CGE Training Materials for Vulnerability and Adaptation Assessment

CGE TRAINING MATERIALS FOR VULNERABILITY AND ADAPTATION ASSESSMENT

Chapter 3: Baseline Socio-economic Scenarios


CONTENTS

CONTENTS.................................................................................................. I

3.1 INTRODUCTION...................................................................................1

3.1.1 Why we Need Socio-economic Scenarios.......................................2

3.2 STEPS TO DEVELOPING AND APPLYING BASELINE SCENARIOS.4

3.2.1 Step 1: Analyse the vulnerability of current socio-economic and natural conditions to future climate change......................................4

3.2.2 Step 2: Identify at least one key indicator for each sector being assessed..........................................................................................5

3.2.3 Step 3: Use or develop a scenario approximately 25 years into the future...................................................................................6

3.2.4 Step 4: Use or develop a baseline scenario 50 to 100 yearsinto the future...................................................................................8

3.3 DATA SOURCES..................................................................................9

3.4 ASSESSING THE COSTS AND BENEFITS OF ADAPTATION OPTIONS............................................................................................11

3.5 REFERENCES....................................................................................13

APPENDIX I: PROJECTED INCREASES IN REGIONAL PRODUCTIVITY BY SRES SCENARIO..............................................15

APPENDIX II: A BRIEF EXAMPLE: STEPS FOR DEVELOPING THE SOCIO-ECONOMIC SCENARIOS FOR AGRICULTURE...........18

APPENDIX III: SRES SCENARIOS – STORYLINES.....................................21

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3.1 INTRODUCTION

A lot can change in the next 100 years. Potential changes could alter our vulnerability to climate change and influence our adaptation responses. While we can't predict what will happen to our climate and our environment with absolute certainty, we can come up with different scenarios to help us visioning the range of possibilities for what the future might hold. Scenarios are used in most climate change impact and adaptation assessments. In the field of climate adaptation two different types of scenarios are used: GHG emission scenarios and socio-economic scenarios (SES).1

Socio-economic scenarios can be referred to as plausible, simplified representations of future socio-economic conditions. Socio-economic scenarios are representations of possible future states of everything that shapes society, the economy globally, regionally or locally. Socio-economic scenarios are not predictions nor forecasts and do not intend to predict conditions in the future. A scenario is also distinct from a projection, which is often a simple extrapolation of historical trends in one or more variables. The reason for incorporating socio-economic scenarios into vulnerability and adaptation (V&A) assessment and planning (and national communications reporting) is that climate change vulnerability depends on the nature of the system that is exposed to climate change within a socio-economic context.

In the Third Assessment Report to the Intergovernmental Panel on Climate Change (IPCC), Special Report on Emission Scenarios a scenario is defined as:

“… a plausible description of how the future may develop, based on a coherent and internally consistent set of (“scenario logic”) about key relationships and driving forces (e.g. rate of technology changes, prices)”. (Nakicenovic N and Swart R (eds.). 2000. p. 594)

This chapter summarizes the design, development and application of baseline socio-economic scenarios for use in V&A assessments. The development of baseline socio-economic scenarios is critical in developing meaningful V&A assessments and the development of appropriate adaptation actions. The chapter outlines the key steps required for the development of socio-economic scenarios for use within national communications, then presents an overview of potential data sources. Finally, future directions in socio-economic scenario development that are likely to be of relevance (in the next two to three years) to non-Annex I Parties are discussed.

1 <http://www.climatechange.gov.ns.ca/content/scenarios>.

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3.1.1 WHY WE NEED SOCIO-ECONOMIC SCENARIOS

Socio-economic scenarios are developed first by producing a baseline of present-day social and economic conditions, and then developing scenarios of future socio-economic changes. These future socio-economic scenarios can then be compared with climate change scenarios (e.g. temperature, precipitation, sea level rise – see chapter 4) to analyse the impact of future climates on future socio-economic conditions. In other words, the development of socio-economic scenario helps to approximate some of the key elements of an ever-changing backdrop of technology, infrastructure, social conditions and natural environments, and establish a consistent and structured base for comparing the impacts of climate change. For example, increased population growth may place more people and property at risk from increased frequency or intensity of extreme climate events. On the other hand, economic growth and development may increase the wealth and the capacity of a community to withstand and adjust to future changes, thus reducing impacts compared with current circumstances.

The development of socio-economic scenarios can also help to support sectoral assessments that include important aspects of social and economic development. For example, V&A assessments undertaken in the human health sector (chapter 8) or in agriculture (chapter 7) would benefit from a holistic assessment of future socio-economic scenarios. In doing so, this would assist in taking an integrated view of climate change impacts (outlined in chapter 9) within broader socio-economic contexts to the benefit of policy makers.

There are a number of resources written specifically to support the development of socio-economic scenarios, shown in Table 3-.

Table 3-: Key resources supporting socio-economic scenario development

Resource Year Description Link

IPCC General Guidelines on the Use of Socio-economic Scenarios

2007 This document advocates two approaches to incorporating socio-economic information into adaptation assessment.

http://www.ipcc-data.org/guidelines/TGICA_guidance_sdciaa_v2_final.pdf

UNDP Assessing Current and Changing Socio-Economic Conditions

2005 Chapter 6 of the UNDP Adaptation Policy Frameworks provides a step by step guidance through: characterizing socio-economic conditions and drivers with indicators; relating these indicators to vulnerability and climate analyses; and integrating adaptation to climate change into sustainable development objectives.

http://www.undp.org/climatechange/adapt/apf.html>.

