Literature DB >> 31150893

Who are the low-carbon activists? Analysis of the influence mechanism and group characteristics of low-carbon behavior in Tianjin, China.

Weidong Chen1, Jingqian Li2.   

Abstract

Individual low-carbon behavior plays an important role in reducing carbon emissions and improving the ecological health of the environment. This study explored the factors that influence individuals' low-carbon behavior and the variations between different groups. A questionnaire measuring seven dimensions (including low-carbon awareness, low-carbon knowledge, personal norms, social norms, situational factors, private low-carbon behavior, and public low-carbon behavior) was distributed to the residents of Tianjin, yielding 418 valid responses. The results indicated that low-carbon awareness, low-carbon knowledge, personal norms, social norms, and situational factors had an impact on residents' low-carbon behavior. In particular, the level of public low-carbon behavior was higher than private low-carbon behavior. Second, by exploring the effects of situational factors on residents' low-carbon behaviors, we found that situational factors inhibited both private and public low-carbon behaviors. Third, in different groups based on age, gender, income, education, and other variables there were differences in impact effects. This research has significant potential for guiding residents' low-carbon behavior and improving low-carbon management.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  China; Group characteristics; Low-carbon behavior; Multi-group analysis; Structural equation model; Survey data

Mesh:

Substances:

Year:  2019        PMID: 31150893     DOI: 10.1016/j.scitotenv.2019.05.307

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

1.  Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network.

Authors:  Chai Ziyuan; Yan Yibo; Zibibula Simayi; Yang Shengtian; Maliyamuguli Abulimiti; Wang Yuqing
Journal:  Environ Sci Pollut Res Int       Date:  2022-01-11       Impact factor: 5.190

2.  Does Fear of the New Coronavirus Lead to Low-Carbon Behaviors: The Moderating Effect of Outcome Framing.

Authors:  Wenlong Liu; Wen Shao; Qunwei Wang
Journal:  Risk Manag Healthc Policy       Date:  2021-10-07

3.  Exploring built environment factors on e-bike travel behavior in urban China: A case study of Jinan.

Authors:  Yonghao Yu; Yuxiao Jiang; Ning Qiu; Heng Guo; Xinyu Han; Yuanyuan Guo
Journal:  Front Public Health       Date:  2022-09-12

4.  Key Factors Influencing Low-Carbon Behaviors of Staff in Star-Rated Hotels-An Empirical Study of Eastern China.

Authors:  Jie Li; Peng Mao; Hui Liu; Jiawei Wei; Hongyang Li; Jingfeng Yuan
Journal:  Int J Environ Res Public Health       Date:  2020-11-06       Impact factor: 3.390

5.  How Does Environmentally Specific Servant Leadership Fuel Employees' Low-Carbon Behavior? The Role of Environmental Self-Accountability and Power Distance Orientation.

Authors:  Yuhuan Xia; Yubo Liu; Changlin Han; Yang Gao; Yuanyuan Lan
Journal:  Int J Environ Res Public Health       Date:  2022-03-04       Impact factor: 3.390

  5 in total

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