1. 1 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 simin.davoudi@ncl.ac.uk

2. 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

3. 3 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

4. 4 A 6°C rise in GA temperature will lead to extreme weather events!

5. 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

6. IPCC A1 Scenarios Assume ‘business as usual’: continuing increase in CO 2 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

7. 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

8. Exposure to climatic stimuli Sensitivity to climatic stimuli Territorial impact of climate change Adaptive Capacity Vulnerability to Climate change Conceptual framework

9. 9 (Füssel & Klein 2006)

10. 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

11. 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

12. Draft Typology of Climate Change Regions in Europe in 2100

13. ‘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

14. Sensitivity dimensions Physical Social Environmental Economic Cultural

15. 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

16. 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

17. 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

18. 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)

19. 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

20. Examples of economic sensitivity indicators Agriculture –Share of GVA for agriculture –Share of employment Tourism (winter / summer) –Number of beds per 1000 inhabitants

21. 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)

22. 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

23. 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

24. Selection criteria Case study area ESPON 3-level approach* Geographic coverage Macro-geographic regions Geo-morphological character INTERREG IVB cooperation areas Hanko (Finland) Local + EuropeanNorthern Europecoastal area, lowlandsBaltic Sea Region North Rhine- Westphalia regionalWestern Europeriver basin, hillsNorth West Europe Bergen localNorthern Europecoastal area, mountain area North Sea Region Tisza River basin trans-nationalCentral & Eastern Europe river basinCentral Europe South East Europe Spanish coast regionalSouthern Europecoastal area + IslandsWestern Mediterranean South West Europe The Netherlands nationalWestern Europecoastal area, river basin, lowlands North Sea Region, North West Europe Alpine space trans-nationalCentral Europemountain area, maritime Alps Alpine Space Western Mediterranean South East Europe

26. 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

27. 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)