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National Assessment Synthesis Team
October 15, 1998
Introduction
This paper discusses the handling of socio-economic and ecological (SEE)
assumptions in assessing the impacts of climate change and variability
in the National Assessment. It is revised from the paper circulated before
the Monterey meeting, and incorporates discussion and feedback from Monterey,
Woods Hole, and other conversations.
The paper outlines a Framework to approach the problem of describing
socio-economic and ecological (SEE) characteristics of the future world
for which climate impacts are assessed. The framework is intended to provide
some level of consistency in products of regional and sectoral teams,
so their results can be used in preparing the National Synthesis Report.
The framework is not intended to be a final product, but to be further
developed through continuing discussions. Moreover, the framework cannot
possibly seek to be a complete set of instructions, specifying how to
make every required socio-economic or ecological (SEE) assumption for
every analysis: each team will have to make many such decisions. The final
section of this paper identifies some resources and contact people to
help you find data and think through your input decisions.
The first section discusses a few general principles for making SEE assumptions.
The second provides more specific, illustrative suggestions and examples
for putting these principles into practice. The third discusses the specific
SEE data that will be provided, and identifies sources of further information
and support.
In addition to the common approach outlined here, your team may wish
to perform various other types of analyses, perhaps using approaches very
different from what is outlined here. If you have time, we encourage you
to experiment with a variety of methods and approaches, and to report
both what worked and what did not work, so that one of the products of
this assessment is insight into how to do a better job next time.
General Principles:
In discussions in Woods Hole and elsewhere, it has been useful to make
explicit a few principles that have helped to organize our thinking about
SEE assumptions. This section summarizes these principles. The next section
provides some suggestions for putting them into practice.
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Climate Impact Assessment needs socio-economic and ecological
assumptions.
Simply describing how climate varies or changes is not sufficient to
understand impacts or vulnerability. Climate impacts happen to
societies, economies, and ecosystems, so describing impacts requires
information about the current (and in some cases the cumulative past)
state of the economic, social or ecological domain that bears the
impacts. Consequently, assessing impacts and vulnerability requires
assumptions (explicit or implicit) about the future state of society and
ecosystems in which climate impacts occur. These will not be the same as
today's society and ecosystems. In the future climate-changed world,
patterns of population, economic activity, technology, resource use, and
ecosystems will all likely differ from those of today, in ways that are
relevant to thinking about impacts. Consequently, while precise and
reliable prediction is impossible, assumptions about the future cannot
be avoided. In most cases, it is more reasonable to take on the hard job
of making the required SEE assumptions and making them explicit, than to
assume implicitly that the future will be like the present. If on
reflection you judge that the most reasonable assumption about some
particular future characteristic is that it will be unchanged from the
present, you should also make this assumption explicit.
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The approach must accommodate each team's data needs and specialized
expertise, while maintaining broad consistency of national assumptions.
The national assessment includes many separate studies of impacts at
finer-scale domains - impacts on particular activities, resources, or
values (e.g., coastal buildings, winter wheat yields, or heat stress on
human health) at particular spatial scales (e.g., a county, a
metropolitan area, a watershed, a particular coastal zone, a state, a
region). Each study will need different SEE data inputs, and each study
team will know best what data they need, and often what values are
plausible. The assessment process must take advantage of this
specialized expertise as much as possible.
But these fine-scale studies must ultimately be synthesized into
an assessment of climate impacts and vulnerability for the nation
as a whole. A few key variables are likely to be required inputs to
many studies, and to this national synthesis. Population and GDP are
obvious examples, although there could be others. For these, all studies
should use consistent assumptions. Moreover, the aggregation across
separate studies will be much easier if teams use a broadly parallel
approach to their studies, even when their specific data needs are
unconnected.
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Studies should choose a few key impact domains and
impact-determining variables.
The complexity of impact assessment can be overwhelming. Multiple
dimensions of climate potentially influence a vast number of coupled
economic, societal, and ecological values, and these influences are
mediated by a host of individual, organizational, and governmental
choices. Developing scenarios for such studies is a much harder problem
than developing emissions scenarios, because of the intensely
fine-grained, detailed, and coupled information required to describe and
understand impacts. Even within a single region or sector, no team can
hope to grasp the entire, massively complex problem.
