanxi" in China, it is particularly challenging to develop a framework capable to capture the complexity and adaptability of farmers facing conflicts between wetland conservation and livelihood development. In this paper, we integrate agent-based model (ABM) and field experimental methods to develop a hybrid simulation experiment model for wetland conservation behavior, in order to reveal the driving forces of land change and understand the dynamic change process of farmers' behavior. Through a field experiment and a questionnaire survey of farmers around the Shankou Mangrove Ecological Nature Reserve (SMENR) in Guangxi, China, the intention of different types of farmers to adopt wetland conservation behavior was assessed from different angles, constructing a rational choice theory (RCT) framework and ABM proxy behavior rules. The results showed that after 5 years, the adoption rate of farmers' wetland conservation behavior reached 100% under the assumption of bounded self-interest, but only about 80% under bounded rationality. This confirms that herd is more conducive to promoting farmers' wetland conservation behavior. Decision makers should consider the importance of bounded rationality and bounded self-interest interaction, foster farmers' sense of belonging and responsibility toward wetlands, and guide farmers to participate in the ecological protection and sustainable use of wetlands. Graphical abstract: Image 1 Highlights: Herd is more conducive to promoting farmers to protect wetlands. The fastest take-off can be achieved at a threshold of about 20%. Livelihood patterns lead to differences in willingness to protect wetlands. Sense of belonging and responsibility is important for wetland conservation. Combining experimental economics and ABM can improve the accuracy of the model.