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Vulnerability and Adaptation Assessments Hands-On

Training Workshop

Developing Baseline Socioeconomic

Scenarios for Climate Change Vulnerability and Adaptation

Assessment

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Overview

What are baseline socioeconomic scenarios?

Four steps for developing socioeconomic scenarios

Structured examples

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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

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What Are BaselineSocioeconomic Scenarios?

Baseline scenarios estimate changes in socioeconomic and environmental conditions absent climate change

Socioeconomic conditions determine key aspects of vulnerability and adaptive capacity to climate changes

Object is to construct plausible reference points to understand how vulnerability may change It is not to predict future socioeconomic

conditions

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Surprises Are PossibleHIV/AIDS Cases (Avert.org)

Country Adults AdultRate %

Women Children AIDS DeathsAmong Adultsand Children

Orphans due to AIDS

Angola 220,000 3.9 130,000 23,000 21,000 110,000

Benin 62,000 1.9 35,000 5,700 5.800 34,000

Botswana 330,000 37.3 190,000 25,000 33,000 120,000

Burkina Faso 270,000 4.2 150,000 31,000 29,000 260,000

Burundi 220,000 6.0 130,000 27,000 25,000 200,000

Cameroon 520,000 6.9 290,000 43,000 49,000 240,000

Central African Republic 240,000 13.5 130,000 21,000 23,000 110,000

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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

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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

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Step 2: Identify Key Sectors and Indicators and Examine Current Conditions

Indicators Good general proxy for the sector’s health

and condition 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

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Examples of Indicators

Examples Agriculture sector

Food security Import and food aid share

Water sector Water use intensity Percent of population served by water

treatment plants

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Demographic indicators Access to clean water and sanitationWithdrawals as a % of available water% uses (household, industry, agriculture) and rate of increase in uses

Economic indicators Presence or absence of water marketsContribution 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 controlDevelopment 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 quantitySalt water intrusion

Water

Example Indicators for the Water Sector

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Indicators Should be Quantifiable

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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

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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

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Example of Using National Planning Documents to

Develop Scenarios

Tunisia’s Economic Development Plan

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Economic Goals Identified in Tunisia’s Economic Development 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%

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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

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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)

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Using Analogue Countries to Estimate Change in

Indicators

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Baselines for Bangladesh

“Best Guess” Macro Projections for Bangladesh

1998 2020 2050

Population (millions) 124 168 218

GDP (billions) $28.6a $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

Population (millions) 124 165 165

GDP (billions) $28.6a $206.3 $1,485.0

GDP/capita $220 $1,250 $9,000

a. 1995 value.

Source for 1998 data: WRI, 1998.

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Vulnerability Indicators

Vulnerability Indicators for 2020

1998 Bangladesh

2020 Best Guess for Bangladesh

2020 Optimistic for Bangladesh

Analogue Country Pakistan Kazakhstan

GDP/Capita $240 $460 $1330

% of Economy in Agriculture 30% 25% 12%

Life Expectancy in Years (1995-2000) 58 64 68

% Pop. with Access to Health Care 45% 55% Not available

Literacy 38% 39% 98%

Sources: WRI, 1998; literacy rates from CIA, 1998.

Vulnerability Indicators for 2050

1998

Bangladesh 2050 Best Guess for Bangladesh

2050 Optimistic for Bangladesh

Analogue Country 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.

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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/

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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

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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

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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

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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

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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

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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

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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

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Global Population Growth Across the Scenarios

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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

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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

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SRES Country-Level Data

Country level population data are available on the CIESIN web site

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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

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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

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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

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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.

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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

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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 in-country production and assume that planted area is constant.

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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

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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

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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

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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