Vulnerability and Adaptation Assessments Hands-On Training Workshop Developing Baseline Socioeconomic Scenarios for Climate Change Vulnerability and Adaptation Assessment Vute Wangwacharakul CGE member 1A.1
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Vulnerability and Adaptation Assessments Hands-On Training Workshop Developing Baseline Socioeconomic Scenarios for Climate Change Vulnerability and Adaptation Assessment Vute Wangwacharakul CGE member 1A.1 Overview What are baseline socioeconomic scenarios? Four steps for developing socioeconomic scenarios Examples Conclusions 1A.2 A Note Before We Begin It can be very complicated to create detailed and comprehensive socioeconomic and environmental scenarios There may be greater uncertainties about future socioeconomic conditions than about climate change Try not to get bogged down in this exercise The best thing to get out of this is identification of variables that can substantially affect vulnerability to climate change 1A.3 Economic Scenarios and Integration Analysis Integration analysis explicitly or implicitly includes socio-economic scenarios Consultation (bottom-up approach) tends to cover socio-economic scenarios implicitly Cross-sectoral approach mostly use simple methods IAM requires quantitative methods to derive socio-economic scenarios, complexity varied 1A.4 Building the AIM – Step 4: Filling AED Matrix Cells Determine VIA impacts on development goals and policies. Key Vulnerabilities, Impacts and Adaptation (VIA) + = beneficial - = adverse 3 = HIGH 2 = MODERATE 1 = LOW Economic (1) Agricultural output (S0) Status (only natural variability) [-1] Agriculture is presently vulnerable (S1) Status (with climate change) [-2] Agricultural output is likely to decline Environ. (2) Indust. Activity (3) Water Resources Social (4) Health further with changing rainfall & temp. rise Development Goals/Policies 1 (A) Growth - (B) Poverty alleviation - (C) Food Security - (D) Employment -1 2 2 1A.5 Getting socio-economic scenarios for emission and vulnerability studies Approaches could be compatible or not compatible depends on final results expected Aggregate emission projection - there are approaches to by-pass sectoral level Sectoral emission projection - estimate by sector and sectoral scenarios are needed Vulnerability requires sectoral scenarios Generally, vulnerability study is from aggregate to sectoral 1A.6 What Are Baseline Socioeconomic Scenarios? Baseline scenarios estimate changes in socioeconomic and environmental conditions in absent of climate change (BAU) Socioeconomic conditions determine key aspects of vulnerability and adaptive capacity to climate changes The objective is to construct plausible reference points to understand how vulnerability may change It is not to predict future socioeconomic conditions 1A.7 Logical steps Be clear about the overall objectives: e.g. to analyse vulnerability to CC Need to arrive at secondary level of impacts: CC - physical/biological impacts - sectoral (socioecon related impacts) Develop baseline socio-econ scenarios for the corresponding sectors Assess the difference between the CC impacts vs the baseline scenarios to get vulnerabilities 1A.8 Examples: Agriculture CC on agriculture: use cc variables as inputs to crop models to derive changes in yields/production in certain period Socio-econ scenarios should be able to bring about BAU yields/production over the same period (national demographic economic development - potential demand on food (crops) potential domestic production/yield) Comparison between CC and BAU production/yield to analyse vulnerabilities 1A.9 Examples: water resources CC on water resources: use cc variables as inputs to water resource models to derive changes in water resource availability (agriculture, industry, domestic) in certain period Socio-econ scenarios should be able to bring about BAU water resources needed over the same period (national demographic economic development - potential demand on water by sector - potential production/supply) Comparison between CC and BAU production/yield to analyse vulnerabilities 1A.10 Examples: Health CC on health: use cc variables as inputs to health models to derive changes in diseases and potential effects in certain period Socio-econ scenarios should be able to bring about BAU potential effects of diseases over the same period (national demographic economic development - potential health development potential people affected by the diseases) Comparison between CC and BAU effects to analyse vulnerabilities 1A.11 General Approach Step 1: Analyze vulnerability of current