Rather, the most we can hope for in the limited time available is
that each team undertake a few narrowly targeted studies, providing
thoughtful analysis of possible impacts, vulnerabilities, and key
decisions in a few key domains (e.g., particular impacts, activities,
resources, or locations) within their area. Both the regional and
sectoral teams have begun this process with their scoping exercises
and identification of key issues. But even studies of a few key
domains or issues will require major simplifications to deal with the
multiple factors that potentially bear on each domain. Attempting to
describe fully the SEE factors that influence the climate
vulnerability of, say, wheat cultivation in Kansas or heat-stress
deaths in Chicago, will quickly become an impossibly complex - and
arbitrary - exercise.
It is futile to attempt to describe the future in all detail, even
as it shapes climate impacts in one narrow domain. Instead, each study
should seek to identify a small number of key SEE variables that are
likely to have the strongest and most direct influence on climate
impacts and vulnerabilities in their domain. The analysis can then
proceed by assuming values for these few key variables, without
specifying how (of the multitude of possible ways) they came to have
those values. If a forest study decides that demand for commercial
forest products is a key variable determining impacts, they can assume
values for demand without having to specify what combination of
demographic, market, ecosystem, and technological factors caused
demand to take that value.
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Studies should reflect uncertainty by choosing ranges, not points,
for key variables.
Because the future is uncertain, it is important not just to assume
a single value for the key variables that influence impacts, but to
consider alternate values. The simplest approach is to consider a
high and a low value, so as to span a range. However confident you
may be that the future is going in a particular direction (e.g., demand
for forest products will increase), you should work hard to think
through other possibilities that could broaden the range of plausible
values for your impact variables. Considering a range of values for
key inputs is necessary to investigate how impacts and vulnerability
in your domain depend on these inputs. Moreover, when you estimate
ranges, you must work hard to draw them wide enough to accurately
reflect the applicable uncertainty. A host of research on decision-making
has shown that you are likely to be too confident in your predictions.
You should try to guard vigilantly against this bias.
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Impacts in a domain may depend both on characteristics of that domain,
and on characteristics of its broader socio-economic context.
It is clear that climate impacts within a particular domain will
depend on the SEE characteristics of that domain. But for many
domains, impacts may also depend strongly on the broader context in
which the domain is situated. Impacts and vulnerability in one county
or state may, for example, depend on the condition of other counties
or states nearby, or the nation as a whole; impacts in one economic
sector may depend on the condition of other sectors. These contexts
may affect impacts and vulnerability for several reasons. The context
- other places or activities - may compete with the domain for some
constrained resource (e.g., regional water supply may be shared among
nearby regions, alternative forms of agriculture, or between
agriculture and urban or industrial development.)
Alternatively, the context may provide resources that shape or
mitigate vulnerabilities: e.g., capital to support new investment or
adaptation; knowledge and capacity for technological and social
innovation; people to fill new jobs or places for people to move to;
support for transition to different livelihoods; or a governmental
capacity to make policies to manage vulnerabilities, adapt to impacts,
or compensate those who suffer from them. Where such linkages are
important, contextual conditions may either intensify or diminish the
vulnerability of the domain to climate impacts, or the significance of
these impacts.
Making the Principles Operational: Tentative Guidelines
Implementing these principles - getting SEE assumptions straight for
a particular analysis - will pose serious practical problems, many of
which will differ from study to study. Here, we provide suggestions and
illustrations on how to proceed. Perhaps your study will fit the approach
well; if not, you should feel free to adjust parts of the approach, or
in extreme cases discard them.
In particular, the following discussion assumes that analyses and key
SEE variables are quantified. Where possible this is a desirable approach,
but where not possible it should not be forced. Where quantitative analysis
or modeling is not feasible, a good qualitative treatment of possible
impacts will be more useful, both to your team and to the national synthesis,
than a seriously incomplete or biased attempt to perform a quantitative
assessment.
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Choose a few key impact domains to study, and a few key impact-determining
variables:
Most teams have already gone through a scoping exercise to identify
a few key impact domains on which they will concentrate. We would
only urge that all teams also establish a clear priority ranking of
these domains, reflecting your collective best guess of their overall
importance. With such a ranking, you can begin your analyses with
the highest ranked domains, so when you run out of time (as we all
will) you will at least be able to say something about the few most
important impact domains in your region or sector.
For each domain, the team should then list the most important SEE
variables that are likely to influence impacts and vulnerability in
that domain. You should choose a small number of such variables, that
appear have the strongest and most direct influence. These variables
should represent key SEE uncertainties, not objects of direct policy
choice, since key policy decisions that influence vulnerabilities
will be considered explicitly in your analysis.
