environment accounts and statistics division division des comptes et de la statistique de...

Environment Accounts and Statistics Division

Division des comptes et de la statistique de l'environnement

Climate Change IndicatorsThe Role of National Statistical Offices

UN Expert Group Meeting on

Climate Change and Sustainable Development New York

15-16 October 2008

Robert SmithStatistics Canada



Climate change indicators –A Statistical roadmap

1. Framework – What to measure and why?– Two options

• Policy as the guide• Theory as the guide

2. How to measure?• Accounting systems• Classifications

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Division des comptes et de la statistique de l'environnement 3

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 policy-based

– 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



Policy-based frameworksStrengths

• 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



Classifications• Many existing classifications are relevant

– industries, products, census regions

• Also needed are– 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



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– Land use and land cover– Water use and availability– Air quality – Ecosystems– Energy



Thoughts on building environmental data 1. Choose a clear and robust conceptual framework to guide

data collection– Avoids ad hoc collection

2. Follow the success story of economic statistics to build environmental statistics programs

– Focus on time series of a few key indicators

3. Ensure cooperation between statistical offices and policy departments

4. Bring environmental statistics more into the mainstream – Many opportunities lost today due to poor coordination of effort

5. Build a spatial analysis capacity and collect data that can exploit its potential

– Cannot measure climate change impacts except spatially



Thank you

Robert SmithDirector, Environment Accounts and Statistics

Statistics [email protected]

613-951-2810

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