Toward a cleaner road: Environmental transformation in Hungary’s automotive sector
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DOIhttp://dx.doi.org/10.21511/ee.16(2).2025.01
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Article InfoVolume 16 2025, Issue #2, pp. 1-14
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A transition toward sustainable logistics is crucial for Hungary’s automotive industry, which remains a main contributor to environmental degradation through its reliance on carbon-based supply chains. This study aims to examine how green technology, policy regulation, and infrastructure availability influence sustainability outcomes for the industry, with a focus on reducing carbon emissions and improving operational efficiency. Partial least squares structural equation modeling with 2015–2023 empirical data was employed. The model examined direct and moderate effects of such factors on lowering carbon footprint and sustainability performance, as well as on implementation cost, firm size, and demand.
The results suggest a significant impact on carbon footprint reduction (path coefficient = 0.32) and sustainability performance (0.38) through the adoption of green technology. Availability of regulatory frameworks (0.29 for reduction of carbon footprint; 0.25 for sustainability performance) and infrastructure (0.35 and 0.40, respectively) also have a significant impact. High implementation costs (–0.22 and –0.18) and the complexity of the supply chain (–0.15 and –0.17) have a negative impact, particularly for small and medium-sized enterprises. Moderation analysis shows that firm size (0.22) and strong demand (0.26) enhance the benefits of adopting green technology.
The indications are toward enhancing regulatory enforcement, raising financial aid schemes, and upgrading logistics infrastructure as a solution for Hungary’s accelerating adoption of sustainable logistics practices. Public-private partnerships are put forward as a strategic solution for bridging infrastructure and investment gaps and enabling long-term economic and environmental advantages for the automotive logistics industry.
- Keywords
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JEL Classification (Paper profile tab)Q56, L62, O33, R41
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References49
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Tables7
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Figures0
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- Table 1. Statistical descriptive analysis
- Table 2. Cronbach’s alpha and composite reliability
- Table 3. Average variance extracted (AVE)
- Table 4. Fornell–Larcker criterion
- Table 5. Path coefficients
- Table 6. Moderation and control analysis
- Table 7. R², Q², f², and VIF tests
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