Investigation of predictors of air pollution-reducing behaviors among taxi drivers in Tehran based on the health belief model in 2024
Abstract Eco-driving behaviors among taxi drivers can significantly contribute to reducing urban air pollution, particularly in megacities such as Tehran. This study aimed to identify the psychosocial predictors of air pollution-reducing behaviors among taxi drivers based on the Health Belief Model...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-04288-7 |
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| Summary: | Abstract Eco-driving behaviors among taxi drivers can significantly contribute to reducing urban air pollution, particularly in megacities such as Tehran. This study aimed to identify the psychosocial predictors of air pollution-reducing behaviors among taxi drivers based on the Health Belief Model (HBM). A cross-sectional descriptive-analytical study was conducted in late 2024 with 401 taxi drivers in Tehran, using structured face-to-face interviews. A validated questionnaire assessed seven HBM constructs and eco-driving behaviors. Pearson correlation and stepwise multiple regression analyses were applied. The findings revealed that self-efficacy was the strongest predictor of eco-driving behavior (β = 0.324, p < 0.001), followed by awareness, perceived benefits, and perceived susceptibility. The final regression model explained 27.1% of the variance in eco-driving behavior (R 2 = 0.271). Although perceived barriers showed a significant negative correlation with eco-driving, it was not a significant predictor in the final model. These results highlight the importance of enhancing drivers’ self-efficacy and awareness of eco-driving benefits, while addressing motivational and contextual barriers. Developing targeted, theory-driven educational interventions based on the HBM may play a critical role in promoting eco-driving practices and improving air quality in high-pollution urban environments. |
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| ISSN: | 2045-2322 |