GCAM Experiments in the Implementation of A New Scenario Matrix Architecture Using Shared Socio-ecosystem Pathways

Transcription

1 GCAM Experiments in the Implementation of A New Scenario Matrix Architecture Using Shared Socio-ecosystem Pathways Richard Moss, Jae Edmonds, Jiyong Eom, Nate Engle, et al. SMA/SSP Workshop July 16, 2011 Changwon-si, South Korea

2 Objective The purpose of this exercise is to begin to explore the use of the new Scenario Matrix Architecture (SMA) and the use of Shared Socio-ecosystem Pathways (SSPs). A Presentation in Two Parts 1. International and Regional Shared Socioeconomic Pathways (SSPs) for Impacts, Adaptation, and Vulnerability Analysis. 2. Numerical Experiments using the GCAM. Note that this work is still under development and all of the pieces are not yet complete. 2

3 We Will Explore Three Dimensions of Uncertainty for IAV Analysis Future forcing (SPAs) Different Shared Policy Assumptions (SPAs) matching up with RCPs Climate sensitivity Approach TBD Socio-environmental conditions (SSPs) Int l and regional scale Finer scale scenarios with bottom-up approach

4 We Plan to Include Three Types of Information with each SSP Narrative: a verbal description of the scenario. All nonquantitative aspects of the scenario are included in the narrative. IAM drivers or outputs: quantitative variables, e.g., population by region by year, GDP, labor productivity, energy mix Quantitative variables from other sources: e.g., governance index, clean water/sanitatio n, or ecosystem productivity

5 SSPs: Population and the MDGs SSP6 + Scenario Characteristics SSP6 SSP9 SSP14 SSP6 - SSP9 + SSP9 - SSP14 + SSP14 - Local Sustainability Rapid transition to sustainability and social progress drives low fertility. Levels of int l trade and cooperation are high, and there is also an emphasis on locally oriented sustainability and communitarianism. Derailed Development Initial rapid growth is derailed by a shock that results in economic, demographic, and environmental collapse. Int l and domestic institutions are ineffective. Markets and Sustainability Sustained social progress occurs in context of a market-oriented development, rapid economic growth, moderate pop. growth, and limited but effective int l institutions. High materialism and individualism. Muddling Through Low and sporadic economic growth slows social progress. A strained world marked by competition and protectionism with major implications for Africa and Asia. Engineered Environment Resurgence in traditional cultural values and life styles drives high pop. growth. Rapid economic growth, technological innovation, and engineered ecosystem goods and services. Global Apartheid Authoritarian response to potential global breakdown. Global apartheid with drastically reduced economic, social, and environmental capital for most of the global population. -B1 -Raskin Ecocommunalism - Changing Wealth of Nations - Mad Max -Raskin Breakdown -B2 -Raskin, Conventional World, Market Forces?? -A2 -Raskin Fortress World

6 Potential Proxy Variables: Global and Regional Influences on Vulnerability Demographic conditions Total population and growth rate (global and for each region)* % Population of working age* Economic capital GDP/capita* Labor productivity* Income distribution equity (Gini coefficient) Technology Crop productivity* Statistics on energy sources and use/capita* Price of benchmark energy sources: oil and electricity* Ecosystem capital % land conserved Fertilizer use/area cropland* Social capital and infrastructure Governance and institutional capacity Completed fertility* Child mortality at age 5 % Population with access to safe water Tariff status for LDCs Country Performance and Institutional Assessments (CPIA) Indicator

7 7 Metrics for vulnerability We are exploring application of the Vulnerability-Resilience Indicator Model (VRIM) for calculating an index of vulnerability that will initially be applied to global averages and IPCC regions for calibration purposes We are using widely available baseline data and results from GCAM for projections with proxy indicators Sample VRIM Baseline Scores Mexico Saudi Arabia Uzbekistan Sudan Ukraine Nigeria Thailand Libya South Africa Senegal Bangladesh Egypt China Tunisia Yemen India Canada Australia New Zealand Brazil Venezuela USA Argentina Germany Japan Spain Bulgaria Jordan Hungary Chile Indonesia Netherlands UK Korea D P Rp Poland Cambodia Korea Rep Iran World

8 Finer Scale Scenarios Can Be Nested within the SSPs The information provided by SSPs is inadequate for most IAV research and assessment, and thus approaches to developing finer scale information are needed Downscaling from IAMs is one approach Another is for IAV analysts to develop their own scenarios that are consistent with and nested within the SSPs Credibility, legitimacy, and salience for IAV research Based on detailed local knowledge of conditions and trends Place-based narratives can be created and used to communicate with a variety of audiences Monica Zurek will touch on degrees of coupling