Several teams went through this exercise at Monterey. Choosing a
few key SEE input variables is difficult, but is necessary to keep
the analysis to manageable complexity. The key uncertainties are likely
to be different for each domain of analysis; they may even be different
for the same domain at different times. For example, key inputs to
a health study may include the size of particular vulnerablepopulations;
for a water study, they may include the level of various categories
of water demands; or for a coastal study they may include the intensity,
character, and location of coastal development. Choosing the most
important input variables for your study may require that you first
do some scoping exercises or preliminary, exploratory analyses. For
some domains, you may find that national economic growth, or some
other characteristic of the national context, is a key input; for
others, all important inputs might be characteristics of the domain
itself.
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Choose Ranges, not points, for key impact-determining variables.
For each SEE variable you have chosen, estimate a high and a low value
that span a plausible range. Existing data or projections may provide
some guidance in estimating such a range; for certain demographic and
economic data up to the year 2030, high and low values (as well as a
middle, "baseline" value) will be provided. But in many cases, projecting
values for SEE variables will depend on the collective judgement of
team members.
How wide a range should you choose? In qualitative terms,
we suggest the range should include a substantial majority of uncertainty,
but should not seek to include low-probability extreme events. In quantitative
terms, the range should correspond - in the judgement of the team -
to an 80 per cent confidence interval. In your judgement, there should
be no more than 1 chance in 10 that the true value will be higher than
your "high" value, and no more than 1 in 10 that the true value will
be lower than your "low" value.
The purpose of the range you construct is to provide a set of alternative
SEE assumptions to construct baselines on which climate-related perturbations
are imposed. Your assumptions will support a series of "what if" analyses,
examining the impacts of various scenarios of climate change under
various alternative sets of SEE assumptions, and decisions that might
manage associated vulnerabilities. Consequently, these ranges should
include all sources of uncertainty except climate itself and policy
responses to it. Climate and climate responses should be examined
explicitly in the analysis, not concealed in the range of alternative
values assumed for SEE inputs.
There is one important exception to this exclusion of climate-related
uncertainties from SEE assumptions. For analyses where climate impacts
or policies outside the US are important, the range of SEE assumptions
should include these uncertainties. For example, climate and climate
responses abroad might have strong influence on US immigration, or
export demand for US agricultural products. If immigration or agricultural
export demand are key assumptions for a particular analysis, their
range should include this uncertainty.
However you choose values for the key SEE inputs to your analysis,
be careful to explain how you got them. When you scale national numbers
to a region, please explain how you did it. It may be useful to discuss
historical patterns of change, to illustrate what "high" and "low"
have meant in the past.
You should be keenly aware that you are likely to be too confident
in your predictions, and to underestimate the uncertainty that applies
to your estimates. Abundant research has shown that people are systematically
over-confident in estimation and prediction, and fail to account adequately
for uncertainty. In drawing uncertainty ranges for your key inputs,
this means that you must be careful to draw the ranges wide enough,
wider than you are initially likely to think is necessary.
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Combine Assumed values of impact variables to construct high and
low-impact scenarios.
At this point you should have the following: an ordered list of a
few key impact domains that are your priorities to study; for each
of these domains, a list of a few key SEE factors that in your judgement
most strongly influence impacts and vulnerabilities from a specified
climate change; and for each of these factors, a range of values reflecting
your collective judgement of its uncertainty.
Your analysis will of course require many other data inputs and assumptions,
in addition to these few key factors. To keep your analyses manageable,
only the few key factors you have identified should be varied. Other
inputs should be left at "best guess" or baseline values.
From here, you should combine high and low values for various SEE
inputs to generate a small, usefully diverse set of SEE scenarios.
From this point, differences among particular analyses are likely
to be of overwhelming importance in determining sensible ways to construct
these scenarios. While we continue to offer suggestions on how to
do this under certain ideal circumstances, you may find that minor
or major departures from our suggested approach are necessary for
your analysis to make sense.
- For each input variable, note which of its values (high or low)
you expect to be associated with high versus low impacts and vulnerability.
- Cluster your key input variables, according to your judgement
of how strongly they are likely to vary together. If possible, create
two clusters, such that the members of each cluster are strongly
correlated with each other (whether positively or negatively), but
only weakly correlated with the members of the other cluster. If
your important inputs include some local and some national, it might
be most appropriate to gather all the local ones into one cluster,
and all the national ones into the other.
