Unlocking the nexus of supply chain efficiency in electric vehicle markets: A system thinking approach
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DOIhttp://dx.doi.org/10.21511/im.21(3).2025.03
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Article InfoVolume 21 2025, Issue #3, pp. 31-46
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Creative Commons Attribution 4.0 International License
Electric vehicles (EVs) present a promising pathway to reduce carbon emissions, yet their adoption in developing markets remains constrained by high production costs, infrastructure gaps, and supply chain inefficiencies. This study investigates the Indonesian EV market using a mixed-method approach, combining techno-economic analysis with a system thinking framework. Primary data were collected through interviews and FGDs with eight stakeholders, while secondary data from 2020-2024 supported trend analysis. The study employs a Causal Loop Diagram (CLD) to map systemic interactions among policy, technology, and market forces. The results suggest that a 25% increase in subsidies and a 40% infrastructure expansion could lead to a 60% rise in EV adoption, assuming battery prices remain stable. Policy and marketing strategies, such as targeted incentives and circular economy practices like localized battery recycling, emerge as key leverage points for building a sustainable and resilient EV supply chain. The findings offer actionable insights for developing markets to design integrated policies that align economic incentives with clean mobility goals.
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JEL Classification (Paper profile tab)Q56, M11, O13, R41, M31, M37
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References28
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Tables6
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Figures4
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- Figure 1. Simple causal loop diagram for the electric vehicle market
- Figure 2. Research design workflow and CLD construction process
- Figure 3. Integrated CLD macro, micro, interface and policies
- Figure 4. Techno-economic data trends (2020-2024)
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- Table 1. Techno-economic data for CLD validation
- Table 2. Experience and expertise of FGD participants
- Table 3. Triangulated data processing results
- Table 4. Techno-economic data for CLD validation
- Table 5. Experience and expertise of FGD participants
- Table 6. Triangulated data processing results
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