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UNDP/GEF Developing Socio-economic Scenarios for use in Vulnerability and Adaptation Assessments

2004 This handbook provides a framework for developing integrated socio-economic scenarios at local, national, and regional and/or global levels. It aims to improve the construction of socio-economic scenarios in two ways. First, it broadens the scope of factors to be included. Second, the handbook focuses on the local sectors that are most relevant for policy, agriculture and water resources.

http://www.adaptationlearning.net/guidance-tools/developing-socioeconomic-scenarios-use-vulnerability-and-adaptation-assessments

Socio-economic Scenarios for Climate Change Impact Assessment

A guide to their use in the UK Climate Impacts Programme

2001 Detailed document that explores what future worlds might look like and considers how our vulnerability to climate change and adaptation responses might have to change to suit different situations. UK focused.

http://www.ukcip.org.uk/wordpress/wp-content/PDFs/socioeconomic_tec.pdf

US National Assessment of the Potential Consequences of Climate Variability and Change

Socio-economic Scenarios: Guidance Document

1998 A brief document that provides a useful introduction to incorporating socio-economic scenarios into climate change impact assessments.

http://www.usgcrp.gov/usgcrp/nacc/background/meetings/socio-econ.html

The resources outlined in Table 3- are drawn on, extensively in this chapter, particularly the UNDP/GEF Developing Socio-economic Scenarios for use in Vulnerability and Adaptation Assessments (Malone et al., 2004), and chapter 6 of the UNDP Adaptation Policy Frameworks on Assessing Current and Changing Socio-Economic Conditions (Lim et al., 2005).

Clearly, there is inherent uncertainty with respect to future socio-economic conditions. Whether and how much such key variables as population, income, technology, wealth distribution, laws and the environment will change can have considerable uncertainties associated with them. In addition, there can be surprises, such as the emergence of new diseases or new technologies, which can substantially affect socio-economic conditions. Thus a range of scenarios will be needed to help analysts explore sets of possible and

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plausible changes in key socio-economic variables affecting vulnerability, rather than trying to develop ‘predictions’ of future socio-economic conditions. One important benefit of using socio-economic scenarios is to identify which socio-economic variables are most likely to increase or decrease vulnerability to climate change.

The effort devoted to the development of socio-economic scenarios should be tailored to the needs of the V&A assessment being undertaken, including the needs of specific sectors (refer to chapters 5–8). From past experiences with national communications, there remains potential to devote a disproportional amount of time, energy and financial resources to this exercise. It is important to retain a focus on the ultimate use of socio-economic scenarios and initially to use relatively simple approaches in developing them. These simple approaches can be extended further should time, resources or the needs of specific V&A assessments allow.

Scenarios can be qualitative descriptions (storylines), quantitative estimations of indicators, or a combination of qualitative and quantitative characterizations of the future. Stakeholder involvement – including civil society, government ministries and representatives of important economic, environmental and cultural sectors – is key in the development of storylines, as they could also participate in the definition of indicators and projections.

3.2 STEPS TO DEVELOPING AND APPLYING BASELINE SCENARIOS

The following four steps are recommended for developing and applying baseline scenarios, although it is not necessary to conduct all four steps. Analysts are encouraged to go as far as time and resources permit. The four steps are:

1. Analyse the vulnerability of current socio-economic and natural conditions to future climate change;

2. Identify at least one key indicator for each sector being assessed;

3. Use or develop a baseline scenario approximately 25 years into the future; and

4. Use or develop a baseline scenario 50 to 100 years into the future.

3.2.1 STEP 1: ANALYSE THE VULNERABILITY OF CURRENT SOCIO-ECONOMIC AND NATURAL CONDITIONS TO FUTURE CLIMATE CHANGE

The first step is to examine what impact climate change would have on current socio-economic conditions. Current conditions are used as a baseline to consider developing climate change scenarios and are recommended for three reasons:

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1. Today’s conditions are known. Population demographics (how many and where people live), income levels, technology levels, economic status and natural conditions are known or can easily be determined.

2. It will most likely be easier to communicate risks about today’s conditions than risks regarding a hypothetical future set of socio-economic conditions. It is generally easier for people to understand (and conceptualize) how current conditions could be affected by climate change than first imagining how socio-economic conditions could change and then trying to impose climate change on top of those socio-economic conditions.

3. Using current socio-economic conditions to analyse the vulnerability to future climate change is a starting point. Analysis can then compare the effect of socio-economic changes on vulnerability. For example, if a half metre sea level rise happened with today’s socio-economic conditions, then a particular number of people would be at risk. If the coastal population grows and the same sea level rise happens, then an additional number of people would be at risk. The advantage of this approach is that it can identify variables that increase or decrease vulnerability to climate change. This can be useful in addressing adaptation, i.e. trying to reduce or minimize change in variables that increase vulnerability and encourage change in variables that decrease vulnerability.

Current socio-economic conditions are not expected to remain unchanged over time. This should be clearly communicated when presenting results from socio-economic scenarios.

