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
Scenarios
The data presented in Sections 4 and 5 is used to find robust, consistent and plausible future projection bundles (aka raw scenarios). Due to the amount of data and the combinatorial problem involved (see C.6) this process is software based utilizing evolve:IT’s ScenLab v1.7. Further, the genetic algorithm mode of ScenLab was used to find raw scenarios. Due to the stochastic nature of this algorithm it was run 10 times to find the most robust, consistent and plausible raw scenarios respectively. The results for the multiple runs were consistent, i.e. the top 30 raw scenarios were the same in the respective runs of the algorithm.
From ScenLab’s output a variety of raw scenarios (Table 1) was selected for further production of the final scenarios. The criteria employed for selection of raw scenarios were: (i) no more than two partial inconsistencies, (ii) ideally a combination of high robustness, consistency and plausibility values, (iii) diversity of the set of selected raw scenarios.
Decsription | r | c | p | pI | Wild Card |
---|---|---|---|---|---|
Scenario 1 | |||||
Most robust | 0.7239 | 0.1433 | 6.69 × 10−7 | 0 | none |
Robust, consistent, plausible | 0.7086 | 0.1579 | 2.01 × 10−7 | 0 | none |
Robust, plausible | 0.5150 | 0.1404 | 9.37 × 10−7 | 1 | none |
Robust, Wild Card | 0.4632 | 0.1520 | 6.76 × 10−9 | 1 | Hot Cold War |
Scenario 2 | |||||
Most consistent | 0.3805 | 0.4004 | 5.00 × 10−12 | 0 | none |
Scenario 3 | |||||
Most plausible | 0.4212 | 0.1287 | 1.17 × 10−6 | 2 | none |
Plausible, variation | 0.4206 | 0.1228 | 1.17 × 10−6 | 2 | none |
Plausible, variation | 0.4203 | 0.1199 | 1.17 × 10−6 | 2 | none |
Plausible, Wild Card | 0.3767 | 0.1345 | 6.76 × 10−7 | 2 | Hot Cold War |
Scenario 4 | |||||
Scenario 4 | 0.4272 | 0.2953 | 1.10 × 10−10 | 1 | none |
Scenario 4, Wild Card | 0.3658 | 0.3480 | 1.11 × 10−12 | 1 | Hot Cold War |
Sections 7.1-7.3 describe scenarios derived from highest scoring raw scenarios, that is, from the most robust, the most consistent and the most plausible raw scenario respectively. The scenario in Section 7.4 is given to diversify the range of described futures. The raw scenarios utilized for this are in no way special, but were picked deliberately because of their difference to the first three scenarios.
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