Why urban residents’ deeds do not match their attitude?A dual mediation model of knowledge-behavior in low-carbon consumption
MILingyun, CONGJinqiu, DINGChaoqiong, QIAOLijie, XUTing School of Management, China University of Mining and Technology, Xuzhou 221116, China 收稿日期:2018-11-5 修回日期:2018-12-16 网络出版日期:2019-05-25 版权声明:2019《资源科学》编辑部《资源科学》编辑部 基金资助:中国矿业大学双一流建设文化传承专项“能源矿业经济智库”项目(2018WHCC01) 作者简介: -->作者简介:芈凌云,女,江苏徐州人,博士,副教授,主要研究方向为资源环境行为管理。E-mail: milingyun@126.com
关键词:低碳知识;低碳行为;低碳意愿;低碳能力;结构方程模型;认知失调 Abstract In order to explore the reasons that urban residents’ attitude are not matched by their actions and promote the implementation of low-carbon behavior, this empirical research examined what kind of low-carbon knowledge is more conducive to residents’ low-carbon behavior. First, low-carbon knowledge was classified as system knowledge, action knowledge, and effective knowledge. Then, by extending the knowledge-attitude-behavior theory and introducing low-carbon capability (LCC) as a variable on the basis of low-carbon intention (LCI), a dual mediation model for the mechanism of low-carbon knowledge on low carbon behavior was established. Finally, 1339 valid questionnaires were obtained and analyzed using structural equation model. A multiple group analysis was used to test the moderating effect of demographics. The results indicate that: (1) The difference of mechanism between the three types of low-carbon knowledge and low-carbon behaviors is one of the reasons that why urban residents’ attitude are not matched by their actions. LCI and LCC have significant moderating effects on the relationship between system knowledge and residents’ low-carbon behavior. Meanwhile, action knowledge can directly influence two kinds of low carbon behaviors or affect them indirectly through LCI. LCI is not affected by effective knowledge, but effective knowledge indirectly drives two kinds of low-carbon behaviors through LCC. (2) LCI driven by low-carbon knowledge is more often transformed into low-carbon purchasing behavior, while LCC is more often affected by low-carbon habitual behavior. Therefore, in order to form a long-term mechanism of low-carbon consumption and reduce the “rebound effect” of energy efficiency improvement, it is necessary to pay attention to the cultivation of low-carbon capability. (3) Gender, marital status, age, education level, and personal monthly disposable income have significant moderating effects on the relationship between action knowledge and two types of low-carbon behaviors. Relevant government agencies need to guide and promote the integration of knowledge and behavior among residents in low-carbon comsumption according to the mechanism of different low-carbon knowledge.
Keywords:low-carbon knowledge;low-carbon behavior;low-carbon intention;low-carbon capability;structural equation model;cognitive dissonance -->0 PDF (3114KB)元数据多维度评价相关文章收藏文章 本文引用格式导出EndNoteRisBibtex收藏本文--> 芈凌云, 丛金秋, 丁超琼, 乔丽洁, 许婷. 城市居民低碳行为认知失调的成因——“知识—行为”的双中介模型[J]. 资源科学, 2019, 41(5): 908-918 https://doi.org/10.18402/resci.2019.05.08 MILingyun, CONGJinqiu, DINGChaoqiong, QIAOLijie, XUTing. Why urban residents’ deeds do not match their attitude?A dual mediation model of knowledge-behavior in low-carbon consumption[J]. RESOURCES SCIENCE, 2019, 41(5): 908-918 https://doi.org/10.18402/resci.2019.05.08
综上所述,提出不同类型低碳知识对居民低碳行为的作用机理概念模型M,如图1所示。 显示原图|下载原图ZIP|生成PPT 图1不同类型低碳知识对居民低碳行为的作用机理概念模型M -->Figure 1Conceptual model (M) of the mechanism of action for different types of low-carbon knowledge on low-carbon behavior of residents -->
对1339份有效问卷数据采用SPSS 19.0进行信效度分析。结果显示:各分量表的Cronbach’s α值均大于0.600,表明该量表具有良好的信度。然后采用探索性因子分析对量表进行效度检验发现,自变量(低碳知识)的KMO=0.882(>0.700),且通过Bartlett’s球形检验(p<0.001),主成分分析将低碳知识(系统知识、行动知识、效力知识)的7个测量指标分为3个因子,总的方差解释率为83.1%。因变量(低碳意愿、低碳能力、低碳习惯行为和低碳购买行为)的KMO=0.814(>0.700),也通过了Bartlett’s球形检验(p<0.001),并且主成分分析将因变量的测量指标分为了4个因子,总的方差解释率为67.2%。采用方差最大化正交旋转矩阵之后,各测量指标在其潜变量上的因子载荷均大于0.500,表明量表具有良好的收敛效度;各测量指标在其他潜变量上的因子载荷均小于0.500,表明量表具有良好的区别效度。然后,对自变量和因变量分别进行验证性因子分析,结果显示各项拟合指标均符合要求,说明量表具有较高的效度。结果详见表2。 Table 2 表2 表2正式量表的测量指标及信效度检验 Table 2Measured indicators and reliability and validity tests of the formal scale
为了检验低碳知识和2类低碳行为之间的关系路径是否会因为人口特征因素的不同而存在显著差异,本文采用多群组结构方程来检验人口特征因素的调节效应。 为了检验理论模型是否适配不同的群体,采用多群组结构方程模型进行分析(表3)。本文将所有的样本数据分别按照性别分为男性和女性;按婚姻状况分为已婚和未婚;按年龄分为40岁以下的年轻组和40岁及以上的年长组;按照学历水平,分为本科以下的低学历组和本科及以上的高学历组;按照个人月可支配收入水平分为5000元以下的低收入组和5000元及以上的高收入组。多群组结构模型分析结果表明,各项指标反映多群组分析模型与样本数据适配情况良好:CFI值介于0.966~0.997,都高于0.900的标准值;RMSEA值介于0.021~0.055,都小于0.080的适配临界值,卡方统计量p值达到显著性水平。多群组分析的人口特征因素的调节作用检验结果见表3。 Table 3 表3 表3人口特征因素的调节作用检验结果 Table 3Test results of the effect of population characteristic factors
(1)区分低碳知识的类型,加强低碳行动知识的教育与传播。可以通过教育短片、公益广告、社区活动等形式告知居民关于“怎样做可以减少碳排放”的行动知识,发挥行动知识对2类低碳行为的直接促进作用。 (2)重视效力知识的传播,培养和提升居民的低碳能力。由于效力知识对激发低碳能力作用强度远远高于系统知识,低碳能力比低碳意愿更能促进居民实施低碳习惯行为。日常用能行为的改善可以减少低碳购买带来的“回弹效应”,更有利于形成长效的绿色低碳生活方式。因此,政府和媒体的低碳教育与宣传中需要不断加强“哪种方式更低碳”的效力知识的传播,通过效力知识提升居民的低碳能力,弥补低碳意愿与低碳行为之间的偏差。减少居民在低碳行为上的认知失调。 (3)根据系统知识、行动知识、效力知识对不同人口特征群体的影响效果差异,有区别地设计低碳教育的内容,促进低碳教育效果的不断提升。 The authors have declared that no competing interests exist.
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