Vulnerability and Adaptation

Transcription

1 Vulnerability and Adaptation Elizabeth L. Malone, Richard Moss, Antoinette Brenkert Joint Global Change Research Institute University of Maryland/Battelle-PNNL 28 May 2002 Pacific Northwest National Laboratory

2 Important concepts Vulnerability: Susceptibility to be harmed; composite of sensitivity and coping/adaptive capacity Resilience: capacity to cope with, recover from, or adapt to hazards (seasons-to-centuries) Sensitivity: the degree to which changes and/or variability in climate lead to changes in system attributes (the why ) Coping/Adaptive capacity: resources available for implementing adaptation options (the how ) Adaptation: adjustments in anticipation of or in response to climate change and/or variability (the what ) 2

3 Quantifying Vulnerability and Resilience Sensitivity sectors Settlement Food Health Ecosystems Coping and Adaptive Capacity Economics Human Resources Environment Water Sensitivity Indicators Coping-Adaptive Capacity Indicators National Baseline Estimates and Projections of Sectoral Indicators, Sensitivity and Coping-Adaptive Capacity, and Vulnerability-Resilience Response Indicators to Climate Change 3

4 Vulnerability assessment: framework for answering the so what question Integrates both biophysical and socio-economic dimensions and both near and long terms Comparison to impact assessment:. Climate impact assessment addresses the magnitude and distribution of the consequences of climate variability and change. Vulnerability assessment extends the impact assessment by highlighting who (as in what geographic or socio-economic groups) is susceptible, how susceptible they are, and why. Ribot, et al., 1996 Quantification would facilitate integration of diverse types of information to assess significance of climate change 4

5 Why is adaptation important? Climate change cannot be totally avoided; current (mal)adaptations to current variability may be exacerbated by climate change Adaptation is crucial to understand the response of natural and managed systems; relevant to identifying dangerous change Developing countries economies and livelihoods are sensitive to climate variability; potential to reduce losses by increasing resilience Anticipatory adaptation is more effective and less costly than forced, last-minute emergency response Interest in adaptation is growing: National Assessment, IPCC special reports and TAR highlight its importance 5

6 Need for indicators of vulnerability and adaptive capacity Aid in assessment of effects associated with different GHG stabilization levels Compare extent of vulnerability in different geographical locations Project vulnerability over time under different scenarios what is a dangerous level of change? Supplement other impacts metrics (e.g., $$) Aid in establishing adaptation priorities Aid in identifying research priorities: where are case studies, more complete data sets & more comprehensive analytical tools needed? 6

7 Constructing a Prototype Indicator Current vulnerability (resilience) estimated 39 countries Proxy selection: availability of data/projections National data from several sources (FAO, WRI, ) Normalization/scaling: World 1990=100 Aggregation: proxies combined into sectors; sectors combined into sensitivity/coping capacity indicators (geometric means); vulnerability: difference of sensitivity and coping capacity Future vulnerability estimated under different socio-economic and climate scenarios based on IPCC s SRES scenarios as implemented by PNNL s MiniCAM Monte Carlo analysis to evaluate results and implications of model structure and proxy uncertainty 7

8 Proxy Selection for the Prototype Settlement sensitivity Population at flood risk from SLR % Population with access to safe water % Population with access to sanitation Food sensitivity Cereal production/area crop land Animal protein consumption per capita Human health sensitivity Ecosystem sensitivity Completed fertility Life expectancy % Managed land Fertilizer use/area cropland Water sensitivity Water supply from internal resources and inflow from rivers Withdrawals to meet current and projected needs Economic capacity GDP (market) per capita Income distribution equity (Gini coefficient) Human resource capacity Environmental capacity % Population in the workforce (age dependency) Illiteracy % non-managed land SO 2 emissions Population density 8

9 Yemen Baseline Vulnerability-Resilience Indicator Value (World value = 0 for 1990) Mexico Saudi-Arabia Uzbekistan Sudan Ukraine Nigeria Thailand Libya South-Africa Senegal Bangladesh Egypt China Tunesia India Australia Canada New-Zealand Germany Brazil Venezuela USA Argentina Spain Netherlands Japan Bulgaria UK Jordan Korea-D-R-Rp Hungary Chile Indonesia Poland Korea-Rep Iran Cambodia World

