Transcript Baseline socioeconomic scenarios

Vulnerability and Adaptation
Assessments Hands-On
Training Workshop
Developing Baseline Socioeconomic
Scenarios for Climate Change
Vulnerability and Adaptation
Assessment
1A.1
Overview
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What are baseline socioeconomic scenarios?
Four steps for developing socioeconomic
scenarios
Structured examples
1A.2
A Note Before We Begin
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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
What Are Baseline
Socioeconomic Scenarios?
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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
1A.4
Surprises Are Possible
HIV/AIDS Cases (Avert.org)
Country
Adults
Adult
Rate %
Women
Children
AIDS Deaths
Among Adults
and 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
1A.5
General Approach
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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.6
Step 1: Analyze Vulnerability of Current
Conditions to Climate Change
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Most straightforward baseline scenario is to
use today’s conditions. Why?
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Today’s conditions are known
Easier to communicate about today’s
conditions than hypothetical future
This is a starting point
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Can compare to vulnerabilities with
hypothetical scenarios to identify variables
which most affect vulnerability
Current conditions will change
1A.7
Step 2: Identify Key Sectors and Indicators
and Examine Current Conditions
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Indicators
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Good general proxy for the sector’s health
and condition
Is closely related to vulnerability of the sector
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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.8
Examples of Indicators
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Examples
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Agriculture sector
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Food security
Import and food aid share
Water sector
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Water use intensity
Percent of population served by water
treatment plants
1A.9
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.10
Indicators Should be
Quantifiable
1A.11
Step 3: Develop ~25 Year
Baseline Scenario
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Forecasting socioeconomic conditions
beyond ~25 years has much uncertainty
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~25 years consistent with many planning
horizons
Nothing magic about 25 years; could be a
longer or shorter period
1A.12
Developing Baseline Scenarios
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Use government or other scenarios if
available
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Can they be used to estimate how indicator
variables have changed?
Can use other countries as analogue
Develop own scenarios
1A.13
Example of Using National
Planning Documents to
Develop Scenarios
Tunisia’s Economic
Development Plan
1A.14
Economic Goals Identified in Tunisia’s
Economic Development Plan
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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.15
Tunisian Agriculture Goals
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Increase production (4.3% annual growth)
and diversity
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Improve food security
Increase export income
Mobilize water resources
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Increase storage capacity
Improve efficiency and reuse of water
1A.16
Developing a Baseline
for Agriculture
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Define relevant analytic timeframe
(e.g., 2030)
Annual rates of change for
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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.17
Using Analogue Countries
to Estimate Change in
Indicators
1A.18
Baselines for Bangladesh
“Best Guess” Macro Projections for Bangladesh
1998
2020
2050
124
168
218
$72.2
$180.0
$430
$825
Population (millions)
a
GDP (billions)
$28.6
GDP/capita
$220
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.19
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.20
An Approach for Creating a
25 Year Baseline Scenario: 1
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Estimate total population and workforce
population change
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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.21
An Approach for Creating a
25 Year Baseline Scenario: 2
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Estimate change in labor productivity
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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%:
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Multiply 1.03  1.01 to get 1.04; 4% rate of
economic growth
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Multiply, do not add, the percentages.
This becomes important over
many years
1A.22
An Approach for Creating a
25 Year Baseline Scenario: 3
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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.23
Step 4 (Optional): Develop
50-100 Year Baseline Scenario
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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
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There is very uncertainty about key variables
such as population growth, productivity,
technology, tastes
1A.24
An Approach for 50-100 Year Baseline:
Use IPCC SRES Scenarios
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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
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Some cover more than one continent
1A.25
SRES Scenarios
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IPCC SRES aims for an internally consistent
framework and assumptions relating to various
factors including:
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GHG emissions
Socioeconomic conditions
Climate conditions
Each storyline describes a global paradigm
based on:
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Prevalent social characteristics and attitudes
Global relationships among economic growth,
industrialization, global and regional trade, social
attitudes, and environmental conditions
1A.26
SRES Scenarios (continued)
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Internal consistency requires that relationships
among variables such as emissions, economic
activity, and global trade be plausibly maintained:
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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:
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Population growth
Economic growth and industrialization
Environmental resource use and
conditions
1A.27
SRES Scenarios (continued)
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Four poles along two major axes
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Economic vs. environment
Global vs. regional
Combinations of these four poles
give rise to four primary storylines
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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.28
Global Population Growth Across
the Scenarios
1A.29
Developing Country-Level SRES
Storylines
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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
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Project teams will then need to make
projections about how indicators could
change in the future under the alternative
storylines
1A.30
SRES Storyline Data
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Scenario data are limited on national and
subnational scales
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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
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Expert judgment and stakeholder input are
especially relevant here
1A.31
SRES Country-Level Data
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Country level population data are available
on the CIESIN web site
1A.32
Brief Example for a
Developing Country
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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
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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.33
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.34
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.35
Steps for Scenario Development
(steps 1-3)
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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.36
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.37
Steps for Scenario Development
(steps 4-6)
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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.
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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.
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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.38
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.39
Timeline
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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
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e.g., 30 or 50 years
Tradeoff with examining longer-term climate
change
1A.40
Concluding Thoughts
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Remember that creating baseline scenarios
is not an end in itself
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The purpose is to understand how
vulnerability can change
Most desirable outcome is to identify
variables that can substantially change
vulnerability
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Examine sensitivity to change in those
variables
1A.41
Concluding Thoughts (continued)
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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.42