Climate Change, Disaster Management and Poverty: Towards an Integrated Framework

Transcription

1 Climate Change, Disaster Management and Poverty: Towards an Integrated Framework Louise Cord, World Bank, Gregory van der Vink, TerramtetricsLLC, Princeton University and Christel Hennet, TerramtetricsLLC Climate Change, Disaster Risks and Poverty Reduction: Towards a common approach to reduce vulnerability Stockholm, October 24, 2008

2 Uncertainty in predicting climate change and its effects. Climate Change (mean global T 0 ) Increasing uncertainty Manifestations of Climate Change (e.g. extreme weather) Transmission Channels (e.g. migration, changes in livelihood strategies) Poverty & Economic Outcomes (e.g. GDP/Capita, % below poverty line)

3 Climate Change and Poverty: A Simulated Partial Equilibrium Analysis

4 Climate Change and Poverty An Integrated View Climate Change Increased severity and frequency of weather events Gradual changes in temperature, sea level and climate zones, and fresh water Transmission Channels Effects on livelihoods Changes in capital stock and productivity Increases in mortality and morbidity rates Changes in settlement patterns Political tensions and conflict Changes in relative prices Macro and fiscal effects Economic, Equity and Poverty Outcomes Growth and its distributional pattern will be affected Increased variability. Poverty traps (country, region and household) Increased risk and vulnerability to climate change

5 Climate change and poverty: uneven impacts LIC countries and poorer regions in MICS will be most affected: Share of GDP of climate-sensitive sectors (Madagascar vs. Brazil) Capacity to respond (GDP/capita) Factor mobility and market efficiency (China) Fragile LIC states most at risk: Weak institutions, poorly integrated markets and higher risk of conflicts (Sudan)

6 Policy Options to Cope with Increased Vulnerability Increased short-term variability Gradual changes in mean Protection Adaptation Response -Protective infrastructure (dams, flood walls, levees) -Drought resistant agriculture -Adaptive infrastructure (houses on stilts, schools and health centers in boats) -Vaccination campaigns, improved services -Building code regulations -Emergency response plans (national and community levels) (water, housing, resettlement, etc.) -Improved and rebuilding of services and infrastructure in vulnerable areas -Disaster Insurance (public/private) -Same as short-term, although multiplies risk and overtime become prohibitive - Land taxes and resettlement initiatives for vulnerable areas Subsidies and incentives to promote less climate sensitive activities. -Improved water management and pricing policies -Targeted safety net programs -Disaster and meteorological monitoring systems.

7 Climate Change and Development: Poverty Reduction Strategies

8 How To Integrate Climate Change into PRSs? Multi-sectoral diagnostic work: Quantify vulnerability to identify priority areas and assess impacts on growth, poverty and equity Quantify the benefits of reducing vulnerability through response, adaptation and protection Institutional coalitions: Build on existing institutions, with strong core leadership and a champion in MOF Identify and cost specific investments/policies for protection, adaptation and response Multi-dimensional national and community based monitoring systems linked to PRS. Be realistic

9 Conceptual Framework to Identify Priority Areas.

10 Uncertainty in predicting climate change and its effects. Climate Change (mean global T 0 ) Increasing uncertainty Manifestations of Climate Change (e.g. extreme weather) Human Response to Manifestations of Climate Change (e.g. migration, resource degradation) Conflict Environmental Degradation? Migration Instability Shortages

11 Increased uncertainty = Increased risk Manage uncertainty by managing risk

12 Risk as a component of vulnerability Risk, in its simplest form*, can be represented as: Risk = [probability of an event] x [exposed elements] event (e.g. tropical cyclone intensity, flooding, extreme heat, drought, and fire) exposed elements (e.g. population, infrastructure, economic activity) * In a more comprehensive sense, the overall risk is the integrated value of the sum of individual risks (α), represented as (Σα Riskα) at any given time (t i ), and can be expressed as: Risk (t i ) = [Σα (probability of event α (t i )) x Σ (poverty & economic outcomes (t i ))] dt

13 Vulnerability Risk term Vulnerability [V i ] = Probability of event Resiliency X + Elements at risk Capacity to respond

14 Impact of climate change on vulnerability Increases due to uncertainties of climate change Vulnerability [V i ] = Probability of event Resiliency X + Elements at risk Capacity to respond

15 Methods to Reduce Vulnerability Land use Protection Vulnerability [V i ] = Adaptation Probability of event Resiliency X + Elements at risk Capacity to respond Response

16 Adaptation vs. Protection Adaptation Creating infrastructure consistent with the changing natural environment Floating schools in Bangladesh Bill and Melinda Gates Foundation Protection Protecting infrastructure from the changing natural environment New Orleans Levee Wall (Netherlands $US1.3B, % annual probability)

17 Perils of. Protection Decrease Increase Risk = [probability of an event] x [exposed elements] Recurrence Interval Probability of event / year Population exposed to risk People at risk / year 5 year flood (0.2) year flood (0.1) year flood (0.05) 10, year flood (0.02) 50,000 1, year flood (0.01) 100,000 1,000 By creating protective measures against frequent events, you may encourage more people to move into high-risk areas and increase overall risk!

18 Test of a conceptual framework Land use Protection Vulnerability [V i ] = Adaptation Probability of event Resiliency X + Elements at risk Capacity to respond Response Sample Indices 42 indices incl.: - Access to clean drinking water - Infant mortality - % pop. under poverty level - GDP/capita 15 indices incl.: - GDP - Accountability - Corruption - Institutional effectiveness

19 Methodology for identifying the most vulnerable populations density(infmort)$y ) Create a probability-density plot for each index. density(infmort)$x 2) Calibrate each index against other indicators (e.g. World Food Programme, OFDA) 3) Create rankings by population Vulnerability index allows us to identify the populations that will be most vulnerable to poverty and to conflict!

20 Thank you! 10 0 China OECD Madagascar DR Congo Indonesia Brazil Malawi Tanzania Niger India Pakistan QUESTIONS?