ZOU Jin-peng, SHEN Lu-lin, WANG Fang, TANG Hong, ZHOU Zi-yang
Identifying the factors that influence farmers' adoption of low-carbon technologies (FA) and understanding their impacts are essential for shaping effective agricultural policies aimed at emission reduction and carbon sequestration in China. Utilizing a meta-analysis of 122 empirical studies, this research delves into 23 driving factors affecting FA and tries to address the inconsistencies found in existing literature. This study systematically examines the effect size, source of heterogeneity, and time-accumulation effect of the driving factors on FA. Key findings are as follows: (1) There is a significant level of heterogeneity in the factors influencing FA, with the exception of farming experience, the sources of heterogeneity come from survey zone, methodology model, technological attributes, report source, financial support, and the sampling year. (2) Age, farming experience, adoption cost exhibit a negative correlation with FA, whereas educational level, health status, technical training, economic and welfare cognition, land contract, soil quality, terrain, information accessibility, demonstration, government promotion, government regulation, government support, agricultural cooperatives member, peer effect, and agricultural income ratio demonstrate a positive correlation. Especially, demonstration and age show a particularly strong correlation. (3) The effect of demonstration, age, economic and welfare cognition, farming experience, land contract, soil quality, information accessibility, government promotion, and support, as well as agricultural cooperative membership and peer effects on FA, are generally stable but exhibit varying degrees of attenuation over time. The effect of village cadre, family income, farm scale, gender, health status, technical training, and off-farm work on FA show notable temporal shifts and maintain a weak correlation with FA. This study plays a pivotal role in shaping China's current low-carbon agriculture policies across various regions. It encourages policymakers to comprehensively consider the stability of key factors, other potential factors, technological attributes, rural economic and social context and their interrelations.