The impact mechanism of media use on the green and low carbon consumption behavior of China: integrating planned behavior theory and social cognitive theory

As a crucial medium for information dissemination and public opinion guidance, how to leverage media to promote the development of public green and low-carbon consumption (GLC) is a major challenge in the new era. This study, based on the Theory of Planned Behavior and Social Cognitive Theory, employs PLS-SEM and MGA methods to construct a model encompassing traditional media, online media, and social media channels, delving into the relationship between media use (MU) and GLC. The findings indicate that media usage is positively correlated with both aspects of GLC: green and low-carbon consumption intention, green and low-carbon consumption behavior. The order of correlation strength is social media > traditional media > online media. Further mechanistic analysis indicates that green and low-carbon knowledge and social norms mediate the relationship between MU and GLC. Additionally, there are differences in the correlation between MU and GLC among populations with varying levels of media trust and media use frequency. This study provides theoretical and quantitative guidance for leveraging traditional media, online media, and social media to promote GLC among the Chinese public.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-08-15
DOI
https://doi.org/10.1038/s41598-025-14905-0
Citations
1
Primary Topic
Environmental Education and Sustainability
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact mechanism of media use on the green and low carbon consumption behavior of China: integrating planned behavior theory and social cognitive theory

Yating Niu, Shuangchen Ruan, Chuang Li, Liping Wang
1 citations
Scientific Reports
Environmental Education and Sustainability
2.42
article

The impact mechanism of media use on the green and low carbon consumption behavior of China: integrating planned behavior theory and social cognitive theory

Yating Niu, Shuangchen Ruan, Chuang Li, Liping Wang
article en
1 citations

Abstract

As a crucial medium for information dissemination and public opinion guidance, how to leverage media to promote the development of public green and low-carbon consumption (GLC) is a major challenge in the new era. This study, based on the Theory of Planned Behavior and Social Cognitive Theory, employs PLS-SEM and MGA methods to construct a model encompassing traditional media, online media, and social media channels, delving into the relationship between media use (MU) and GLC. The findings indicate that media usage is positively correlated with both aspects of GLC: green and low-carbon consumption intention, green and low-carbon consumption behavior. The order of correlation strength is social media > traditional media > online media. Further mechanistic analysis indicates that green and low-carbon knowledge and social norms mediate the relationship between MU and GLC. Additionally, there are differences in the correlation between MU and GLC among populations with varying levels of media trust and media use frequency. This study provides theoretical and quantitative guidance for leveraging traditional media, online media, and social media to promote GLC among the Chinese public.

Scientific ReportsVol. 15(1)
Jimei University (CN)
National Office for Philosophy and Social Sciences, National Social Science Fund of China
Responsible consumption and production
Openalex Percentile: Top 10%
Environmental Education and Sustainability
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.