数字农科院2.0

Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China's Cotton Farmers

文献类型： 外文期刊

作者： Cai, Lu;Zhang, Zhenggui;Mao, Shaohua;Azimov, Jamshed;Yusufujiang, Nilupaier;Zhang, Yaopeng;Bi, Rusheng;Wang, Lin;Wang, Zhanbiao;Gao, Lei

作者机构：

关键词： climate-smart technologies;technology demonstration;technology cognition;technology adoption;mediating mechanisms

期刊名称： SUSTAINABILITY

ISSN：

年卷期： 2025 年 17 卷 16 期

页码：

收录情况： SCIE（2025版） ; ; SSCI（2025版）

摘要： Amid escalating global climate challenges and the accelerating transition toward low-carbon agriculture, the effective diffusion of climate-smart technologies (CSTs) has become a critical pathway to achieving sustainable agricultural development. This study focuses on cotton farmers in Xinjiang and draws on micro-level survey data from 504 respondents to construct an analytical framework of demonstration-cognition-adoption. It systematically examines the impact pathways and mediating mechanisms of different demonstration models. The findings reveal that technology demonstration indirectly influences farmers' adoption of CSTs by shaping their cognitive perceptions, with perceived operational utility emerging as the most critical mediating mechanism in the entire technology dissemination chain. Among current extension models, government-led demonstrations play a central role, while the effectiveness of enterprise-led demonstrations hinges on brand credibility and service quality. Moreover, the ease of operation of a technology outweighs its economic returns in determining adoption outcomes, and farmers exhibit significant heterogeneity in their responses to different demonstration types. Based on these insights, the study recommends the development of a stratified and differentiated dissemination strategy, the strengthening of government-led demonstration functions, the promotion of standardized enterprise participation, and the enhancement of both farmers' cognitive understanding and technology fit to enable broader and higher-quality adoption of climate-smart technologies.

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