9 We Will Explore a Place-Based Scenario Process Analysts (and stakeholders) generate scenarios for their jurisdiction Goal is to capture range of plausible future socioenvironmental conditions and explore various response options Focus on strategic issue or question for jurisdiction Consider context-specific drivers Robust decisionmaking variant Nesting place-based scenarios in SSPs acknowledges that there are uncertainties beyond immediate control

10 Example: U.S. National Park Service Scenario Planning US Nat l Park Service managers have used the process to identify social and political drivers they deemed most influential to their decision making. The scenarios represent a context within which climate change will occur and affect sensitivity and adaptive capacity Source: Leigh Welling, US NPS

11 Three SSPs and Their Family Members GCAM NUMERICAL EXPERIMENTS 11

12 The Reference Scenario for an SSP Our focus today will be on the quantitative assumptions that feed the GCAM IAM in terms of only TWO variables: Population & Economic Activity. Regional Climate Change Temperature, Precip, Storm intensity, Other Quantitative Variables that define reference no-climatepolicy scenario 1, but which are not IAM drivers. E.g. governance index or ecosystem productivity and sensitivity. Quantitative Variables that define IAM reference noclimate-policy scenario 1. E.g. reference scenario population by region by year. Storyline 1: The storyline is a verbal description of the scenario. All non-quantitative aspects of the scenario are included in the storyline. SSP1 IAM Other IAM assumptions Quantitative Variables that define reference noclimate-policy scenario 1, but which are not IAM drivers. E.g. governance index or ecosystem productivity and sensitivity. Regional GDP, Pop, Energy prices, Energy mix, Commodity prices, Land prices, Crop prices, Trade, Land cover, GHG Emissions, pollutant emissions, Etc. Present Storyline 1: The storyline is a verbal description of the scenario. All nonquantitative aspects of the scenario are included in the storyline. SSP1 Reference Scenario 12

13 The Five Shared Policy Assumptions (SPAs) and The Three Shared Climate Assumptions (SCAs) We explore three population pathways based on the NEW UN longrange projections (extension of 2008 revision) High, Medium & Low fertility rate scenarios (3) High, Mid & Low Technology development rate assumptions (3) 4 SPAs representing a range of mitigative stringency: No policy case (Ref) End-of-the-century radiative forcing reaching 6.0, 4.5 and SCAs representing a range of climate sensitivity: Climate sensitivity of 2 o C, 3 o C, and 5 o C per doubling of CO 2 This gives 12 scenarios for each Shared Socio-ecosystem Pathway (SSP) Note: We have not had time to implement the full 6 SSPs that Richard just described. 13

14 14 THREE SSPS

15 Populations for the SSPs [billion] SSP6 SSP9 SSP14 (Constructed based on UN long-range projections released in 2011) 15

16 GDP for the Three SSPs Reflect both an Income Effect (high fertility lower income) and a Demographic Composition Effect Total GDP varies by nearly 2X. 16

17 Thousands of 1990 USD (MER) Per capita income across the three SSPs SSP 6 is richer than SSP 9 or 14. Much larger variation over time than across SSPs 100 Asia SSP6 Asia SSP9 Asia SSP14 Industrial SSP6 10 Industrial SSP9 Industrial SSP14 Others SSP6 Others SSP9 1 Others SSP14 17

18 18 Climate Results

19 SSP14-LT SSP14-MT RCP 8.5 SSP9-LT SSP9-MT SSP9-HT SSP6-MT RCP 6.0 SSP6-HT RCP 4.5 RCP 2.6 [W/m 2 ] Reference Scenario Radiative Forcing (with Technology Variations) RCP year 2100 RF Levels =1.7Wm

20 Initial carbon price [2005 USD /tc] Mitigation under the Three SSPs We assumed a common global price of carbon applied to ALL emissions (fossil fuel and landuse change). RCP 2.6 is an overshoot scenario Other stabilization scenarios are notto-exceed SPA2.6 SPA3.7 SPA4.5 SPA6.0 0 SSP6 SSP9 SSP14 20

21 21 Global STEADY-STATE and TRANSIENT Mean Temperature Change from Preindustrial

22 22 Energy Results

23 Global Primary Energy Consumption [EJ]: Reference and Common Tech Scenarios Fossil fuel intensive even in SSP14. Population effect dominates income effect in SSP6 and, to a lesser degree, in SSP9. 23

