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Diversification of import networks for green hydrogen: a case study for Germany

Louis Vincent Sroka & Frank Meisel

Journal of Business Economics2026https://doi.org/10.1007/s11573-025-01248-5article
AJG 2ABDC B
Weight
0.37

Abstract

Green hydrogen is a crucial component in the transition to a low-carbon economy. However, large-scale production is limited by geographical conditions in many regions, which requires hydrogen imports to meet demand. To ensure supply security and avoid dependence on singular exporters, sourcing from multiple regions within a diversified import network is essential. This study presents a mixed-interger linear programming (MILP) model that determines an optimal import network for green hydrogen, considering diversification constraints and liquid hydrogen losses during transport due to boil-off. Our case study, focusing on the diversified import of green hydrogen to Germany, reveals that production and conversion operating costs dominate the overall cost structure and transporting hydrogen as ammonia is the most cost-efficient option in most cases. Further analysis indicates that enforced diversification increases total costs and influences the structure of the import network. Lastly, we demonstrate that reducing the boil-off rate of liquid hydrogen during transport may lead to liquid hydrogen being a more cost-effective carrier than ammonia.

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https://doi.org/https://doi.org/10.1007/s11573-025-01248-5

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@article{louis2026,
  title        = {{Diversification of import networks for green hydrogen: a case study for Germany}},
  author       = {Louis Vincent Sroka & Frank Meisel},
  journal      = {Journal of Business Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s11573-025-01248-5},
}

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Diversification of import networks for green hydrogen: a case study for Germany

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.