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Harnessing machine learning to explore influencing mechanism in the dual pro-environmental intention-behavior gap
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  • Published: 05 March 2026

Harnessing machine learning to explore influencing mechanism in the dual pro-environmental intention-behavior gap

  • Zihao Dong1,
  • Yu Zhang1,
  • Yanying Mao2,3,
  • Liudan Jiao1,
  • Xiaosen Huo1 &
  • …
  • Liu Wu1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental social sciences
  • Psychology

Abstract

Fostering pro-environmental behaviors (PEBs) is crucial for advancing low-carbon development. A significant obstacle in this endeavor is the intention-behavior gap, where intentions fail to translate into actual behaviors. This study addresses both the commonly discussed negative gap, where high intentions do not lead to corresponding behaviors, and the less explored positive gap, where behaviors exceed intentions. Drawing from 2216 questionnaires, this study compared eight machine learning methods and selected LightGBM as the optimal approach. And the study examined the impact of individual and situational factors on these two types of gaps by LightGBM. The results identify robust predictive associations: for Cooperative-Grey-PEBs, attitude and ascription of responsibility exhibit inverted U-shaped patterns, while high environmental knowledge supports behavior maintenance in no-intention contexts. For Negative-Grey-PEBs, the negative gap narrows when attitude surpasses a critical threshold (4.5). Furthermore, higher levels of ascription of responsibility and self-efficacy are associated with a lower negative gap. Conversely, high infrastructure visibility is characterized by a divergent pattern, where it correlates with an expanded negative gap, consistent with a “responsibility dilution effect”. The study proposes tailored measures for different groups, which would have significant implications for policies aiming to bolster low-carbon development.

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Data availability

All data included in this study are available upon request by contacting the corresponding author.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No.72204033, 72474039), the Science and Technology Research Project of Chongqing Education Commission (Grant No. KJQN202303120) and the Natural Science Foundation of Chongqing [CSTB2025NSCQ-GPX0145].

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Authors and Affiliations

  1. School of Economics and Management, Chongqing Jiaotong University, Chongqing, 400074, China

    Zihao Dong, Yu Zhang, Liudan Jiao, Xiaosen Huo & Liu Wu

  2. Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Yanying Mao

  3. Department of Communication Engineering, Chongqing College of Electronic Engineering, Chongqing, 401331, China

    Yanying Mao

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  1. Zihao Dong
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  2. Yu Zhang
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Contributions

All authors read and approved the final manuscript. The authors confirm their contribution to the paper: Z.D. writing—original draft preparation, conceptualization, formal analysis, data curation, visualization, supervision. Y.Z. writing—reviewing and editing, visualization, and formal analysis. Y.M. conceptualization, data curation, supervision, writing—reviewing and editing. L.J. methodology, formal analysis, software, visualization. X.H. conceptualization, supervision. L.W. conceptualization, supervision.

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Correspondence to Yu Zhang.

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The research involved an anonymous online questionnaire without any personal identifiers or sensitive information. Therefore, the need for formal ethics approval was deemed unnecessary according to national regulations (Measures for Ethical Review of Life Sciences and Medical Research Involving Human Beings, National Health Commission of the People’s Republic of China, 2023).

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Dong, Z., Zhang, Y., Mao, Y. et al. Harnessing machine learning to explore influencing mechanism in the dual pro-environmental intention-behavior gap. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42468-1

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  • Received: 21 October 2025

  • Accepted: 25 February 2026

  • Published: 05 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42468-1

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Keywords

  • Machine learning
  • Pro-environmental behaviors
  • Intention-behavior gap
  • Color-code model
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