A systematic approach to qualitative scenarios

Transcription

1 Systematic approaches to A systematic approach to qualitative scenarios qualitative scenarios Vanessa Schweizer, Assistant Professor Department of Knowledge Integration IRGC Workshop on Energy Scenarios and Models: Improving Methods to Assess Future Energy Demand, October 9-10, 2014, Karlsruhe

2 Socioeconomic scenarios: integrated qualitative and quantitative information Integration through Story and Simulation (Alcamo, 2001) (Nakicenovic et al in Raskin et al. 2005) 2

3 Retrospective on the IPCC SRES storylines (Nakicenovic et al. 2000) (Schweizer & Kriegler 2012) 3

4 Prospective (exploratory) study on new Shared Socioeconomic Pathways (SSPs) How many SSP types needed (only 3, or more)? What would different SSPs be? Can CIB derive new scenarios that are internally consistent? (O Neill et al. 2014) 4

5 Number of combinations CIB inconsistency scores sort possible scenarios 180, , , , ,000 80,000 60,000 40,000 20, (Best) Inconsistency score Cumulative # combinations > 1.5 million (Worst) (Schweizer & O Neill, 2014) 5

6 1000 best scenarios mapped in challenges space Mitigation challenges dominate Low challenges Medium challenges High challenges Adaptation challenges dominate 1: Low challenges 2: Medium challenges 3: High challenges 4: Adapt > Mit challenge 5: Mit > Adapt challenge (Schweizer & O Neill, 2014) 6

7 Mitigation challenges Both Adaptation challenges Type Pop E int C int Tech Ag prod Innov Gov -H2O GDP X pov Educ Urb Coasts Low Med High A > M M > A Summary of scenario characteristics (What different SSPs could be) Finding: There should be at least 5 types of SSPs (Schweizer & O Neill, 2014) 7

8 Summary: The benefits of a systematic approach Retrospectively, CIB can assess the internal consistency of finished scenarios Prospective/Exploratory CIB can generate new scenarios from scratch Ensure internal consistency of scenarios from the beginning New direction: Studying inconsistent scenarios Nuances also exist among inconsistent scenarios Studying inconsistent scenarios may be important for understanding transition pathways, resilience 8

9 References Alcamo (2001) Scenarios as a tool for international environmental assessments. Environmental issue report 24. Copenhagen: EEA. Nakicenovic et al. (2000) Special Report on Emissions Scenarios, New York, Cambridge University Press. O Neill et al. (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change, doi: /s Raskin et al. (2005) Global scenarios in historical perspective. In S.R. Carpenter et al. (Eds.), Ecosystems and human well-being: Scenarios, Volume 2. Washington: Island Press. Schweizer and Kriegler (2012) Improving environmental change research with systematic techniques for qualitative scenarios. Environmental Research Letters, 7, Schweizer and O Neill (2014) Systematic construction of global socioeconomic pathways using internally consistent element combinations. Climatic Change, 122:

10 BACKUP

11 Climate scenarios: integrated qualitative and quantitative information Traditional Scenario Process (Hibbard et al. 2007) Handled by integrated assessment Handled by climate models, GCMs Integration through Story and Simulation (Alcamo, 2001) (Nakicenovic et al in Raskin et al. 2005) 11

12 Qualitative scenarios as dual-use objects Rooting scenarios in firmly held, pre-existing beliefs about how the world works leads to the uptake or credibility of scenarios (Selin, 2006) Scenarios [can] enhance expectancies that an event will occur. This can be useful for gaining acceptance of a forecast (Gregory, 2001) Surreptitiously shaping someone s views through such manipulation is not appropriate in policy analysis, where the objective should be to give decision makers balanced and unbiased assessments on which to base their decisions (Morgan and Keith, 2008) 12

13 CIB inconsistency scores as ranks Pop GDP/capita FFA C intensity PE intensity EcP EnP L M H L M H VH L Co H VLC LC B HC L M H R G R G Population Low (< 8 billion) Medium (8-12 billion) High (>12 billion) GDP growth per capita Low (< 1.4%) Medium (1.4%-2.0%) High (2.0%-2.6%) Very high (> 2.6%) Fossil fuel availability Low fossils Low oil/gas (High coal) High fossils Carbon intensity Very low C (< 6% oil/coal) Low C (6% oil/coal < 30%) Balanced (30% oil/coal < 50%) C intensive (oil/coal 50%) Primary energy intensity Low (< 4.3 MJ/$) Medium ( MJ/$) High (> 6.5 MJ/S) Economic policy orientation Regional Global Environmental policy orientation Regional Global GIVEN SCENARIO STATES: Impact balance scores: TARGET SCENARIO STATES: (Schweizer & Kriegler 2012) Inconsistency score: 2 (-1) = 3 13

14 Historical and projected trends for expert elicitation (Schweizer & O Neill, 2014) 14

15 Differences in assumptions for factor weights (Schweizer & O Neill, 2014) 15

16 Common characteristics in each challenges domain 100% 80% 60% Population trends Factor trend: Low Medium High 40% 20% Challenges type: 0% Low Med High Adapt > Mit Mit > Adapt Mitigation challenges Both Adaptation challenges Type Pop E int C int Tech Ag prod Innov Gov -H2O GDP X pov Educ Urb Coasts Low Med High A > M M > A 16

17 Governance and High adaptation challenges Mitigation challenges Both Adaptation challenges Type Pop E int C int Tech Ag prod Innov Gov -H2O GDP X pov Educ Urb Coasts Low Med High A > M M > A Finding: High adaptation challenges characterized by aggregate trend for low quality of governance; cross-cutting influence of governance keeps adaptation challenges high (Schweizer & O Neill, 2014) 17

18 Notable characteristics for Low mitigation challenges Mitigation challenges Both Adaptation challenges Type Pop E int C int Tech Ag prod Innov Gov -H2O GDP X pov Educ Urb Coasts Low Med High A > M M > A Medium, high trends for innovation correspond with low mitigation challenges Notable that futures where adaptation challenges dominate still have high innovation capacity (Schweizer & O Neill, 2014) 18

19 Mitigation challenges Both Adaptation challenges Type Pop E int C int Tech Ag prod Innov Gov -H2O GDP X pov Educ Urb Coasts Low Med High A > M M > A The importance (or lack thereof) of particular socioeconomic factors Not distinctive (aside from Low challenges typology): Income per capita trends Extreme poverty trends Educational attainment trends (Schweizer & O Neill, 2014) 19

20 Scenario logic embedded in CIB matrix (Schweizer & O Neill, 2014) 20

21 Scenario processes in climate change research Traditional/Linear/Forward Scenario Process (Hibbard et al. 2007) Integrated assessment modeling (WG3) Climate modeling (WG1) New/Parallel/Reverse Scenario Process Shared Socioeconomic Pathways (SSPs) Representative Concentration Pathways (RCPs) 21

22 How resilient is a scenario? SSP5 SSP2 SSP5 SSP4 (Guivach, Schweizer, and Rozenberg, 2013) 22