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Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model

Author

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  • Qiaoling Wang

    (Beijing Academy of Educational Sciences, Beijing 100036, China)

  • Ziyu Kou

    (College of Teacher Education, Capital Normal University, Beijing 100048, China)

  • Xiaodan Sun

    (Institute of Education, University College London, London WC1E 6BT, UK)

  • Shanshan Wang

    (Department of Foreign Language, Guangdong University of Science & Technology, Dongguan 523070, China)

  • Xianjuan Wang

    (Beijing Academy of Educational Sciences, Beijing 100036, China)

  • Hui Jing

    (National Center for Schooling Development Programme, Ministry of Education, Beijing 100032, China)

  • Peiying Lin

    (College of Teacher Education, Capital Normal University, Beijing 100048, China)

Abstract

The emergence of the COVID-19 pandemic has hindered the achievement of the global Sustainable Development Goals (SDGs). Pro-environmental behaviour contributes to the achievement of the SDGs, and UNESCO considers college students as major contributors. There is a scarcity of research on college student pro-environmental behaviour and even less on the use of decision trees to predict pro-environmental behaviour. Therefore, this study aims to investigate the validity of applying a modified C5.0 decision-tree model to predict college student pro-environmental behaviour and to determine which variables can be used as predictors of such behaviour. To address these questions, 334 university students in Guangdong Province, China, completed a questionnaire that consisted of seven parts: the Perceived Behavioural Control Scale, the Social Identity Scale, the Innovative Behaviour Scale, the Sense of Place Scale, the Subjective Norms Scale, the Environmental Activism Scale, and the willingness to behave in an environmentally responsible manner scale. A modified C5.0 decision-tree model was also used to make predictions. The results showed that the main predictor variables for pro-environmental behaviour were willingness to behave in an environmentally responsible manner, innovative behaviour, and perceived behavioural control. The importance of willingness to behave in an environmentally responsible manner was 0.1562, the importance of innovative behaviour was 0.1404, and the perceived behavioural control was 0.1322. Secondly, there are 63.88% of those with high pro-environmental behaviour. Therefore, we conclude that the decision tree model is valid in predicting the pro-environmental behaviour of college student. The predictor variables for pro-environmental behaviour were, in order of importance: Willingness to behave in an environmentally responsible manner, Environmental Activism, Subjective Norms, Sense of Place, Innovative Behaviour, Social Identity, and Perceived Behavioural Control. This study establishes a link between machine learning and pro-environmental behaviour and broadens understanding of pro-environmental behaviour. It provides a research support with improving people’s sustainable development philosophy and behaviour.

Suggested Citation

  • Qiaoling Wang & Ziyu Kou & Xiaodan Sun & Shanshan Wang & Xianjuan Wang & Hui Jing & Peiying Lin, 2022. "Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model," IJERPH, MDPI, vol. 19(15), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9407-:d:877236
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    References listed on IDEAS

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    1. Beibei Yue & Guanghua Sheng & Shengxiang She & Jiaqi Xu, 2020. "Impact of Consumer Environmental Responsibility on Green Consumption Behavior in China: The Role of Environmental Concern and Price Sensitivity," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
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    4. Raza Hasan & Sellappan Palaniappan & Salman Mahmood & Kamal Uddin Sarker & Ali Abbas, 2020. "Modelling and predicting student's academic performance using classification data mining techniques," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 34(3), pages 403-422.
    5. Sunyoung Park, 2019. "Identification of Overall Innovation Behavior by Using a Decision Tree: The Case of a Korean Manufacturer," Sustainability, MDPI, vol. 11(22), pages 1-54, November.
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    1. Gregor Wolbring & Simerta Gill, 2023. "Potential Impact of Environmental Activism: A Survey and a Scoping Review," Sustainability, MDPI, vol. 15(4), pages 1-46, February.
    2. Jianzhen Zhang & Ziyang Wang & Collins Opoku Antwi & Xiaoyu Liang & Jiahao Ge, 2022. "Geospatial Thinking and Sense of Place: The Mediating Role of Creativity," Sustainability, MDPI, vol. 15(1), pages 1-16, December.

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