10 World Sensitivity/Coping-Adaptation Capacity by Sector (1990) USA Germany Canada Australia settlement sensitivity food sensitivity human health ecosystem sensitivity water sensitivity economic capacity human resources environmental capacity 10

11 Sensitivity/Coping-Adaptive Capacity by Sector (1990) settlement sensitivity food sensitivity human health ecosystem sensitivity water sensitivity Senegal India China Brazil economic capacity human resources environmental capacity 11

12 Projecting future vulnerability Scenarios of the future based on Special Report on Emissions Scenarios and modeled by MiniCAM (energy and economics internally balanced) Pacific Northwest National Laboratory

13 USA's Vulnerability-Resilience Indicator values and their uncertainties over time from the Prototype (VRIP) USA s scenario calculations pathways Rapid growth VRIP values Local sustainability Delayed development 13

14 VRIP values Senegal's Vulnerability-Resilience Indicator values and Senegal s their uncertainties scenario over time from pathways the Prototype (VRIP) calculations Rapid growth Local sustainability Delayed development

15 Some conclusions from the experiment The model-based approach of the Vulnerability-Resilience Indicator is a potentially powerful tool in the assessment of vulnerability and policy development Different starting conditions lead to different results Proxies interact in diverse ways because of their contributions within sectors and at the Sensitivity/Adaptive Capacity level Different scenarios lead to different types of vulnerability 15

16 But some caveats apply Wealth is not the only determinant of vulnerability many countries indices show no significant correlation Lack of knowledge about inequality within societies hampers our ability to assess who is vulnerable to what The projections based on the SRES scenarios are very optimistic compared to case studies or other extrapolations Governance is not explicitly considered (i.e., good governance is assumed) 16

17 Role for research and assessment Support decisions using available knowledge and scientific infrastructure Explore what if questions using scenarios that illuminate relationships, tradeoffs, and possible outcomes, given uncertainties Identify vulnerabilities and opportunities and so provide more focus to decisions about infrastructure, investments, and future policy choices Engage the public and use assessment as a tool for communicating knowledge and identifying needs 17

18 USA s rapid growth scenario USA: percentages contribution by the proxies to the uncertainties of the VRIP indicator values in the rapid growth scenario stacked correlations between the VRIP values and the proxies population density sulfur emissio ns/to tal land no n-manag ed land (% o f to tal) illiteracy age dependency Gini coefficient GDP/cap water availab ility fertilizer use/ag land managed land (% of total) life expectancy birth rate animal protein demand cereal production/ag land sanitation safe water population at risk due to sealevel rise 18

19 USA s delayed development USA: percentages contribution by the proxies to the uncertainties of the VRIP indicator values in the delayed development scenario stacked correlations between the VRIP values and the proxies scenario population density sulfur emissio ns/to tal land no n-manag ed land (% o f to tal) illiteracy age dependency Gini coefficient GDP/cap water availab ility fertilizer use/ag land managed land (% of total) life expectancy birth rate animal protein demand cereal production/ag land sanitation safe water population at risk due to sealevel rise 19

20 Senegal s rapid growth scenario Senegal: percentages contribution by the proxies to the uncertainties of the VRIP indicator values in the rapid growth scenario stacked correlations between the VRIP values and the proxies population density sulfur emissio ns/to tal land no n-manag ed land (% o f to tal) illiteracy age dependency Gini coefficient GDP/cap water availab ility fertilizer use/ag land managed land (% of total) life expectancy birth rate animal protein demand cereal production/ag land sanitation safe water population at risk due to sealevel rise 20

21 Senegal: percentages contribution by the proxies to the Senegal s uncertainties of delayed the VRIP indicator development values population density in the delayed development scenario scenario stacked correlations between the VRIP values and the proxies sulfur emissio ns/to tal land no n-manag ed land (% o f to tal) illiteracy age dependency Gini coefficient GDP/cap water availab ility fertilizer use/ag land managed land (% of total) life expectancy birth rate animal protein demand cereal production/ag land sanitation safe water population at risk due to sealevel rise 21