socioeconomic and natural conditions to future climate change Step 2: Identify at least one key indicator for each sector being assessed Step 3: Use or develop a baseline scenario approximately 25 years into the future Step 4: Use or develop a baseline scenario 50 to 100 years into the future 1A.12 Step 1: Analyze Vulnerability of Current Conditions to Climate Change Most straightforward baseline scenario is to use today’s conditions. Why? Today’s conditions are known Easier to communicate about today’s conditions than hypothetical future This is a starting point Can compare to vulnerabilities with hypothetical scenarios to identify variables which most affect vulnerability Current conditions will change 1A.13 Step 2: Identify Key Sectors and Indicators and Examine Current Conditions Indicators Good general proxy for the sector’s health and condition and development Basic factors (demographic, economic, social government policies and plans, natural resources/environments) Is closely related to vulnerability of the sector More or less of the indicator is correlated with more or less vulnerability in the sector Enable link to change in larger socioeconomic variables such as population or income to change in sector 1A.14 Examples of Indicators Examples Agriculture sector Food demand Food security Import and food aid share Water sector Water use intensity Percent of population served by water treatment plants 1A.15 Example Indicators for the Water Sector Water Demographic indicators Access to clean water and sanitation Withdrawals as a % of available water % uses (household, industry, agriculture) and rate of increase in uses Economic indicators Presence or absence of water markets Contribution of water to products (e.g., irrigation to agricultural products) Amount/kinds of water infrastructure (reservoirs, dams, etc.) Governance and policy indicators Treaties or agreements re available water resources % of water resources not under regional control Development plans for area (population growth, agricultural development and water use implications) Cultural and social indicators Cultural meaning and recreational uses of rivers/lakes (sacred or forbidden uses) % unpolluted stream and beach kilometers (and nature of protection) Natural resource indicators Measures of water quality and quantity Salt water intrusion 1A.16 Step 3: Develop ~25 Year Baseline Scenario Forecasting socioeconomic conditions beyond ~25 years has much uncertainty ~25 years consistent with many planning horizons Nothing magic about 25 years; could be a longer or shorter period 1A.17 Developing Baseline Scenarios Use government or other scenarios if available Can they be used to estimate how indicator variables have changed? Can use other countries as analogue Develop own scenarios 1A.18 Example of Using National Planning Documents to Develop Scenarios Tunisia’s Economic Development Plan 1A.19 Economic Goals Identified in Tunisia’s Economic Development Plan (5 year plan) Increase trade liberalization Continue privatization of production in competitive sectors Increase economic growth to 6% Improve capital and human resources Annual population growth of 1.6% Annual per capita income growth of 4.3% 1A.20 Tunisian Agriculture Goals Increase production (4.3% annual growth) and diversity Improve food security Increase export income Mobilize water resources Increase storage capacity Improve efficiency and reuse of water 1A.21 Developing a Baseline for Agriculture Define relevant analytic timeframe (e.g., 2030) Annual rates of change for Crop yield Arable acreage Irrigated acreage Water use intensity (e.g., m3/ha) Socioeconomics (e.g., population and GDP) World commodity prices (e.g., from U.S. BLS) 1A.22 Using Analogue Countries to Estimate Change in Indicators Base on appropriate ground: Status and potential trends of the economy and demography Consider historical and potential development of the country Mobilize regional trend appropriately 1A.23 Baselines for Bangladesh “Best Guess” Macro Projections for Bangladesh 1998 2020 2050 124 168 218 Population (millions) a GDP (billions) $28.6 $72.2 $180.0 GDP/capita $220 $430 $825 a. 1995 value. Source for 1998 data: WRI, 1998. Optimistic Macro Projections for Bangladesh 1998 2020 2050 124 165 165 GDP (billions) $28.6a $206.3 $1,485.0 GDP/capita $220 $1,250 $9,000 Population (millions) a. 1995 value. Source for 1998 data: WRI, 1998. 