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Climate change and territorial effectson regions and local economies

ESPON UK Network

8 March 2010, London

Prof. Simin DavoudiCo-Director

Institute for Research on Environment and Sustainabilitysimin.davoudi@ncl.ac.uk

Project partners• 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

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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!

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

Exposure to climatic stimuli

Sensitivity toclimatic stimuli

Territorial impact of climate change

Adaptive Capacity Vulnerability to

Climate change

Conceptual framework

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Conceptual frameworkConceptual framework

(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

• 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

• 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

ForestsShare of different types of forests

Protected ecological areasShare of Natura 2000 areas

Sensitive ecological areasShare 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

Infrastructure Greater variety of infrastructure enhances adaptive capacity

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

ESPON 3-level approach*

Geographic coverage

Macro-geographic regions

Geo-morphological character

INTERREG IVB cooperation areas

Hanko (Finland)

Local + European Northern Europe coastal area, lowlands

Baltic Sea Region

North Rhine-Westphalia

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 EuropeSouth 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 SpaceWestern MediterraneanSouth 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)