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Journal of Integrative Agriculture  2024, Vol. 23 Issue (9): 3215-3233    DOI: 10.1016/j.jia.2024.07.016
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Dual carbon goal and agriculture in China: Exploring key factors influencing farmers’ behavior in adopting low carbon technologies
Jinpeng Zou*, Lulin Shen*, Fang Wang#, Hong Tang, Ziyang Zhou

College of Management, Sichuan Agricultural University, Chengdu 611130, China

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摘要  

识别影响农民低碳农业技术采纳的因素,并深入理解其影响,对于中国制定有效的农业“双碳”政策具有重要意义。本研究对122项中国境内的实证研究结果进行meta分析,系统探讨了23个驱动因素在农民低碳农业技术采纳中的效应大小、异质性来源及其随时间累积的效应,旨在解决现有文献中这些驱动因素对技术采纳影响效果不一致的问题。研究结果显示:(1)除了农民农业经验外,其他影响因素对农民低碳农业技术采纳表现出显著的异质性影响效果,异质性的来源主要来源包括调查区域、方法模型、技术属性、报告来源、资金来源以及抽样年份等。(2)年龄、农业经验、采用成本与农民低碳农业技术采纳呈负相关,而教育水平、健康状况、技术培训、经济和福利认知、土地契约、土壤质量、地形、信息获取能力、政府示范、政府促进、政府监管、政府支持、农业合作社成员、同伴效应和农业收入比与农民低碳农业技术采纳呈正相关。特别是,示范、年龄与技术采纳显示出特别强的相关性。(3)示范效应、年龄、经济和福利认知、农业经验、土地契约、土壤质量、信息获取能力、政府推广和支持、以及农业合作社成员和同伴效应对农民低碳农业技术采纳的影响通常保持一致,但随时间有减弱趋势。村干部、家庭收入、农场规模、性别、健康状况、技术培训和非农就业对农民低碳农业技术采纳的影响呈现出明显的时间变化,并且与农民低碳农业技术采纳之间保持弱相关。本研究在指导中国各地制定当前低碳农业政策方面具有重要意义,可帮助政策制定者全面考虑关键因素的稳定性、其他潜在因素、技术属性、农村经济和社会背景及它们之间的相互关系。



Abstract  
Identifying the factors influencing farmers’ adoption of low-carbon technologies (FA) and understanding their impacts are essential for shaping effective agricultural policies amied at emission reduction and carbon sequestration in China.  This study employs a meta-analysis of 122 empirical studies, delves into 23 driving factors affecting FA and addresses the inconsistencies present in the existing literature.  We systematically examine the effect size, source of heterogeneity, and time-accumulation effect of the driving factors on FA.  We find that significant heterogeneity in the factors influencing FA, except for farming experience, sources of heterogeneity from the survey zone, methodology model, technological attributes, report source, financial support, and the sampling year.  Additionally, age, farming experience, and adoption cost negatively correlate with FA.  In contrast, 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.  Finally, 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 contributes to 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 socio-economic context, and their interrelations.
Keywords:  farmers       influencing factors        low-carbon technology adoption        meta-analysis  
Received: 31 July 2023   Accepted: 07 May 2024
Fund: 
This work was supported by the National Social Science Fund of China (19BGL152), the Sichuan Technology Planning Project, China (2022JDTD0022), and the Provincial College Student Innovation and Entrepreneurship Training Program of Sichuan Province, China (S202310626018).
About author:  Jinpeng Zou, E-mail: zoujinpeng@stu.edu.cn; Lulin Shen, E-mail: shenlulin@stu.sicau.edu.cn; #Correspondence Fang Wang, E-mail: wangfangscnd@sicau.edu.cn * These authors contributed equally to this study. *These authors contributed equally to this study.

Cite this article: 

Jinpeng Zou, Lulin Shen, Fang Wang, Hong Tang, Ziyang Zhou. 2024. Dual carbon goal and agriculture in China: Exploring key factors influencing farmers’ behavior in adopting low carbon technologies. Journal of Integrative Agriculture, 23(9): 3215-3233.

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