Green Transportation Acceptance and Carbon Footprint Reduction in Supply Chain Logistics

Road transport is one of the largest contributors to greenhouse gas (GHG) emissions in modern supply chains, yet surprisingly little is known about why individual logistics workers respond to their carbon footprint the way they do especially in Malaysia. This study explores how three factors which are awareness of carbon footprint, transportation habits, and perception and acceptance of green transportation in shaping the transportation-related carbon footprint among road transport users in Pulau Pinang, Malaysia. Drawing on the Theory of Planned Behaviour (TPB), the study adopted a quantitative cross-sectional design. Data were gathered from 108 respondents using a structured five-point Likert-scale questionnaire and analysed through descriptive statistics, Pearson correlation, and multiple linear regression in IBM SPSS Statistics. All constructs showed good internal reliability (Cronbach’s α = 0.773–0.864). Correlation results showed that awareness of carbon footprint (r = 0.565, p < 0.001) and transportation habits (r = 0.516, p < 0.001) had moderate positive relationships with carbon footprint, while green transportation acceptance showed a weaker but significant link (r = 0.229, p = 0.015). Regression analysis identified awareness (β = 0.413, p < 0.001) and transportation habits (β = 0.300, p = 0.003) as the two significant predictors, together explaining 37.2% of the variance (R² = 0.372, F(3, 104) = 21.565, p < 0.001). Green transportation acceptance did not independently predict carbon footprint (β = −0.047, p = 0.598) once the other variables were accounted for. These results highlight how deeply awareness and ingrained travel behaviour influence the carbon footprint of logistics workers, and point to practical steps — including targeted education campaigns and infrastructure investment — that policymakers and logistics managers can take to reduce supply chain emissions in Malaysia.

green transportation; carbon footprint; supply chain logistics; transportation habits

1. Ajzen, I. (1991). The theory of planned behavior. ‘Organizational Behavior and Human Decision Processes’, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T [Google Scholar] [Crossref]

2. Bettenhausen, C. L. (2022). Greenhouse gas accounting and reporting in the supply chain. ‘Journal of Cleaner Production’, 340, 130764. [Google Scholar] [Crossref]

3. Creswell, J. W., & Creswell, J. D. (2018). ‘Research design: Qualitative, quantitative, and mixed methods approaches’ (5th ed.). SAGE Publications. [Google Scholar] [Crossref]

4. Fang, M., Chan, H. L., & Xu, X. (2022). Supply chain management and environmental sustainability: A review. ‘International Journal of Production Economics’, 244, 108398. [Google Scholar] [Crossref]

5. Gadgil, P. (2024). Green supply chain management practices and sustainability outcomes: A systematic review. ‘Sustainable Production and Consumption’, 45, 112–131. [Google Scholar] [Crossref]

6. Golnar, M., & Beškovnik, B. (2024). Carbon footprint awareness and sustainable transportation behaviour: An empirical study. ‘Journal of Sustainable Transportation’, 18(3), 201–218. [Google Scholar] [Crossref]

7. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). ‘Multivariate data analysis’ (8th ed.). Cengage Learning. [Google Scholar] [Crossref]

8. Husain, Z., Ibrahim, R., & Mohamad, S. (2023). Environmental awareness and pro-environmental transportation intentions in Malaysia. ‘Asian Journal of Sustainability’, 11(2), 55–72. [Google Scholar] [Crossref]

9. International Energy Agency. (2022). ‘Malaysia energy profile’. IEA. https://www.iea.org/countries/malaysia [Google Scholar] [Crossref]

10. Ling, J., Tan, C., & Ahmad, R. (2024). Behavioural predictors of green transportation adoption: A theory of planned behaviour approach. ‘Transportation Research Part D: Transport and Environment’, 128, 104089. [Google Scholar] [Crossref]

11. Nagy, A., & Szentesi, S. (2024). Heavy vehicle use and transport emissions: Evidence from the Hungarian freight sector. ‘European Transport Research Review’, 16(1), 7. [Google Scholar] [Crossref]

12. Rahal, D., & Elloumi, M. (2024). User perceptions and the adoption of sustainable logistics practices: A structural equation modelling approach. ‘Sustainability’, 16(4), 1423. [Google Scholar] [Crossref]

13. Ramos, P., Valério, M., & Moreira, J. (2020). Travel habits, modal choice, and environmental impact: A multivariate analysis. ‘Transportation Research Part A: Policy and Practice’, 138, 205–221. [Google Scholar] [Crossref]

14. Shahrilmd, S. R. (2024). Environmental externalities of the Malaysian logistics industry: A policy review. ‘Malaysian Journal of Sustainable Development’, 3(1), 29–45. [Google Scholar] [Crossref]

15. Ushakov, D., Mikhaylov, A., & Mikhaylova, A. (2024). Green transportation alternatives and user acceptance: A global review. ‘Energies’, 17(3), 712. [Google Scholar] [Crossref]

16. Wang, Q., Li, R., & Jiang, R. (2025). Determinants of transportation carbon emissions in supply chains: A meta-analytic review. ‘Journal of Cleaner Production’, 388, 135968. [Google Scholar] [Crossref]

17. Yaacob, N., Hassan, M. S., & Isa, Z. (2020). Transportation-related carbon footprint in Malaysia: Trends and measurement. ‘Journal of Environmental Management’, 267, 110597. [Google Scholar] [Crossref]

18. Zhang, Y., Liu, Y., & Chen, H. (2023). Driver behaviour and freight emissions: Habitual patterns and environmental impact in logistics. ‘International Journal of Logistics Research and Applications’, 26(5), 589–607. [Google Scholar] [Crossref]

• The Effectiveness of Logistics Management - A Case Study of: Future Supply Chain Solutions Ltd (FSC)
• The Effect of E-Informing on Procurement Performance of Public Universities in Kenya: The Case of Egerton University
• Barriers to Circular Supply Chain Implementation in Indian Textile SMEs: An Exploratory Qualitative Study
• Sustainable Supply Chain to Enhance the Environmental Performance in the Cleaning Detergents Manufacturing Industry: Case of Harare, Zimbabwe
• Exploring Innovative Frontiers: Digital Public Infrastructure and Multimodal Connectivity as Catalysts for MSME Supply Chain Transformation in India