3.2.2 STEP 2: IDENTIFY AT LEAST ONE KEY INDICATOR FOR EACH SECTOR BEING ASSESSED

After assessing the vulnerability of current conditions to climate change (step 1), the next step is to identify key indicators for each sector being assessed. In this context, an indicator is a socio-economic variable, factor or condition that can determine or be closely related to vulnerability to climate change. For example, population in coastal zones can be an indicator of vulnerability to sea level rise or increased coastal storms. Box 3-1 gives some examples of indicators. The reason for selecting indicators is to help estimate how the vulnerability of a sector can change. Indicators can be a link between socio-economic scenarios and vulnerability in specific sectors.

If indicators are quantifiable, their changes could be measured and, potentially, that change could be used to estimate change in vulnerability. Box 3-3 presents a case study from Kenya about quantifying the costs of climate change adaptation, using the Threshold 21 model. Of course, not all indicators are quantifiable. Adger (2003) mentions social capital as a key factor affecting society’s vulnerability to climate variability and change. Quantifying social capital may be challenging (see, for example, Yohe and Tol, 2002).

The challenge in the next two steps is to develop socio-economic scenarios that will aid in estimating how indicators could change in the future.

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Box 3-1: Example indicators

Examples for the agricultural sector include degree of food security (i.e. percentage of the population with access to sufficient quantities and qualities of food for health and nutrition), share of food imported and production of key crops. In the water sector, examples include the extent of available water supplies that are diverted or consumed, share of the population with ready access to potable water, and per capita water use (see Malone et al., 2004, for some explicit examples1).

1 <http://www.adaptationlearning.net/guidance-tools/developing-socioeconomic-scenarios-use-

vulnerability-and-adaptation-assessments>

3.2.3 STEP 3: USE OR DEVELOP A SCENARIO APPROXIMATELY 25 YEARS INTO THE FUTURE

Following the identification of key indicators per sector, step 3 focuses on developing a baseline scenario within appropriate time frames. The further into the future baseline scenarios are developed, the more hypothetical they are, as the potential for change multiplies. There is no one specific time in the future at which socio-economic scenarios become more or less credible. As a first step, a quarter century baseline scenario should be developed. Official statistics rarely exceed 15–20 years ahead and could be used to develop the baseline.

If such scenarios have been developed (e.g. a national or regional government may have made such projections), analysts should consider using them. The scenarios or projections should be evaluated to determine their usefulness. In particular, determine whether the scenarios provide estimates of variables that can help in estimating how indicators could change. Using an estimate that has already been developed can save time and resources in preparing national communications.

A key element of developing socio-economic scenarios is the development of narrative storylines. Storylines are a qualitative view of the general structure and values of society, and consider national and regional development plans. The development of effective storylines, , requires stakeholders to be closely engaged (such as through participatory scenario development (PSD) approaches2. This is a process that involves the participation of stakeholders to explore the future in a creative and policy-relevant way.) Box 3-2 presents a case study from Thailand regarding the use of storylines in planning for climate change adaptation.

Box 3-2: Developing Socio-economic Storylines in Thailand

The Government of Thailand, in

2 World Bank discussion paper No. 19. (2010).

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collaboration with Southeast Asian START Regional Center, has been exploring the use of socio-economic storylines to assist in climate change vulnerability and adaptation planning in key sectors, including agriculture, tourism and the coastal zone.

One of the storylines has been applied in a case study on annual crop production in Chi-Min River Basin.1

The case study looked at how different scenarios of agricultural development result in different risks under climate change conditions. The different development directions were based on different levels and combinations of commercial farming, food crops, subsistence farming and energy crops. The different cropping patterns led to varying future water demand; the food-bowl scenario resulted in a similar water demand to the business as usual development direction; while the bio-fuel development direction resulted in more significant water shortages in some areas. The different development directions provide varying adaptation challenges in relation to water supply for agriculture that are the focus of the next stages of the project.

1 <http://cc.start.or.th/climatechange/p10.html>

Ideally socio-economic scenarios could be developed in five- or 10-year increments to assess the relative rates of change. Two examples of such are shown in Boxes 3-2 and 3-3.

Box 3-3: Threshold 21 Model (Kenya 2030 Vision)

Through the African Adaptation Programme (AAP) the Threshold 21 model has been customized for long-term integrated development planning in Kenya. The Threshold 21-Kenya model allows the complex interactions between key development areas such as the economy, society and environment to be integrated in a single framework. The model provides the socio-economic evidence for Kenya to invest in climate change adaptation and serves to translate Vision 2030 and the National Climate Change Response Strategy into practical actions. This aims to encourage sustainable development, reduce poverty, and increase wellbeing of vulnerable groups, especially women and children, within the context of Vision 2030.

The Threshold 21 modelling team was trained over a period of two months on different modules and application of the tool concentrated on four priority sectors, including, energy, agriculture, water resources and human health. Analysis of the sectors enabled the core team to gain a better understanding of the potential impacts of climate change and climate variability on Kenya in an integrated manner. This enabled them to develop appropriate policy briefs to guide sustainable development.

The modelling team have been trained to maintain Threshold 21-Kenya and use it for policy scenario analysis. In addition, 25 government officials have been trained in the

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more general use of systems dynamics and Threshold 21 to promote its use in their ministries and departments. To create ownership of the dynamic planning, it is being institutionalized within the Macro Planning Directorate, Ministry of State for Planning, National Development and Vision 2030.

