Sea Ice
- About
- Imprint
- Scenarios
- Arctic Marine Transportation by 2030
- Introduction
- Aim of this Study
- Key Factor Classification
- Definitions of Key Factors and Future Projections
- 1. Climate
- 2. Legal framework
- 3. Global Trade Dynamics – Global economic growth
- 4. Safety of other Routes
- 5. Socio-economic impact of global climate change
- 6. Oil Price
- 7. Major Arctic Shipping Disasters
- 8. Windows of Operation
- 9. Maritime Insurance Industry
- 10. Collaboration in resource extraction by China, Japan and Russia
- 11. Transit fees
- 12. Conflict between indigenous and commercial use
- 13. Arctic Enforcers
- 14. Energy sources for propulsion
- 15. New resource discovery
- 16. World Trade Patterns
- 17. Regulation in the Arctic
- Consistency matrix
- Scenarios
- Suggest Wild Cards
- Suggest Key Factors
- References
- Glossary
- Yakutat Community Energy Scenarios
- Introduction to Scenario-Management
- The Consistency and Robustness Analysis
- 1. Key Factors and their Future Projections
- 2. Assigning plausibility values to future projections
- 3. Projection Bundles
- 4. Assigning consistency values
- 5. Obtaining overall consistency values for the projection bundles
- 6. The combinatorial problem of the consistency analysis
- 7. The Robustness of a projection bundle
- Disruptive event analysis – Wild Cards
- ScenLab v1.7 Client download
- Arctic Marine Transportation by 2030
Aim of this Study
The aim of this study is to supply decision makers concerned with the Arctic Oceans with a base for planing the next forty years. While the AMSA Workshop supplied us with an in-depth brain-storming by experts concerned with different aspects of the Arctic, we are aiming to use this knowledge base to further refine the scenario process. This refinement is done in several stages: (i)further research into and the clear definition of KF and FP in this document, (ii) the evaluation of the gathered information utilizing the Robustness Analysis, (iii) an in-depth analysis of the data obtained through the software supported raw scenario building and (iv) the refinement of the results from the previous step into three to five scenarios with broad background information.
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