24 [EJ/yr] [GJ/yr] Primary Energy in the Three SSPs and With SPA_ SSP14-Ref Global Primary Energy 200 Per Capita Primary Energy SSP9-Ref 2000 SSP6-Ref SSP14-4p SSP9-4p5 SSP6-4p SSP14-Ref SSP9-Ref SSP6-Ref SSP14-4p5 SSP9-4p5 0 0 SSP6-4p5 24

25 Price Indexed to 2005 Price Indexed to 2005 Price Indexed to 2005 Global Energy Prices (Indexed to 2005) Higher population leads to more rapid increase in energy prices, particularly for bio-energy SSP6-Ref SSP9-Ref SSP14-Ref Oil Price SSP6-Ref SSP9-Ref SSP14-Ref SSP6-4p5 SSP9-4p5 SSP14-4p5 Coal Price Bioenergy Price SSP6-Ref SSP9-Ref SSP14-Ref SSP6-4p5 SSP9-4p5 SSP14-4p

26 Land Use Results

27 27 Global Land Use Greater population leads to greater crop land use, rapidly displacing the land use for forest, pasture, and bio-energy.

28 28 Global Land Use Greater population leads to greater crop land use, rapidly displacing the land use for forest, pasture, and bio-energy. Carbon tax drastically changes land use profiles.

29 29 SSP9 Global Land Use with Technology Variation Alternative Technologies (Low Tech, Med Tech, and High Tech) have significant impact on land use.

30 Price Indexed to 2005 Changes in Wheat Price in Reference Scenarios Price is responsive to population: end-of-the-century price is about 50% higher in SSP14 than in SSP SSP6-Ref SSP9-Ref 1.5 SSP13-Ref

31 Price Indexed to 2005 Changes in Wheat Price in Mitigation Scenarios Price becomes even more responsive in mitigation scenarios SSP6-Ref SSP9-Ref SSP13-Ref SSP6-RCP4.5 SSP9-RCP4.5 SSP13-RCP

32 Price Indexed to 2005 Price Indexed to 2005 SSP9 Wheat Price with Alternative Technology Scenarios Price response to technology scenarios becomes very significant in mitigation scenarios SSP9-Ref-LT SSP9-Ref-MT SSP9-Ref-HT SSP9-Ref-LT SSP9-Ref-MT SSP9-Ref-HT SSP9-4p5-LT SSP9-4p5-MT 3.0 SSP9-4p5-HT

33 Pg C /yr Land Use Change Emissions in Reference Scenarios SSP14 results in the dramatic increase in land use change emissions throughout the century. 8 6 Land Use Change Emissions SSP6-Ref SSP9-Ref SSP14-Ref

34 Pg C/yr Land Use Change Emissions in Mitigation Scenarios The previous effect also holds for policy scenarios (SPAs) Greatest land use change emissions in SSP14 due to greatest deforestation. SSP9 vs. SSP6: competition between cropland and bioenergy land 8 6 Land Use Change Emissions SSP6-4p5 SSP9-4p5 SSP14-4p

35 Pg C /yr Pg C Land Use Change Emissions with Technology Variation Greatest land use change emissions with Low-Tech scenario The rate of technical improvement has sizeable impact on land use change emissions. 8 6 Land Use Change Emissions SSP9-Ref-LT SSP9-Ref-MT SSP9-Ref-HT SSP9-4p5-LT Land Use Change Emissions Cummulative Reference SPA4.5 4 SSP9-4p5-MT SSP9-4p5-HT SSP9-LT SSP9-MT SSP9-HT

36 36 Summary We have BEGUN to explore the new scenario matrix architecture and SSP formulation using GCAM and the new UN demographic scenarios. We created 3 SSPs: SSP6, SSP9, and SSP14 Climate results SSP produce Radiative Forcing between 6 and 9 Wm -2. Climate sensitivity is very important. Ocean thermal inertia mutes the observed climate change. Energy results Global energy demand spans more than a factor of 2. Energy prices did not vary much. Land-use change results Food prices rise with population while per capita calories decline. Land-use change emissions increase with population. Bioenergy production declines with population. Land use is dramatically affected by climate policy.

37 Next Steps Revise and continued development of the scenarios Explore the 6 SSPs outlined by Richard in his half of our presentation. Variation in mitigative capacity technology change assumptions Different fossil fuel resource base costs across SSPs. Develop related non-iam quantitative variables. Apply the SMA/SSP methodology to IAV analysis Analysis of vulnerability with quantitative indicators (VRIM) Link to place-based IAV analysis 37

38 38 DISCUSSION