- Finally, create four scenarios by setting all variables within
a cluster to their high and low-impact values respectively; and
combining high and low-impact settings for variables in the two
clusters. If your two clusters consist of local and national variables
respectively, the four scenarios will combine high vs. low impact
values for the local variables, with high vs. low impact values
for the national variables.
Your problem may fail to fit this approach in various ways. All important
SEE inputs may be local to your domain; the important inputs may span
various spatial scales from the local to the national; all important
variables may be strongly correlated; or several important SEE inputs
may all appear to be independently variable.
If these or other obstacles arise, you must rely on your judgment.
Ideally, the goal should be four SEE scenarios based on combining
important, independently variable SEE inputs when possible; where
this is not possible or appropriate, two SEE scenarios should be constructed
based on jointly varying one, or a few, key SEE inputs between their
high-impact and low-impact values. If at all possible, you should
strive to construct at least two distinct and plausible SEE cases.
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Do the Analysis
With decisions made about alternate values to assume for key SEE
impact variables, you are ready to proceed with the analysis. As with
constructing scenarios, the detailed form of analyses will vary greatly
across individual study domains.
A "canonical" analysis will examine the effect of a few distinct
climate scenarios, under a few distinct assumptions regarding SEE
conditions, to generate "what if" analyses that illustrate possible
impacts of particular climates under particular SEE conditions. Differences
between the baseline-climate world and the climate-scenario world
will reveal illustrative impacts and vulnerabilities. Differences
between impacts of the same climate scenario under alternative SEE
conditions will illustrate how vulnerabilities depend on SEE factors,
and may help to identify key decisions that strongly shape impacts
and vulnerabilities.
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Report, Revise, Iterate
The national assessment is an ambitious and novel exercise, and we
must all feel our way. We offer these detailed proposals for crafting
SEE scenarios, even though they are certain to appear inapplicable
or misguided for some - perhaps for many - analyses, in the spirit
of pushing the discussion quickly to the level of practical decisions.
If these suggestions are helpful, or if they are not, please let us
know; in particular, please let us know if you can suggest specific
improvements that would make this approach work better for your analysis.
Since this is the first national assessment, it is crucial that we
try to learn from the methods we employ, particularly including our
failures. Each analysis should yield new substantive and methodological
questions. Reporting both what worked and what did not work, and how
and why, will help to build the capacity to do a better job in subsequent
national assessments.
Above all we stress the importance of being parsimonious, in the
choice of both domains to study and SEE inputs to vary. If your team
studies three high-priority domains under three or four climate scenarios,
using three or four SEE scenarios, for two years (2030 and 2100),
this already yields 54 to 96 separate analyses. Further enrichments
will rapidly increase the number of combinations beyond all feasibility.
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The Difference between 2030 and 2100
The assessment focuses on two target years, 2030 and 2100. While
these dates are both far in the future to be making projections, 2030
is within the scope of some projection models and strategic-planning
tools, while 2100 is beyond almost all such projections. Consequently,
the richness of estimates available from which to construct SEE scenarios
differs greatly between the two target years.
For 2030, population and economic projections for three different
scenarios from a model developed by NPA Data Services, Inc, will be
distributed to all teams on CD-ROM. County-level information for each
year to 2050 will be available for population by sex and 5-year age
cohorts; as well as income and employment by industry sector at the
1-digit SIC code level, plus farm and non-farm proprietors, federal
and state/local government, and military. The information will also
be presented aggregated to states and metropolitan areas; alternatively,
your team can re-aggregate the county data to any other multi-county
region that is useful for your analysis. The three scenarios will
be: a baseline projection developed by NPA Data Services, Inc; and
a low-growth and high-growth scenario developed by NPA in consultation
with the NAST. The scenarios were developed to bound the most likely
range of values for U.S. population growth and economic growth. If
population, employment, and economic growth are among the most important
variables likely to influence impacts and vulnerability in the domains
that you are studying, we request that you use these projections.
This model and data series are widely used as planning tools in various
federal, state and local governments, and by the private sector. Staff
at Oak Ridge National Laboratory (ORNL) supporting the national assessment
are familiar with the model and data series, and are available to
provide technical support to national assessment teams in their use.
