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The main aim is to explore the factors influencing the intention-behavior gap in pro-environmental behaviors using machine learning techniques.

March 7, 2026Scientific ReportsOpen Access

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

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The main aim is to explore the factors influencing the intention-behavior gap in pro-environmental behaviors using machine learning techniques.
Analyzed data from 2216 questionnaires to identify influencing factors.
Compared eight machine learning methods, selecting LightGBM as the most effective.
Examined individual and situational factors affecting both negative and positive gaps.
Identified inverted U-shaped relationships for attitude and ascription of responsibility in Cooperative-Grey-PEBs.
High environmental knowledge facilitates behavior without intention.
Attitude surpassing a threshold reduces the negative gap in Negative-Grey-PEBs.
Increased ascription of responsibility and self-efficacy diminishes the negative gap.
High infrastructure visibility correlates with a widening negative gap due to responsibility dilution.

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Examines influencing mechanisms of intention-behavior gaps in pro-environmental behaviors, highlighting implications for policy design.

Dong et al. (2026) studied this question.

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