1A.24 Vulnerability Indicators Vulnerability Indicators for 2020 1998 Bangladesh Analogue Country 2020 Best Guess for Bangladesh 2020 Optimistic for Bangladesh Pakistan Kazakhstan GDP/Capita $240 $460 $1330 % of Economy in Agriculture 30% 25% 12% 58 64 68 % Pop. with Access to Health Care 45% 55% Not available Literacy 38% 39% 98% Life Expectancy in Years (1995-2000) Sources: WRI, 1998; literacy rates from CIA, 1998. Vulnerability Indicators for 2050 1998 Bangladesh Analogue Country 2050 Best Guess for Bangladesh 2050 Optimistic for Bangladesh Bolivia South Korea GDP/Capita $240 $800 $9,700 % of Economy in Agriculture 30% 17% 8% Life Expectancy (1995-2000) 58 62 73 % Pop. with Access to Health Care 45% 67% 100% Literacy 38% 83% 98% Sources: WRI, 1998; literacy rates from CIA, 1998. 1A.25 An Approach for Creating a 25 Year Baseline Scenario: 1 Estimate total population and workforce population change Workforce will be needed to help estimate economic growth Use UN population projections because they give estimate by age group Project working age population, e.g., 20 to 65 http://esa.un.org/unup/ 1A.26 An Approach for Creating a 25 Year Baseline Scenario: 2 Estimate change in labor productivity Obtain data from national projections The Handbook includes regional productivity projections from Mini-Cam Multiply % change in labor productivity by % change in the workforce to estimate change in national income; e.g., if the workforce grows by 3% per year and productivity grows by 1%: Multiply 1.03 1.01 to get 1.04; 4% rate of economic growth Multiply, do not add, the percentages. This becomes important over many years 1A.27 An Approach for Creating a 25 Year Baseline Scenario: 3 Relate the change in economic growth (or other variable such as population) to the indicator variable There may or may not be a direct relationship between economic growth or population and the indicator variable Judgment may be required 1A.28 Step 4 (Optional): Develop 50-100 Year Baseline Scenario Developing a long-term baseline scenario can be desirable if the analysis of vulnerability and adaptation will go out the same length of time Socioeconomic scenarios developed for such long time periods have very high uncertainty There is very uncertainty about key variables such as population growth, productivity, technology, tastes 1A.29 An Approach for 50-100 Year Baseline: Use IPCC SRES Scenarios IPCC Special Report on Emission Scenarios (SRES) estimates global population, economic activity, and emissions of greenhouse gases out to 2100 Divides world up into very large regions Some cover more than one continent 1A.30 SRES Scenarios IPCC SRES aims for an internally consistent framework and assumptions relating to various factors including: GHG emissions Socioeconomic conditions Climate conditions Each storyline describes a global paradigm based on: Prevalent social characteristics and attitudes Global relationships among economic growth, industrialization, global and regional trade, social attitudes, and environmental conditions 1A.31 SRES Scenarios (continued) Internal consistency requires that relationships among variables such as emissions, economic activity, and global trade be plausibly maintained: For example, high population growth rates may not be consistent with high rates of per capita income increases Storylines are used to estimate patterns and changes in socioeconomic indicators such as: Population growth Economic growth and industrialization Environmental resource use and conditions 1A.32 SRES Scenarios (continued) Four poles along two major axes Economic vs. environment Global vs. regional Combinations of these four poles give rise to four primary storylines A1 – Economic growth and liberal globalization A2 – Economic growth with greater regional focus B1 – Environmentally sensitive with strong global relationships B2 – Environmentally sensitive with highly regional focus 1A.33 Global Population Growth Across the Scenarios 1A.34 Developing Country-Level SRES Storylines Storylines should in most cases be consistent with national and regional scale trends, unless there is clear indication that the exposure unit will develop in a manner that runs counter to such trends Project teams will then need to make projections about how indicators could change in the future under the alternative storylines 1A.35 SRES Storyline Data Scenario data are limited on national and subnational scales National level, downscaled data are available for population and income projections With appropriate caveats, downscaled SRES data can be used to examine changes in specified indicators Qualitative assessment is important Expert judgment and stakeholder input are especially relevant here 1A.36 SRES Country-Level Data Country level population data are available on the CIESIN web site 1A.37 Brief Example