These data may provide many of the inputs you need for your impact
analysis in 2030, but probably not all. If you need economic data
at finer sectoral resolution, you will have to extend the NPA data
with additional assumptions; if you need information about specific
activities or technologies, land use or land cover, or ecosystems,
the NPA data will not provide it. In this case, you must find other
data sources or generate your own assumptions. Staff at ORNL are
also available to help you identify existing data sources or
projections relevant to your needs. Some team leaders have also
offered to help other groups with certain specific kinds of data
problems.
Contact information for these resources is provided below. 2100 is
much more difficult: uncertainties are wider, fewer projections are
available, and the problems of developing believable and useful sets
of assumptions are much harder. The only projections available for
this time horizon come from integrated-assessment models used to study
alternative time-paths of greenhouse gas emissions. Some of these
models provide values for US population, GDP, and emissions, but
little or no data at finer scale relevant to assessing impacts and
vulnerabilities.
To get a sense of how challenging the problem of developing useful
SEE scenarios for 2100 is, you might imagine trying to predict today's
world from the perspective of 1898. At that time, American's fertility
rates were higher; the social mix was sharply different from today's;
many more people lived on farms; aircraft, electronics and nuclear
technologies had not been invented; aluminum was a semi-precious metal;
and automobiles existed only as primitive novelties.
With such a daunting challenge and such sparse and suspect data,
teams might choose two broad directions to proceed. You might choose
to do the analysis for 2100 using essentially the same method as for
2030, generating your own estimates for much more of the input data:
identify key domains and key SEE variables for a hypothetical USA in
2100; choose a range of plausible values for each key SEE variable;
and subject a few alternate SEE futures to a few alternative climate
scenarios.
Uncertainties will be much wider and the entire approach more
speculative and qualitative, but the results may still be useful and
interesting. You might, for example, judge that the key SEE variables
determining impacts and vulnerability in a particular domain change
over time: e.g., economic activity levels might be most important in
the near term, but some specific aspect of technological change most
important in the long term.
Alternatively, you might examine impacts in 2100 by using an inverse
approach. This approach would be parallel to the use of inverse climate
scenarios for 2030, where you assumed some specific SEE picture for
2030 and asked how much climate would have to change, in what ways,
to yield large impacts.
To use an inverse approach for SEE assumptions, you would not fix
specific SEE assumptions in the year 2100. Rather, you would impose
a specific climate scenario - either a GCM output or a historical
analog climate - and ask what SEE characteristics would strongly alter
vulnerabilities to this particular climate. Under what plausible SEE
conditions would this climate bring large impacts, discontinuities,
or qualitative changes? (e.g., rendering important activities non-viable;
exhausting a key natural resource such as freshwater or a key economic
resource such as insurance reserves; pushing major enterprises into
bankruptcy or specialized communities below thresholds of viability.)
And conversely, under what plausible SEE conditions would this climate
impose essentially insignificant impacts?
Resources and Support
- Tom Wilbanks and his colleagues at Oak Ridge are available to provide
general support for assessment teams in identifying relevant sources
of data and projections for socio-economic variables, land use and land
cover, and in working with the NPA data set.
They prefer to receive inquiries by e-mail, so they have a written
record of the request to pass on to the relevant expert.
For general data inquiries:
- Tom Wilbanks
- Phone: 865-574-5515
- Fax: 865-576-2943
- E-mail: twz@ornl.gov
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- Sherry B. Wright
- Phone: 865-574-8651
- Fax: Same as above
- E-mail: sbw@ornl.gov
For inquiries specific to understanding and using the NPA data set
(for these inquiries, it would be most efficient, where possible,
to a team member with some knowledge of econometrics or regional economic
modeling):
- David P. Vogt
- Phone: 865-574-5192
- Fax: 865-573-8884
- E-mail: dpv@ornl.gov
- Several leaders and members of particular teams have expressed willingness
to help with specific inquiries in their areas of expertise, subject
to the constraints imposed by their own projects. Lynne Carter at the
National Assessment Coordination Office has offered to field inquiries
and direct them to people willing to help. She will also circulate a
list of people who have offered to be resources for specific types of
questions.
- Lynne Carter,
- National Assessment Coordination Office
- Phone: 202-314-2230
- Fax: 202-488-8681
- E-mail: lcarter@usgcrp.gov
- Outputs from runs of ecosystem models under the VEMAP project will
be available to national assessment teams. Contact Ben Felzer, National
Center for Atmospheric Research.)
- Ben Felzer,
- National Center for Atmospheric Research
- Phone: 303-497-1703
- Fax: 303 497 1348
- E-mail: felzer@ucar.edu
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