A Study on the Factors Influencing Low-Carbon Behavior Among Residents in Chongqing

Zihao Dong (), Yu Zhang and Xiaoseng Huo
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Zihao Dong: Chongqing Jiaotong University
Yu Zhang: Chongqing Jiaotong University
Xiaoseng Huo: Chongqing Jiaotong University

Chapter Chapter 12 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 165-180 from Springer

Abstract: Abstract In the past few years, there has been growing recognition and widespread attention towards the severity of global climate change and environmental issues. Low-carbon behavior has become one of the important ways to address these problems. This article utilizes a sample of 585 Chongqing residents collected via a questionnaire survey as the research subject, and employs a binary logistic regression model to analyze the factors that influence low-carbon behavior. Additionally, an interpretive structural modeling (ISM) is constructed to examine the logical relationship between the various factors influencing low-carbon behavior. The research results show that: (1) Based on the results of binary logistic regression, this study reveals that low-carbon efficacy, behavioral willingness, low-carbon cognition, sense of responsibility, social pressure, infrastructure, and policy are all significant factors that impact low-carbon behavior. Among them, low-carbon efficacy, behavioral willingness, low-carbon awareness, and sense of responsibility have a significant positive effect on low-carbon behavior, while social pressure and policy have a significant negative effect on low-carbon behavior. (2) The fundamental factors that influence low-carbon behavior, as per the ISM analysis, are policy and infrastructure. Intermediate factors that exert an impact on low-carbon behavior encompass low-carbon efficacy, sense of responsibility, social pressure, and low-carbon cognition. Finally, behavioral willingness functions as a direct determinant of low-carbon behavior.

Keywords: Low-carbon behaviour; Residents; Logistic model; ISM (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-1949-5_12

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