For further information see UNDP Kenya website1 as well as the Millennium Institute website.2

1 <http://www.ke.undp.org/index.php/projects/africa-adaptation-programme>

2 <http://www.millennium-institute.org/integrated_planning/tools/T21/ >

3.2.4 STEP 4: USE OR DEVELOP A BASELINE SCENARIO 50 TO 100 YEARS INTO THE FUTURE

The fourth step is to develop baseline scenarios beyond the middle of the 21st century and even up to the end of the century. The advantage of doing so is that baseline socio-economic scenarios can be on the same time scale as climate change scenarios (which often project out to 2100; see chapter 4), thus ensuring consistency at a conceptual level. The practical disadvantage is that socio-economic scenarios covering such long periods of time are out of the usual development planning timeframe. Indeed, this is good for scenario development because scenarios are not predictions or forecasts but could help policy makers and development planners to define a vision of possible futures. Also, long-term socio-economic scenarios are important in climate scenarios development.

The IPCC Special Report on Emission Scenarios (SRES) was developed to estimate how different development paths could affect emissions of greenhouse gases over the 21st century (see chapter 4). Developing such scenarios requires estimating how socio-economic conditions would change. The SRES scenarios estimate how population, income, productivity and other factors could change over the 21st century.3 They are now an integrated framework for developing the internally consistent socio-economic scenarios required for V&A assessments and other policy analysis.

As these scenarios are published by the IPCC, they can be a good source of information that can help in developing socio-economic scenarios up to a century-long. However, there are two important caveats:

1. The SRES scenarios are at a regional scale. Estimates are not provided for most countries. To develop a socio-economic estimate for a specific country (or region within the country), the in-country analyst will need to either assume that the same regional changes will happen at the national or sub-national scale.

3 <http://sedac.ciesin.columbia.edu/ddc/sres/>.

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http://sedac.ciesin.columbia.edu/ddc/sres/

http://www.millennium-institute.org/integrated_planning/tools/T21/

http://www.ke.undp.org/index.php/projects/africa-adaptation-programme


Alternatively, judgement can be applied as to how change at the national level could differ from the regional level.

2. The SRES scenarios may not represent all possibilities. All the SRES scenarios assume economic growth in all regions, and some assume relatively high levels of growth. For various reasons, some countries or regions may not have continuous economic growth and it may be desirable to include a relatively pessimistic scenario.

3.3 DATA SOURCES

Data useful to consider when developing indicators are available from a variety of sources, depending on the particular sector under consideration (see Table 3-2 ). Many multinational organizations, such as the World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), the United Nations Development Programme (UNDP) and the World Bank have readily accessible data on many variables that might be appropriate for indicators. General data that may be particularly relevant for one or more indicators include the following:

Economy: gross domestic product (GDP), important sectors, comparative advantages, technology, infrastructure, institutions;

Demography: population, age structure, education, health; and

Environment: land, water, air, biota, principal and unique resources, quantity and quality.

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Table 3-2 : Selected data sources for developing baseline and socio-economic scenarios, socio-economic data and indicators

Source and availability Description

Baseline and socio-economic scenarios:

Malone, E.L. and E.L. La Rovere. 2004. Assessing current and changing socio-economic conditions. In Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies and Measures, B. Lim, E. Spanger-Siegfried, I. Burton, E.L. Malone, and S. Huq (eds.). Cambridge University Press, Cambridge, UK, pp. 147–163. <http://www.undp.org/climatechange/adapt/apf.html

Good primary reference on methods and approaches

Excellent general guidance on the process

Good description of indicators and characteristics

Malone, E.L., J.B. Smith, A.L. Brenkert, B.H. Hurd, R.H. Moss, and D. Bouille. 2004. Developing Socioeconomic Scenarios: For Use in Vulnerability and Adaptation Assessments. United Nations Development Programme, New York.< http://www.uncclearn.org/sites/www.uncclearn.org/files/inventory/UNDP19.pdf l>

Good primary resource that describes the concepts, nature of the process and some clear examples for several indicators

Socio-economic data

Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre Socio-Economic Data and Scenarios

<http://sedac.ciesin.org/ddc/baseline/index.html>

Baseline socio-economic data for the world regions, compiled from a variety of sources, up to 1998

Nakicenovic, N. and R. Swart. 2000. Special Report on Emissions Scenarios. Cambridge University Press, Cambridge, UK.<http://www.grida.no/climate/ipcc/emission/023.htm>

Primary source for concepts and discussions relating to the SRES scenarios

Center for International Earth Science Information Networks (CIESIN) Socio-economic data and Applications Center

<http://www.ciesin.columbia.edu>

CIESIN specializes in online data and information management, spatial data integration and training, and interdisciplinary research relating to human interactions in the environment

Indicator sources

World Resources Institute (WRI). 2008. World Resources 2008: Roots of Resilience – Growing the Wealth of the Poor. Washington, DC, US: WRI in collaboration with the United Nations Development Programme, the United Nations

Source for country-level data on a range of possible indicators

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http://www.ciesin.columbia.edu/

http://www.grida.no/climate/ipcc/emission/023.htm

http://sedac.ciesin.org/ddc/baseline/index.html

http://www.uncclearn.org/developing_socio_economic_scenarios_use_vulnerabil



http://www.undp.org/climatechange/adapt/apf.html


Environment Programme and the World Bank

<http://www.wri.org/publication/world-resources-2008-roots-of-resilience>

3.4 ASSESSING THE COSTS AND BENEFITS OF ADAPTATION OPTIONS

Assessing the costs and benefits of adaptation options can be an important component of adaptation projects. Adaptation planners can make use of a range of approaches that have proven to be effective decision-support tools in broader development and sectoral planning contexts. In 2011 under the Nairobi Work Programme (NWP) the UNFCCC published Assessing the Costs and Benefits of Adaptation Options: An Overview of Approaches.4 This useful publication provides detailed guidance on the three key approaches for assessing the costs and benefits of adaptation options. These include cost-benefit analysis, cost-effectiveness analysis and multi-criteria analysis as well as a range of other less common approaches as summarized in Table 3-3 . The publication also draws upon cases of best practice in the application of these approaches, while providing an overview of the key lessons learned, to guide an adaptation planner towards the selection of the most appropriate approach for their particular context.

