Modeling farmers' climate change adaptation strategies: An integrated SEM-SD approach in Southwest China

Abstract

Global climate change, particularly the increasing frequency of extreme weather events, poses significant challenges to agriculture, threatening food security and sustainability. Farmers' adaptive capacity is crucial for maintaining agricultural stability. However, limited research has jointly examined passive adaptation strategies (PAS) and active adaptation strategies (AAS), particularly through an integrated approach. This study addresses this gap by innovatively combining the structural equation model (SEM) and the system dynamics (SD) model to identify key influencing factors and simulate the temporal evolution of farmers' climate change adaptation strategies (CCAS), capturing both causal relationships and dynamic behavioural trends. Based on a 2021 survey of farmers in Sichuan Province, the results revealed that most farmers predominantly relied on PAS, primarily increasing irrigation to cope with climate change. Personal adaptive capacity (PAC), especially technology adoption ability (TAA), farming experience (FE), and meteorological disaster knowledge (MDK) significantly influenced CCAS. Risk perception (RP) and social constraint (SC) strongly promoted PAS adoption, driven by climate risk concerns and social pressure. SD simulations from 2021 to 2031 further revealed that TAA was the most influential factor affecting adaptation behaviour, followed by FE and pressure from neighbours (PN). Moreover, integrated policies involving individuals, communities and the government were significantly more effective than single-actor efforts, with adaptation strategies accelerating after 2027. These findings highlight the value of integrating SEM and SD to explore the drivers and dynamics of climate adaptation. Enhancing individual adaptive capacity is key to shifting from PAS to AAS, offering theoretical and practical guidance for sustainable water use and climate-resilient agriculture.