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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Zihao Dong,
  • Yu Zhang,
  • Yanying Mao,
  • Liudan Jiao,
  • Xiaosen Huo,
  • Liu Wu

摘要

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.