Table 3-3 : Assessment approaches and their main strengths and weaknesses. (Source: UNFCCC, 2011)

Approach Description/outputs

Case studies

Strengths Weaknesses

Cost-benefit analysis(CBA)

CBA assesses benefits and costs of adaptation options in monetary terms. Outputs include net present values, internal rates of return, payback periods or benefit–cost ratios

Bolivia, The Gambia, Nepal and UK

CBA can provide quantitative justification for adaptation options rather than just relative information. It allows for a comparison between different aspects using a common metric (e.g. United States Dollar (USD))

CBA focuses on efficiency, where other criteria may be important (e.g. uncertainty or equity). It has difficulties with non-monetized costs and benefits and may need a subjective input into the choice of discount rate

Cost-effectiveness analysis(CEA)

CEA identifies the least-cost option of reaching an identified

Brazil and Pacific Islands

CEA can assess options, using units other than monetary units, thus it is good for effects that are

CEA is unable to offer an absolute analysis or common metrics. It deals insufficiently with uncertainty or equity. The

4 <http://unfccc.int/adaptation/nairobi_work_programme/knowledge_resources_and_publications/items/4628.php>

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http://www.wri.org/publication/world-resources-2008-roots-of-resilience

http://www.wri.org/publication/world-resources-2008-roots-of-resilience


target/risk reduction level or the most effective option within available resources

difficult to value. It can be applied within the context of routine risks (e.g. health effects) as well as major climate risks

selection of thresholds or target risk levels is not always easy or objective

Multi-criteria analysis(MCA)

MCA assesses adaptation options against a number of criteria, which can be weighted, to arrive at an overall score

Bhutan, the Nether-lands and Yemen

MCA can consider monetized and non-monetized costs and benefits together. It also allows for considering a wide range of criteria including equity

Scoring and ranking of options in MCA is subjective and not easily comparable

Risk assessment

Risk assessment analyses current and future risks and identified options to address the greatest threats

Canada Risk assessments can address issues surrounding uncertainty and allow for mainstreaming of adaptation

Risk assessments require sufficient data and valid assumptions about the likelihood of various events occurring

In addition, a useful source of information on the application of economic assessment tools in different adaptation contexts is provided by the World Bank study Economics of Adaptation to Climate Change.5

5 <http://climatechange.worldbank.org/content/economics-adaptation-climate-change-study-homepage>.

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3.5 REFERENCES

Adger WN. 2003. Social aspects of adaptive capacity. In: JB Smith, RJT Klein and S Huq (eds.).Climate Change Adaptive Capacity and Development. London: Imperial College Press, pp. 29–49.

Lim B, Spanger-Siegfried E, Burton I, Malone E and Huq S (eds.). 2004. Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies, and Measures. New York: Cambridge University Press.

Malone EL, Smith JB, Brenkert AL, Hurd B, Moss RH and Bouille D. 2004. Developing Socioeconomic Scenarios for use in Vulnerability and Adaptation Assessments. New York: UNDP (United Nations Development Programme).

Malone, E.L. and E.L. La Rovere EL. 2004. Assessing current and changing socio-economic conditions. In: B Lim, E Spanger-Siegfried, I Burton, EL Malone and S Huq (eds.). Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies and Measures New York: Cambridge University Press., pp. 147–-163

Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP and Wilbanks TJ. 2010. The next generation of scenarios for climate change research and assessment. Nature, 463: 747–756.

Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, and Dadi Z. 2000. Special Report on Emissions Scenarios, N Nakicenovic and R Swart (eds.). IPCC (Intergovernmental Panel on Climate Change). Cambridge, UK: Cambridge University Press. Available at <http://www.grida.no/climate/ipcc/emission/099.htm>.

UNFCCC .2011. Assessing the costs and benefits of adaptation options. Available at: <http://unfccc.int/files/adaptation/nairobi_work_programme/knowledge_resources_and_publications/application/pdf/2011_nwp_costs_benefits_adaptation.pdf>.World Bank. 2010. Participator Scenario Development Approaches for Identifying Pro-Poor Adaptation Options: Capacity Development Manual. Discussion paper No. 19. Available at <http://www.iisd.org/publications/pub.aspx?id=1410>.

WRI (World Resources Institute). 2008. World Resources 2008: Roots of Resilience – Growing the Wealth of the Poor. Washington DC: World Resources Institute, USA. in collaboration with the United Nations Development Programme, the United Nations Environment Programme and the World Bank.

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http://www.iisd.org/publications/pub.aspx?id=1410

http://www.grida.no/climate/ipcc/emission/099.htm


Yohe G and Tol RSJ. 2002. Indicators for social and economic coping capacity – moving toward a working definition of adaptive capacity. Global Environmental Change 12:25–40.

