Download ESPON Climate Change project

Climate change and territorial effects
on regions and local economies
ESPON UK Network
8 March 2010, London
Prof. Simin Davoudi
Co-Director
Institute for Research on Environment and Sustainability
[email protected]
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Project partners
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Technical University of Dortmund, Lead partner
Budapest University of Technology and Economics
Newcastle University
University of Milan - Bicocca
Hungarian Institute for Regional Development & Town Planning
Potsdam Institute for Climate Impact Research
Geological Survey of Finland
The Swiss Federal Research Institute
Norwegian Institute for Urban and Regional Research
Netherlands Environmental Assessment Agency
Helsinki University of Technology
Universitat Autònoma de Barcelona
The Agency for the Support of Regional Development Košice
The evidence is compelling
• “Warming in the
climate system is
unequivocal” (IPCC 2007)
– Rise in GA temperature
– Rise in GA sea level
– Melting of glaciers and
disappearance of snow
caps
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A 6°C rise in GA temperature will lead
to extreme weather events!
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ESPON-Climate’s research
objectives
• To identify the territorial effects of climate
change on the European regional economies
• To provide a comprehensive and integrated
territorial view of vulnerability to climate change
• Main outputs: indicators, typologies and maps
representing the regionally differentiated
vulnerability to climate change
IPCC A1 Scenarios
• Assume ‘business as
usual’: continuing increase
in CO2 emissions
• Are based on:
– 9b population in 2050
and gradual decline
thereafter
– Spread of new and
efficient technologies
– Converging world in
terms of income and
lifestyle.
– Extensive social and
cultural interactions
A1B subset
• Is based on a
balanced use of all
energy sources
• Time scale for project
analysis:
1961-1990
compared with
2071-2100
• Unit of a analysis:
NUTS 3
Conceptual framework
Exposure to
climatic stimuli
Sensitivity to
climatic stimuli
Territorial impact of
climate change
Vulnerability to
Climate change
Adaptive
Capacity
Conceptual framework
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(Füssel & Klein 2006)
Exposure to climate stimuli
• Represents the nature and the degree to which
a system is exposed to climatic variations
• It depends on both the level of global climate
change and the spatial-temporal specificity of a
system
(Füssel and Klein 2006, p. 313).
• Exposure to climatic stimuli is directly
influenced by:
– Global climate change
– climate variability (variations in spatiotemporal scales)
– concentrations of greenhouse gases
Selected exposure indicators
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Change in annual mean temperature
Change in annual mean number of summer days
Change in annual mean precipitation in winter months
Change in annual mean precipitation in summer months
Change in annual mean number of days with heavy
rainfall
Change in annual mean surface runoff
Change in annual mean evaporation
Change in annual mean number of frost days
Change in annual mean number of days with snow cover
Draft
Typology
of
Climate
Change
Regions
in
Europe
in 2100
‘North Western’ Climatic Region
(e.g. southern parts of the UK)
• Strong increase in:
– annual mean number of summer days
• Strong decrease in:
– number of frost days
– annual mean precipitation in summer months
• Increase in:
– annual mean precipitation in winter months
– annual mean surface runoff
Sensitivity dimensions
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Physical
Social
Environmental
Economic
Cultural
Sensitivity dimensions
• Physical sensitivity: built environment (settlements,
infrastructure, etc)
• Environmental sensitivity: natural ecosystems (forest,
protected areas, etc)
• Economic sensitivity: economic sectors (agriculture,
tourism, etc)
• Social sensitivity: different social groups (elderly, low
income, etc)
• Cultural sensitivity: natural landscapes and built
heritage
Examples of physical sensitivity
indicators
Settlements
• % of settlement areas in flood prone river valleys
• % of settlement areas below 5m of average sea level
Infrastructure
• % of roads, rail networks, power plants in areas below
5m average sea level
• % of roads, rail networks, power plants in flood prone
river valleys
Examples of social sensitivity
indicators
Coastal population
% of population in coastal areas
Flood prone population
% of population in river flood prone areas
Senior citizens
% of population older than 65 years
Low-income groups
% of low income households
Examples of cultural sensitivity
indicators
Cultural monuments
• UNESCO world heritage sites in flood prone river valleys
• UNESCO world heritage site in areas below 5m (a sea l)
Cultural landscapes
• Share of UNESCO cultural landscapes
Cultural institutions
• Density of museums, galleries, libraries in flood prone
river valleys
• Density of cultural institutions in areas below 5m (a sea l)
Examples of environmental
sensitivity indicators
Forests
Share of different types of forests
Protected ecological areas
Share of Natura 2000 areas
Sensitive ecological areas
Share of sensitive eco-regions
Examples of economic sensitivity
indicators
• Agriculture
– Share of GVA for agriculture
– Share of employment
• Tourism (winter / summer)
– Number of beds per 1000 inhabitants
Adaptive capacity
• The ability or potential
of a system to respond
effectively to climate
variability and change
• It includes adjustments
in behaviour, resources
and technologies.
(IPCC 2007)
Determinants of adaptive capacity
(IPCC 2001).
Economic resources
Economic assets, capital resources, financial means and
wealth
Technology
Technological resources enable adaptation options
Information and skills
Skilled, informed and trained personnel enhances adaptive
capacity and access to information is likely to lead to timely and
appropriate adaptation
Greater variety of infrastructure enhances adaptive capacity
Infrastructure
Institutions
Existing and well functioning institutions enable adaptation and
help to reduce the impacts of climate-related risks
Equity
Equitable distribution of resources contributes to adaptive
capacity
Case studies
• The EU-wide approach is complemented
with in-depth case study analyses to:
– Provide a deeper understanding of the
impacts of climate change at the regional /
local levels
– Apply the EU-wide methodology at the case
study level
– Use the findings from the case studies to
refine the findings from the EU-wide analysis
particularly with regards to cultural sensitivity
analyses
Selection criteria
Case study area
Geographic coverage
ESPON 3-level
approach*
Macro-geographic
regions
Geo-morphological
character
INTERREG IVB
cooperation areas
Hanko (Finland)
Local + European
Northern Europe
coastal area, lowlands
Baltic Sea Region
North RhineWestphalia
regional
Western Europe
river basin, hills
North West Europe
Bergen
local
Northern Europe
coastal area,
mountain area
North Sea Region
Tisza River
basin
trans-national
Central & Eastern
Europe
river basin
Central Europe
South East Europe
Spanish coast
regional
Southern Europe
coastal area + Islands
Western
Mediterranean
South West Europe
The Netherlands
national
Western Europe
coastal area, river
basin, lowlands
North Sea Region,
North West Europe
Alpine space
trans-national
Central Europe
mountain area,
maritime Alps
Alpine Space
Western
Mediterranean
South East Europe
What to expect?
• Broad EU-wide analyses
• Overview of regional vulnerability to climate
change
• Identification of commonalities and differences
as a basis for cooperation
• Detailed case study analyses
• Inputs into:
– Revision of the EU White Paper on Adaptation to
Climate Change
– Review of the EU Cohesion Policy
Health warning!
• Uncertainty in climate change scenarios
• Use of one scenario (A1B)
• Use of one model (CCLM) for European
projections
• Data constraint at NUTS3 level (Eurostat &
ESPON)