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The Sustainable Development Research Program
The effect of data and measurement limitations and strategies for
overcoming them
Prepared for presentation at Open Meeting of Human Dimensions of Global Environmental Change Research Community
Montreal, Canada16-18 October 2003
Marc A. LevyCIESIN, Columbia University
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Prognosis
• Research efforts organized around the concept of sustainable development have increased over the past decade
• These efforts have failed to generate any major breakthroughs
• These efforts have failed to resolve major intellectual and policy debates
• Yet the efforts are increasing in intensity– What is needed to make them more effective?
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Things that are not part of the problem
• Normative or teleological nature of the effort– Most social sciences are rooted in normative theory
• Wooly nature of the core concept– Many research programs are built on foundations that
are conceptually vague
• Critiques that take aim at these “flaws” are cheap shots
Economics: make people better off
Politics: make people more free
International Relations: make the world peaceful
Public Health: help people live longer, better lives
Wooly concepts: Economics – utility; Psychology – the mind;
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Things that are part of the problem
• “Stove pipe” operationalization of sustainable development data needs
• “Grab bag” operationalization of sustainable development needs
• Underinvestment in question-oriented data creation
• Underinvestment in question-oriented data integration
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Stove pipes
Environmental Social Economic
Sometimes we think we are making progress meeting data needs by filling separate thematic bins with relevant data
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Stove pipes
Pressure State Response Impacts
There are more sophisticated variants on such stovepipes
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Filling stove pipes with data doesn’t get us very far
• Helps identify very broad patterns• Doesn’t help answer questions• Doesn’t help identify causal relations• Induces misplaced complacency
– Data compendia get larger and larger, but not necessarily more useful
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Grab bags
• First effort to operationalize sustainable development at a consensus level: Agenda 21
• Almost anything a patient bargainer wanted to include made it in.
• License to label almost any data set a scholar could find as relevant to sustainable development
• Diffused data creation and integration efforts– Obscured need to set priorities, make choices
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Underinvestment in question-oriented data creation
• Land Use/Land Cover Change– 150 years after Marsh identified it as a global-
scale issue, still no global-scale data• We are unable to characterize patterns of land
use/land cover change globally
• Governance– No empirical basis for characterizing the nature
of institutional arrangements that govern resource globally, across relevant scales
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Underinvestment in question-oriented data integration
Why has our understanding of water/population interactions tended to look mostly like this?
This is just one example of an overly simplistic understanding of the relationship between human and physical phenomena
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Physical scientist’s view of water data
% Popu la ti on wi thi n 20 0 Km o f Coa st0 - 2 020 - 4040 - 6060 - 8080 - 10 0
Physical scientist’s view of demographic data
Physical scientists insist on pushing data requirements on physical phenomena to ever increasing precision, but frequently are content to use coarse social science data
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Social scientist’s view of population data
Social scientist’s view of water data
Meanwhile, social scientists devote considerable effort to improving resolution of human phenomena, but often seem content with very coarse phys ical data.
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Why so little data integration?
• Doing it right isn’t overwhelmingly difficult, but isn’t easy either– Requires intensive interdisciplinary
collaboration– Few incentives to do this– Researchers are impatient and take shortcuts
with data outside their own disciplines that scholars within those disciplines would not take
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Consequences of Current Data Shortages
• Unable to provide usable answers to high-priority sustainable development questions– What is relevance of environmental change to
achieving global poverty reduction goals?• Unable to characterize global patterns
– Job of communicating global change patterns to decision-makers monopolized by physical sciences
• Scholars who need evidence to test hypotheses, to get published, to get tenure, resort to levels of analysis where data are available– Field turning into a fractionated thicket of thousands of
unconnected case studies. Conclusions aren’t scaling up and we aren’t getting closer to understanding the questions that originally motivated to do this work.
We have dropped the ball
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Setting Strategic Priorities
• Time• Space• Systems
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Time
• We’re supposed to study human dimensions of global change, but we have almost no data on the global changes that most directly affect people.– Land Use/Land Cover– Water availability, access, quality– Air quality– Natural Hazards
• We’re forced to use static snap-shots, or to patch together ad hoc collections that aren’t consistent over time, or to rely on model calculations
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Space
• Studying human aspects of environmental change requires placing social science data in a comparable spatial context to global change data.
• Progress in basic population data• Slow progress in other dimensions
– Health– Wealth– Security– Equity– Shelter– Sanitation
Otherwise it is like trying to study the interaction of two liquids while keeping them in separate beakers.
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Maps
These preliminary infant mortality maps are examples of the strategy of making the spatial dimensions of sustainable development more explicit and more precise
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GNP Per Unit Area (1990)1990 US$
0 - 1,000
1,001 - 100,000
100,001 - 1,000,000
1,000,001 - 10,000,000
10,000,001 -
Dry Subhumid
5.6% of world total
By making spatial dimensions more explicit, it becomes possible to combine data in ways that are more relevant to sustainable development dynamics. In this example, world GNP is aggregated to the dry subhumid ecosystem, one of the ecosystems in which human well-being is, in general, markedly lower than average.
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This example shows how spatial data can help explore the connection between water supply technology and water-borne disease.
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This preliminary map shows the distribution of underweight children.
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Family Income (log)
1.51.0.50.0-.5-1.0-1.5-2.0
INF
AN
T M
OR
TA
LIT
Y R
AT
E (
per
1000
live
birt
hs):
1991
200
100
0
-100
Arid climate
1.00
.00
In the arid zones of Brazil, the income-infant mortality relationship is much less pronounced, and the range of observed incomes is narrower.
This is another example of how the spatial strategy facilitates new research directions. It shows the relationship between family income and infant mortality in Brazil (from the 1991 census), broken down by aridity zones. Within Brazil’s arid regions, increases family income are not associated with as large reductions in infant mortality as they are in other regions of Brazil.
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Systems
• Snapshots, trends and maps are not enough• We study complex coupled systems• We have very few measurements of
important systemic attributes– Critical thresholds– Transitions– Non-linearities– Syndromes
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Emerging Exceptions
• Vulnerability measurement and mapping• PIK global change syndromes• Dematerialization measurements• Search for Environmental Kuznets Curves• But far to go
– No compelling global measurements– Syndromes, dematerialization and EKC not yet focus of
much causal research (mostly descriptive)• Analysis of systemic features of human/global
change interactions left largely to the modelers
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What’s needed
• Set priorities– What do we need most?
• Pool efforts– Share work that fills data gaps
• Coordinate fundraising and implementation strategies
Business as usual will not generate satisfactory responses to the data needs facing the sustainable development research program. Serious effort, coordinated across scholars and stakeholders, is needed.