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APPENDIX I: PROJECTED INCREASES IN REGIONAL PRODUCTIVITY BY SRES SCENARIO

The data in Table I.1 were provided by Dr Hugh Pitcher, Pacific Northwest Laboratory. Estimates are from “Mini-Cam”, a model that estimates global greenhouse gas emissions. Mini-Cam is one of the models used in development of the SRES scenarios.

Table I.1 Labour productivity for the four SRES storylines for the 11 regions used in the Mini-Cam version of the SRES scenarios

A1 family of scenarios

(%) A2 (%) B1 (%) B2 (%)

United States

1990– 2005 1.51 1.51 1.52 1.52

2005– 2020 1.59 0.75 1.18 0.88

2020– 2035 1.58 0.72 1.15 0.80

2035– 2050 1.60 0.75 1.16 0.83

2050– 2065 1.59 0.75 1.15 0.83

2065– 2080 1.59 0.77 1.16 0.84

2080– 2095 1.55 0.76 1.13 0.83

Canada

1990– 2005 1.51 1.51 1.51 1.51

2005– 2020 1.77 0.86 1.35 1.01

2020– 2035 1.72 0.74 1.25 0.84

2035– 2050 1.73 0.79 1.26 0.89

2050– 2065 1.69 0.79 1.24 0.89

2065– 2080 1.67 0.81 1.23 0.89

2080– 2095 1.64 0.80 1.20 0.88

Western Europe

1990– 2005 1.64 1.64 1.65 1.64

2005– 2020 1.78 0.95 1.45 1.10

2020– 2035 1.71 0.73 1.23 0.83

2035– 2050 1.73 0.78 1.24 0.88

2050– 2065 1.69 0.78 1.22 0.88

2065– 2080 1.67 0.80 1.22 0.89

2080– 2095 1.63 0.79 1.19 0.88

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(%) A2 (%) B1 (%) B2 (%)

Japan

1990– 2005 1.79 1.78 1.79 1.79

2005– 2020 2.13 1.32 2.12 1.63

2020– 2035 1.46 0.68 1.03 0.73

2035– 2050 1.50 0.72 1.04 0.78

2050– 2065 1.50 0.72 1.04 0.78

2065– 2080 1.51 0.74 1.06 0.79

2080– 2095 1.51 0.73 1.05 0.79

Australia and New Zealand

1990– 2005 1.76 1.76 1.76 1.76

2005– 2020 1.94 0.84 1.38 0.95

2020– 2035 1.87 0.81 1.39 0.93

2035– 2050 1.84 0.85 1.36 0.96

2050– 2065 1.77 0.84 1.32 0.94

2065– 2080 1.74 0.85 1.30 0.94

2080– 2095 1.69 0.84 1.26 0.93

Former Soviet Union

1990– 2005 -0.71 -0.71 -0.71 -0.71

2005– 2020 5.19 2.59 4.92 3.94

2020– 2035 5.23 2.26 4.37 3.15

2035– 2050 4.17 2.04 3.39 2.56

2050– 2065 3.34 1.84 2.72 2.14

2065– 2080 2.82 1.71 2.31 1.88

2080– 2095 2.46 1.58 2.01 1.68

China and centrally planned Asia

1990– 2005 7.46 7.45 7.46 7.46

2005– 2020 6.84 4.54 6.61 5.59

2020– 2035 6.21 2.96 5.62 4.39

2035– 2050 5.21 2.57 4.39 3.40

2050– 2065 4.10 2.24 3.38 2.69

2065– 2080 3.33 2.03 2.76 2.27

2080– 2095 2.78 1.83 2.31 1.96

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(%) A2 (%) B1 (%) B2 (%)

Middle East

1990– 2005 0.28 0.28 0.28 0.28

2005– 2020 2.30 1.25 2.35 1.80

2020– 2035 4.38 1.70 3.60 2.37

2035– 2050 3.63 1.57 2.86 2.02

2050– 2065 2.99 1.47 2.40 1.79

2065– 2080 2.59 1.38 2.09 1.62

2080– 2095 2.32 1.32 1.88 1.49

Africa

1990– 2005 0.65 0.65 0.65 0.65

2005– 2020 3.65 2.59 3.71 3.15

2020– 2035 6.37 3.71 6.32 5.14

2035– 2050 6.41 3.28 5.71 4.57

2050– 2065 5.35 2.77 4.40 3.48

2065– 2080 4.23 2.41 3.40 2.76

2080– 2095 3.38 2.12 2.74 2.29

Latin America

1990– 2005 1.39 1.39 1.39 1.39

2005– 2020 3.81 2.04 3.76 2.93

2020– 2035 4.76 1.83 3.88 2.62

2035– 2050 3.79 1.72 3.07 2.23

2050– 2065 3.10 1.59 2.53 1.93

2065– 2080 2.67 1.50 2.20 1.74

2080– 2095 2.37 1.41 1.96 1.58

South and Southeast Asia

1990– 2005 3.81 3.81 3.81 3.81

2005– 2020 5.93 3.50 5.81 5.06

2020– 2035 6.14 2.93 5.49 4.17

2035– 2050 5.10 2.55 4.26 3.24

2050– 2065 4.01 2.23 3.29 2.59

2065– 2080 3.25 2.00 2.68 2.18

2080– 2095 2.75 1.81 2.27 1.90

Note: Percentages are based on use of market exchange rates. Results should not be used to compare wealth across countries or regions.

