Developing Baseline Socioeconomic Scenarios for V&A Assessments

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Developing Baseline Socioeconomic Scenarios for V&A

AssessmentsConsultative Group of Experts on National Communications

from Parties not Included in Annex I to the UNFCCC (CGE)

Hands-on Training Workshop on Vulnerability and Adaptation for Asia and Pacific Countries

20~24 March 2006

Jakarta, Indonesia

Xianfu Lu

National Communications Support Programme (NCSP), UNDP-UNEP-GEF

Xianfu.lu@undp.org

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In the next 45 minutes or so…

• Introduction: what are baseline socio-economic scenarios and why do we need them?

• Procedures for developing socio-economic scenarios: how to develop socioeconomic scenarios?

• Guidance documents, and data sources

• Examples

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Introduction: what are baseline socio-economic

scenarios?

• Socio-economic scenarios are plausible, and often simplified, representation of future state of socio-economic parameters, i.e., they are neither predictions nor forecasts;

• Baseline socio-economic scenarios are scenarios that do not account for the effects of climate change.

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Increasing demand for policy relevant information has led to the growing consideration of multiple stressors and drivers in V&A assessments.

Introduction: why do we need baseline socio-economic

scenarios?

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V&A assessments suffer from serious weakness if by default they assume that the projected climates will take place in a world with a society and economy similar to today.

Introduction: why do we need baseline socio-economic

scenarios? (continued)

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Features about the socio-economic future that have a bearing on our response to climate change. They affect:• Availability of resources to cope with climate

change

• The administrative quality of future governments

• Ability of special interest groups to influence the public agenda

Introduction: why do we need baseline socio-economic

scenarios? (continued)

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Interactions between society/economy and climate impacts and adaptation is that socio-economic developments can make the world more or less vulnerable to the impacts of climate change.

Introduction: why do we need baseline socio-economic

scenarios? (continued)

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• Flooding events may be worse if there is a larger population living on the flood plain as a result of planning decisions.

• The effect of climate change on crop yields will depend on how many farmers have planted the crops, whether their farm income is dependent on that crop, in turn depending on agricultural subsidies, access to technology and so on.

• Some technological developments, such as improvement of weather forecasting, may better enable precautions to be taken to diminish vulnerability to extreme weather events.

Introduction: why do we need baseline socio-economic

scenarios? (continued)

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• Flooding events may be worse if there is a larger population living on the flood plain as a result of planning decisions.

• The effect of climate change on crop yields will depend on how many farmers have planted the crops, whether their farm income is dependent on that crop, in turn depending on agricultural subsidies, access to technology and so on.

• Some technological developments, such as improvement of weather forecasting, may better enable precautions to be taken to diminish vulnerability to extreme weather events.

Introduction: why do we need baseline socio-economic

scenarios? (continued)

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If you think the task of developing climate scenarios is difficult, the job of generating socio-economic scenarios is even more complex.

Due to the fast changing, and poorly understood interactions of factors operating within socio-economic systems, it is not possible to construct socio-economic scenarios on the same long-term time-scales as climate scenarios.

Procedures for developing socio-economic scenarios

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Procedures for developing socio-economic scenarios – general approach

NOT every scenario exercise has to go through all the steps. Efforts could be saved by making use of existing elements of scenarios.

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Procedures for developing socio-economic scenarios: identify drivers

• Key factors that define impacts, vulnerability and adaptive capacity;

• Often not directly measurable (e.g., social wellbeing, quality of governance, etc.)

• Wide stakeholder participation required

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Procedures for developing socio-economic scenarios: develop narrative storylines

• Qualitative and holistic portraits of the general structure and values of society;

• Conditions resulting from economic and social policies, human reproduction, occupations, and use of energy and technology;

• Major national and regional development policy and plans to be consulted;

• Stakeholders to be widely engaged

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Procedures for developing socio-economic scenarios: identify indicators

• Most relevant measurements of the key factors that define impacts, vulnerability and adaptive capacity;

• Indicators to be quantifiable;

• Wide stakeholder participation required

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Procedures for developing socio-economic scenarios: develop narrative storylines

• Qualitative and holistic portraits of the general structure and values of society;

• Conditions resulting from economic and social policies, human reproduction, occupations, and use of energy and technology;

• Major national and regional development policy and plans to be consulted;

• Stakeholders to be widely engaged

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Procedures for developing socio-economic scenarios: project indicators/parametres

•Model simulation (e.g., SRES);

•Analogues;

•Expert judgement

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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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Procedures for developing socio-economic scenarios: criteria

• Relevance - applicable to public and private sector decision-making

• Consistency- based on coherent assumptions;

• Credibility - not over-estimating the rate of change;

• Transparency – explicit account of assumptions

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Example: the IPCC SRES Scenarios

• IPCC Special Report on Emission Scenarios (SRES) makes projections about emissions of GHGs and aerosols up to 2100 for the purpose of modelling climate change;

