Transcript Frameworks

Climate Change Indicators
The Role of National Statistical Offices
UN Expert Group Meeting on
Climate Change and Sustainable Development
New York
15-16 October 2008
Robert Smith
Statistics Canada
Environment Accounts and Statistics Division
Division des comptes et de la statistique de l'environnement
Climate change indicators –
A Statistical roadmap
1. Framework – What to measure and why?
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Two options
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Policy as the guide
Theory as the guide
2. How to measure?
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Accounting systems
Classifications
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1. What to measure and why?
• Frameworks tell us what to measure and why –
they are the tools that give structure to statistics
– Policy-based frameworks
• Policy serves as the guide for what to measure and
why
• Most indicators of sustainable development are policybased
– Theory-based frameworks
• A theoretical understanding of the issue provides the
guide for what to measure and why
• The classic example is economic statistics, where
macroeconomic theory guides all data collection
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Policy-based frameworks
Strengths
• Strengths
– Guaranteed buy-in from stakeholders
– Responsive to the express needs of decision
makers
– Flexible and adaptable as needs and
understanding change
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Policy-based frameworks
Drawbacks
• Not always theoretically defensible
• Can lead to long indicator lists because of
“horse-trading”
• Prone to frequent revision, often with
every change of government
• Often differ significantly from one country
to the next
– Even if the issues actually don’t
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Theory-based frameworks
Strengths
• Politically neutral
• Can be defended based on agreed
principles
– Can only be refuted if the theory is refuted
• Applicable across countries
• Not inflexible
– But also not subject to frequent change
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Theory-based frameworks
Drawbacks
• Theories that are not widely accepted can
lead to statistics that are not relevant
• Theory may be entirely absent in a given
domain, leaving policy as the only guide
• Theory leaves little room for stakeholder
input, possibly limiting buy-in or bringing
accusations of elitism
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Policy or theory as a guide for statistics?
• Both have their place, but...
...theory-based frameworks are closer to what statistical
offices strive for in general
• Help ensure political independence of the statistical system
• Help ensure “compactness” in indicator sets
– No need to add indicators for political acceptability
• Indicators should form a complete, coherent and
interpretable set
• Stability over time allows time series to be built
• Wide applicability allows for int’l comparisons
• The question is whether a suitable theory exists
for climate change indicators
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Capital theory as a framework for
climate change indicators
• Climate change impacts are long-term
– Capital theory is well suited to long-term issues
• The standard capital framework must be broadened
– Most importantly, to include natural capital
– Recognizing human and social capital also important
• The good news is that much of the thinking has
already been done
– Rich academic literature
– Joint ECE/OECD/Eurostat Working Group on Statistics for
Sustainable Development
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2. How to measure?
• Statistics compatible with those in other domains
are needed
– Understanding climate change impacts requires
linkage of environmental, economic and social
data
• UN System of Environment and Economic
Accounts (SEEA) is very close to what is needed
– A rigorous framework for organizing
environmental stock and flow data
– Closely aligned with economic statistics through
the System of National Accounts
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Classifications
• Many existing classifications are relevant
– industries, products, census regions
• Also needed are
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ecological classifications
land cover and land use classifications
drainage basin classifications
waste classifications
classifications of ecosystem services
• Climate change impacts will be spatially
differentiated
– therefore, spatial classifications are key for analysis
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Data to address climate change impacts
• Economic and social data reasonably complete
– Health data may be an exception
• Environmental data are where the weaknesses
are mainly found, including
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Land use and land cover
Water use and availability
Air quality
Ecosystems
Energy
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Thoughts on building environmental data
1.
Choose a clear and robust conceptual framework to guide
data collection
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2.
Follow the success story of economic statistics to build
environmental statistics programs
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3.
4.
Focus on time series of a few key indicators
Ensure cooperation between statistical offices and policy
departments
Bring environmental statistics more into the mainstream
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5.
Avoids ad hoc collection
Many opportunities lost today due to poor coordination of effort
Build a spatial analysis capacity and collect data that can
exploit its potential
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Cannot measure climate change impacts except spatially
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Thank you
Robert Smith
Director, Environment Accounts and Statistics
Statistics Canada
[email protected]
613-951-2810
Environment Accounts and Statistics Division
Division des comptes et de la statistique de l'environnement