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APPENDIX II: A BRIEF EXAMPLE: STEPS FOR DEVELOPING THE SOCIO-ECONOMIC SCENARIOS FOR AGRICULTURE

Annex 1 in Malone et al. (2004) gives a relatively clear and concise data set and example indicators to illustrate and apply the concepts behind the baseline socio-economic scenario. The example below, excerpted from Malone et al. (2004) is numeric; in practice, however, the most useful analyses and assessments will also most likely involve qualitative information and supportive judgements.

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 can 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 tonnes. WRI (2008) 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, assuming that, if there are imports, all the country’s production is also consumed internally. For example, the estimates for Developing Country 1 are 847,000 tonnes produced, and 43% of consumption met with imports in 1995. Therefore, the share met by internal production is 57%, which, divided into total production, yields 1,486,000 tonnes of cereal needed in 1995. This number is then adjusted by population growth to reflect demand in 2000 and is estimated at 1,872,000.

Step 4: Estimate import and food aid shares. Food imports begin at 43% for Developing Country 1, as reported in WRI (2008) for 1995 (available at http://pubs.wri.org/pubs_pdf.cfm?PubID = 3027). One way to proceed is to choose a target import share for 2100 that is consistent with the relevant SRES storyline. These targets were set at 25% and 35%. These particular estimates were arrived at subjectively by the authors, and illustrate consistency with the SRES scenarios – not necessarily accuracy or consistency with Developing Country 1’s own situation. Having both endpoints (i.e., estimates for 2000 and 2100), the intervening years can be estimated by proportional scaling with the estimated changes in income (based on the assumption that changes in either agricultural production or imports is enabled by GDP growth). For example, the following equation is used to interpolate import shares:

I2010 = I2000 – (I2000 I2100) * [ (GDP2010 – GDP2000)/(GDP2100 GDP2000) ]

where I2000, I2010, and I2100 = estimated import/food aid share in 2000, 2010, and 2100, respectively, and GDP2000, GDP2010, and GDP2100 = estimated GDP percentage changes from 1990 for 2000, 2010, and 2100, respectively.

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http://pubs.wri.org/pubs_pdf.cfm?PubID=3027


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 the assumption that planted area is constant. Cereal crop planted area is estimated from data in WRI (2008) in which total cereal production in Developing Country 1 in 1996–1998 is 847,000 tonnes, and average cereal crop yields are given as 719 kg/ha. Therefore, estimated planted area in Developing Country 1 in 1996–1998 is 1.18 million ha. Using this land base and dividing into the estimated production level gives the required crop yield. The percentage change in crop yields is then estimated using 719 kg/ha in 1995 as the base. An estimate of annualized yield changes is also helpful. This example, which suggests that yields will rise by 491% by 2100, implies an annual rate of change of 1.6% – consistent with recent technological changes but highly speculative that this rate can persist indefinitely. Table II.1 presents the information and data used in this illustrative example.

In addition to the use of SRES storylines, analysts could also consider using standard scenario approaches, such as both “optimistic” and “pessimistic” scenarios. The intent of such scenarios is to identify a range of plausible outcomes. Certainly, the longer the time frame used in the analysis, the greater the uncertainty inherent in the scenario.

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Table II.1. Estimated basic food demand for Developing Country 1: SRES A2 scenario

Developing Country 1 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Percentage change in population from 1990 26 58 94 133 172 212 248 281 309 329 349

Estimated percentage change in GDP from 1990 47 126 226 421 673 989 1 452 1 978 2 578 3 284 4 073

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 tonnes) 1 872 2 348 2 883 3 462 4 042 4 636 5 171 5 662 6 078 6 375 6 672

Estimated import and food aid share (%)a 43 43 43 42 41 40 38 36 33 30 25

Estimated in-country production (thousands of tonnes) 1 067 1 338 1 643 2 008 2 385 2 782 3 206 3 624 4 072 4 463 5 004

Average cereal crop yields (kg/ha)b 906 1 136 1 395 1 705 2 025 2 362 2 722 3 076 3 457 3 789 4 248

Estimated percentage increase in crop yields from 1995 26 58 94 137 182 229 279 328 381 427 491

Note: Net cereal imports and food aid as a percentage of total cereal consumption, 1995–1997 (WRI, 2008): Developing Country 1: 43%.

a. Estimated import and food aid share is based on taking current share and using judgement to estimate the target share for 2100 under the given SRES scenario. In this case, the A2 scenario suggests greater self-reliance. Therefore, a goal might be to reduce food imports from 43% to 25% by 2100. Capacity to reduce imports is a function of income; therefore, estimated food import share is scaled by the percentage change in projected income. For example, 2% of the overall increase in income occurs between 2000 and 2010; therefore, we estimate that 2% of the total 33% change in import share (i.e. –0.6%) occurs in this decade. Caution must be used here to ensure overall consistency – falling import shares must be matched by increasing in-country agricultural production, which implies an increase in the intensity of agricultural production or in the cultivated land area.

b. Cereal crop yields are estimated based on required in-country production and assume that planted area is constant. Cereal crop planted area is estimated from data in WRI (2008) in which total cereal production in 1996–1998 is 847,000 tonnes, and average cereal crop yields are given as 719 kg/ha. Therefore, estimated planted area in Developing Country 1 in 1996–1998 is 1.18 million ha. Production levels, however, are also subject to increases by increasing the land base.

