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中国消费者低碳产品支付意愿的差异分析——基于碳标签的情景实验数据 被引量:75

Variance Analysis of Consumers' Willingness to Pay for Low-Carbon Products in China Based on Scenario Experiment with Carbon Labeling
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摘要 为了引导低碳消费和施行低碳经济,碳标签应运而生。作为一种产品全生命周期内温室气体排放量的标识方法,它必将对不同类型的消费者对贴有碳标签的低碳产品的消费行为产生影响。本文根据课题组在我国四省六市开展的碳标签情景实验数据,首先采用单因素方差分析的Dunnett's T3检验方法,对不同地区、不同年龄段、不同学历层次、不同收入水平和不同性别的消费者对贴有碳标签的低碳产品支付意愿的差异分别进行了检验,以期发现各样本组之间差异的显著性;然后,采用Logistic回归模型对购买贴有碳标签农产品意愿与人口变量之间的关系进行了回归分析。结果显示:(1)不同类型的消费者对贴有碳标签的低碳产品的支付意愿具有显著差异;(2)学历层次和月收入水平等变量对消费者低碳产品的支付意愿具有显著影响。最后,本文基于以上研究结论,提出了相应的政策建议。 In order to guide low carbon consumption and implement low carbon economy, carbon labeling came into be- ing. As the identification of the greenhouse gas emissions in the full life cycle of a product, carbon labeling will certainly exert impact on the behavior of different types of consumers for low-carbon products with carbon labels. Based on the data from scenario carbon labeling experiment conducted by the research group in four provinces and six cities in China, we firstly adopt Dunnettg T3 test methods for the single factor variance analysis to test the differences of consumers'willing- ness to pay for the low-carbon products with carbon labels of consumers at different ages, different educational hack- grounds, different income levels and different gender,in an attempt to find their significant differences. Then, we utilize a logistic regression model to analyze the relationship between the willingness to pay for low-carbon products with carbon labels and the demographic variables. The results indicate : ( 1 ) the willingness to pay for low-carbon products with carbon labels of different types of consumers is significant ; ( 2 ) educational and monthly income variables have significant impacts on consumers willingness to pay for low-carbon products. Finally, we put forward relevant policy recommendations based on the above conclusions.
出处 《中国软科学》 CSSCI 北大核心 2013年第7期61-70,共10页 China Soft Science
基金 国家自然科学基金项目"碳关税和碳标签对农产品贸易和减排的影响机制研究"(编号:71173201)
关键词 碳标签 低碳产品 支付意愿 单因素方差分析 Logistic回归 carbon labeling low-carbon products willingness to pay single factor variance analysis Logistic regres-sion
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