for a Developing Country Example, method, and tables are drawn from Malone et al. (2004) Numerical example is illustrative of a quantitative approach Analogous methods may be applied to other indicators Try not to be mechanical in application May need to use some imagination Qualitative and narrative approaches should also be used where appropriate and necessary 1A.38 SRES Percentage Changes in Africa and Latin America Populations from 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 A1 Scenario 24 51 81 104 124 141 148 150 147 135 123 A2 Scenario 26 58 94 133 172 212 248 281 309 329 349 B1 Scenario 24 51 81 104 124 141 148 150 147 135 123 B2 Scenario 25 55 88 120 151 180 202 219 232 236 239 1A.39 SRES Percentage Changes in GDP for Africa and Latin America from 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 A1 Scenario 47 147 289 710 1331 2142 3426 4852 6410 8068 9915 A2 Scenario 47 126 226 421 673 989 1452 1978 2578 3284 4073 B1 Scenario 47 147 289 657 1147 1773 2636 3510 4405 5242 6152 B2 Scenario 47 136 257 521 868 1310 1926 2589 3300 4052 4884 1A.40 Steps for Scenario Development (steps 1-3) Step 1: Use SRES scenarios to develop estimates of population and GDP percentage changes from base year (e.g., 1990). Step 2: Estimate percentage changes in total food consumption from base year. This is likely to follow population changes, but may be adjusted up or down to reflect anticipated improvements or decreases in overall diet and nutrition. Step 3: Estimate total cereal needs in thousands of metric tons. WRI (2000) reports, by country, the “average production of cereals” and the “net cereal imports and food aid as a percent of total cereal consumption.” Together, these two measures can be used to estimate total cereal needs. 1A.41 Downscaled to Country-Level Example: Estimated Basic Food Demand: SRES A2 Scenario (steps 1-3) Developing Country 1 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Percentage change in population from 1990 (from Table 1) 26 58 94 133 172 212 248 281 309 329 349 Estimated percentage change in GDP from 1990 (from Table 2) 47 126 226 421 673 989 1452 1978 2578 3284 4073 Estimated percentage change in total food consumption from 1990 26 58 94 133 172 212 248 281 309 329 349 Estimated total cereal needs (thousands of metric tons) 1872 2348 2883 3462 4042 4636 5171 5662 6078 6375 6672 1A.42 Steps for Scenario Development (steps 4-6) Step 4: Estimate import and food aid shares. Food imports begin at 43% for African Country 1 as reported in WRI (2000) for 1995. One way to proceed is to choose a target import share for 2100 that is consistent with the relevant SRES storyline. Step 5. Estimate in-country production. This estimate is calculated by subtracting from 1 the import share calculated in Step 4. This gives the share of total cereal needs that is met by in-country production. This number is then multiplied by estimated total cereal needs to give the estimated level of agricultural production implied by the scenario. Step 6. Estimate crop yields and percentage changes. Cereal crop yields are estimated based on required incountry production and assume that planted area is constant. 1A.43 Downscaled to Country-Level Example: Estimated Basic Food Demand: SRES A2 Scenario (steps 4-6) Developing Country 1 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Estimated import and food aid share (%)a 43 43 43 42 41 40 38 36 33 30 25 Estimated in-country production (thousands of metric tons) 1067 1338 1643 2008 2385 2782 3206 3624 4072 4463 5004 Average cereal crop yields (kg/ha)b 906 1136 1395 1705 2025 2362 2722 3076 3457 3789 4248 Estimated percentage increase in crop yields from 1995 26 58 94 137 182 229 279 328 381 427 491 1A.44 Timeline Developing century-long scenarios can result in fantastic results If the analysis does not have to go so far out into future, then only go as far as needed e.g., 30 or 50 years Tradeoff with examining longer-term climate change 1A.45 Concluding Thoughts Remember that creating baseline scenarios is not an end in itself The purpose is to understand how vulnerability can change Most desirable outcome is to identify variables that can substantially change vulnerability Examine sensitivity to change in those variables 1A.46 Concluding Thoughts (continued) Identifying key variables can be useful for policy making Don’t get consumed by baseline scenarios Even a relatively simple comparison of vulnerabilities using no change in socioeconomic conditions and a scenario going out a few decades can provide insights on which variables have a particularly large effect on vulnerability 1A.47