• The world divided into four macro-regions (ALM, ASIA, REF, OECD90);

• Six modelling teams (6 modelling teams: AIM, ASF, IMAGE, MARIA, MESSAGE, MiniCAM)

• Open process;• “Storyline-and-Simulation” approach

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• Scoping and planning;• Identify drivers;• Formulate narrative storylines;• Quantify storylines using models;• “Open” review process

Example: the IPCC SRES Scenarios – the process

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Example: the IPCC SRES Scenarios – guiding principles

• No ‘business as usual’ scenario

• No probabilities ascribed

• No climate policy assumed

• No adaptation assumed

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Example: the IPCC SRES Scenarios – principal drivers

• Population growth• GDP growth• Energy and technological

change• Land-use change

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Example: the IPCC SRES Scenarios – the storylines

Four quadrants along two axes• Governance

(autonomy vs interdependence);

• Aspiration/value (consumerism vs. community)

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Example: the IPCC SRES Scenarios – the storylines

Four distinctive future worlds depicted by the four storylines…

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Example: the IPCC SRES Scenarios – the storylines

A1: rapid economic growth, low population growth, rapid adoption of new technologies, convergence of regions, capacity building, increased social interaction, reduced region differences in per capita income;

A2: heterogenous world, self-reliance and local identities preserved, high population growth, regionally-specific economic growth, fragmented economic and technological development

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Example: the IPCC SRES Scenarios – the storylines

B1: convergent world with low population

growth, transition to service and info economy, resource productivity improvements, clean technology towards global solutions

B2: Divergent world with emphasis on local solutions to economic, social and environmental sustainability, moderate population growth, intermediate levels of economic growth, less rapid technological change

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Example: the IPCC SRES Scenarios – the scenarios

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Example: the IPCC SRES Scenarios – the scenarios

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Example: Developing socio-economic scenarios for V&A assessment

in water resource sector (Indonesia)

• Key drivers: level of water demand in the 21st Century

• Storylines: SRES A2 and B2

• Indicators:

o population change rate;

o GDP growth rate

o level of development of domestic water resources

o Water withdrawal per capita

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Example: Developing socio-economic scenarios for V&A assessment

in water resource sector (Indonesia)

Step 1: Derive estimates of population and GDP changes from SRES (or other sources) in relation to base year (1990)

  2010 2020 2030 2040 2050

Population change 36 54 72 90 106

Population change 32 47 59 69 77

GDP change 121 292 521 828 1207

GDP change 121 335 635 1150 1750

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Example: Developing socio-economic scenarios for V&A assessment

in water resource sector (Indonesia)

Step 2: Estimate level of development of domestic water resources

  2010 2020 2030 2040 2050

Level of development of domestic water resources 2.90 5.57 6.07 7.13 17.4

Level of development of domestic water resources 2.90 5.39 5.77 6.54 23.20

)()( 1990,2050,199020501990 rrt GDPGDPLLLL

sources

WithdrawalL

Re 9.21990 L

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Example: Developing socio-economic scenarios for V&A assessment

in water resource sector (Indonesia)

Step 3: Estimate annual water withdrawal

  2010 2020 2030 2040 2050

Annual water withdrawal 158.09 172.25 202.28 299.46 493.81

Annual water withdrawal 153.03 163.73 185.64 274.34 658.42

resourcesLWithdrawal *

)2838(Re 31990 kmsources

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Example: Developing socio-economic scenarios for V&A assessment

in water resource sector (Indonesia)

Step 4: Estimate per capita water withdrawal

  2010 2020 2030 2040 2050

water withdrawal per capital 563.45 542.17 570.05 763.98 1161.97

water withdrawal per capital 561.97 539.90 565.95 786.87 1803.13

PopulationWithdrawalWithdrawal percapita 910

)103.206( 62000 Population

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Guidance documents

1. Guidelines on the use of scenario data for climate impact

and adaptation assessment (http://ipcc-ddc.cru.uea.ac.uk/guidelines/ggm_no1_v1_12-1999.pdf)

2. Developing Socio-Economic Scenarios for Use in Vulnerability and Adaptation Assessments (http://www.undp.org/cc/pdf/publications%20and%20flyers/SES_draft.pdf)

3. Socio-economic scenarios for climate change impact assessment: a guide to their use in the UK (http://www.ukcip.org.uk/resources/publications/pub_dets.asp?ID=34)

4. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (http://

www.grida.no/climate/ipcc/emission/index.htm)

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Data Sources: The World Bank

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Data Sources: The World Bank

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Data Sources: UNDP (Human Development Report)

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Data Sources: IPCC Data Distribution Centre

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A Few Concluding Thoughts

• Remember that creating baseline scenarios is

NOT an end in itself; So,

• Do NOT get consumed by them;

• Perhaps a simple comparison exercise between constant socioeconomic conditions and a changing socioeconomic future can provide much insight;

• Try to focus on the socioeconomic variables to which the vulnerability of interest is most sensitive.