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APPENDIX III: SRES SCENARIOS – STORYLINES

To provide more consistent projections of greenhouse gas (GHG) emissions – projections that considered the complex social, economic, and technological relationships that underlie energy use and resulting emissions – the IPCC developed the Special Report on Emission Scenarios (SRES). The SRES approach aims to present a short “history” of possible future development expressed in a combination of key scenario characteristics based upon an underlying consistency of the complex economic relationships that underlay energy use. The result was a set of logical storylines that encompass the social and physical relationships driving greenhouse gas (GHG) emissions (Nakicenovic and Swart (eds.), 2000).

At the core of the SRES approach are four poles along two major axes:

Economic versus environmental; Global versus regional.

As shown in Figure III.1, 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.

Each storyline describes a global paradigm based on prevalent social characteristics, values and attitudes that determine, for example, the extent of globalization, economic development patterns and environmental resource quality. The storylines are by their nature highly speculative. Nonetheless, they do provide identifiable starting points that are defined and consistent with available data sets for projecting some variables (most notably population, income, land use, and emissions). They have been used in previous and ongoing assessments and provide a basis for inter-country comparisons. Finally, they illustrate the degree of creative imagination that this scenario building embraces. It is certainly appropriate to consider these storylines as appropriate or desired, based on national and regional outlooks and goals and plausible futures. Figure III.1. Conceptual relationships

underlying the SRES scenarios.

Source: Nakicenovic and Swart, 2000.

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The A1 and B1 scenarios focus on global solutions to economic, social and environmental sustainability, with A1 focusing on economic growth and B1 focusing on environmental sensitivity. A2 and B2 focus on regional solutions with strong emphasis on self-reliance. They differ in that A2 focuses on strong economic growth and B2 focuses on environmental sensitivity. The IPCC describes their differences as follows: “While the A1 and B1 storylines, to different degrees, emphasize successful economic global convergence and social and cultural interactions, A2 and B2 focus on a blossoming of diverse regional development pathways.”

The A1 scenario assumes strong economic growth and liberal globalization characterized by low population growth, very high GDP growth, high-to-very-high energy use, low-to-medium changes in land use, medium-to-high resource availability (of conventional and unconventional oil and gas), and rapid technological advancement. The A1 scenario assumes convergence among regions, including a substantial reduction in regional differences in per capita income in which the current distinctions between “poor” and “rich” countries eventually dissolve; increased capacity building; and increased social and cultural interactions. A1 emphasizes market-based solutions; high savings and investment, especially in education and technology; and international mobility of people, ideas, and technology.

The A2 scenario describes a world with regional economic growth characterized by high population growth, medium GDP growth, high energy use, medium-to-high changes in land use, low resource availability of conventional and unconventional oil and gas, and slow technological advancement. This scenario assumes a very heterogeneous world that focuses on self-reliance and the preservation of local identities, and assumes that per capita economic growth and technological change are more fragmented and slower than in other scenarios.

The B1 scenario describes a convergent world that emphasizes global solutions to economic, social and environmental sustainability. Focusing on environmental sensitivity and strong global relationships, B1 is characterized by low population growth, high GDP growth, low energy use, high changes in land use, low resource availability of conventional and unconventional oil and gas, and medium technological advancement. The B1 scenario assumes rapid adjustments in the economy in the service and information sectors, decreases in material intensity, and the introduction of clean and resource-efficient technologies. A major theme in the B1 scenario is a high level of environmental and social consciousness combined with a global approach to sustainable development.

The B2 scenario, like the A2 scenario, focuses on regional solutions to economic, social and environmental sustainability. The scenario focuses on environmental protection and social equality and is characterized by medium population and GDP growth, medium energy use, medium changes in land use, medium resource availability, and medium technological advancement.

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The four standard SRES scenarios

A1 – ECONOMIC GROWTH AND LIBERAL GLOBALIZATION

Utilitarian values, affluence oriented Rapid economic growth (3% globally) Low population growth, long life, small families Rapid introduction and adoption of efficient technologies Intermediate GHG emissions Personal wealth emphasized over environmental quality Reduced differences in regional incomes Cultural differences throughout the world converge

A2 – ECONOMIC GROWTH WITH GREATER REGIONAL FOCUS

Local, community and family centred values Greater regional emphasis both culturally and economically Less rapid economic growth (1.5% globally) High population growth Low per capita incomes Technology change and adoption depends on resources and culture Highest GHG emissions Focus on agricultural productivity to feed rapidly rising populations

B1 – ENVIRONMENTALLY SENSITIVE WITH STRONG GLOBAL RELATIONSHIPS

High level of environmental and social concern and value Emphasis on globally sustainable and balanced development with investments

in social infrastructure and environmental protection Moderate economic growth (2% globally) Low population growth Moderate per capita income, slightly less than A1 Services emphasized over material goods, quality over quantity Mitigation technologies rapidly adopted and rapid decline in use of fossil fuels Low GHG emissions

B2 – ENVIRONMENTALLY SENSITIVE WITH HIGHLY REGIONAL FOCUS

High level of environmental and social concern and value Emphasis on decentralized decision-making and local self-reliance Moderate economic growth (1% globally) Moderate population growth Moderate per capita income, slightly less than A1 Less technology development and adoption, declining global investment, and

less international diffusion Regional differences in energy use and innovation, transition out of fossil fuels

is gradual Moderate GHG emissions

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