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Andrew Copus Rural Policy Unit Scottish Agricultural College. Martin Price Centre for Mountain Studies Perth College UHI Millennium Institute. A Preliminary Characterisation of the Mountain Area of Europe. Aim.
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Andrew Copus Rural Policy Unit Scottish Agricultural College Martin Price Centre for Mountain Studies Perth College UHI Millennium Institute A Preliminary Characterisation of the Mountain Area of Europe
Aim • To prepare a preliminary characterisation of the mountain area of Europe based on available statistical reporting areas and data
Methodology • to identify a European mountain area using consistant criteria that is spatially compatible with existing databases • to undertake a statistical analysis of selected socio-economic variables for this area
Existing national definitions: EU • linked to support for agriculture • altitude ( + slope ) ( + > 62° N ) Total area (excluding Belgium, East Germany, Finland, Ireland): 780,000 km²
Recent statements: mountain regions of the EU • “Some 30% of community territory consists of mountain ranges or massifs” • (European Commission - DG Regional Policy, 2000) • “mountain regions account for about 30% of the land area … in the European Union” • (European Parliament Committee on Agriculture and Rural Development, 2001) • “Mountain areas as % total EU15 surface area: 38.8%” • (Second Report on Economic and Social Cohesion, 2001)
Existing national definitions: non-EU • Minimum altitude • 350 m: Poland • 500 m: Yugoslavia • 600 m: Bulgaria, Slovakia, Slovenia, Norway • 650 m: Albania, Croatia • 700 m: Czech Republic, Romania
Development of consistent criteria • UNEP – WCMC map (2000) • USGS GTOPO30 altitude database at 1 km² resolution • slope • local elevation range (relief) • 7 km radius • > 300 m elevation change
Europe’s mountain area • 23% (746,321 km²) of EU area is mountainous • 19% of Europe (excluding CIS) is mountainous
Definition of NUTS III mountain regions • UNEP-WCMC map • NUTS III regions • EU and Accession States • Norwegian Fylke • Swiss Cantons • Balkan States • classification of “mountainousness”
Thresholds of mountainousness • Wholly mountainous (>95% within WCMC boundary) • Predominantly mountainous (60-95%) • Partly mountainous (40-59%)
Definition of NUTS III mountain regions >95% within WCMC boundary
Definition of NUTS III mountain regions >60% within WCMC boundary
Definition of NUTS III mountain regions >40% within WCMC boundary
The NUTS III database 1 Mountainousness 2 Area 3 Urban areas 4 Population 5 Population density 6 GDP/capita (purchasing power parity) 3-6 from Eurostat (comparable data for Balkans, NO, CH)
Total area within UNEP-WCMC boundary, (NUTS III regions by % mountain threshold)
Proportion of UNEP-WCMC mountain area within NUTS III regions by threshold
GDP 1999 (purchasing power parity)European NUTS III mountain regions
Impact of large towns • >100,000 population (critical mass to affect regional economy) • 114 “NUTS III” regions >40% mountain and with large town • 86 EU regions • 16 CEEC regions • 8 Norwegian / Swiss regions • 4 Balkan states
The role of peripherality Source: Schürmann, C., Talaat, A. (2000): Towards a European Peripherality Index. Report for General Directorate XVI Regional Policy of the European Commission, Dortmund, Institut für Raumplanung, Universität Dortmund
Peripherality and Mountainousness: (a) • Peripheral mountain regions are experiencing depopulation
Peripherality and Mountainousness: (b) • Peripheral mountain regions have a lower GDP per capita
Conclusions • The data suggests that mountain regions have some disadvantages hampering socio-economic development relative to lowlands, in terms of: • population • GDP/capita • this relationship is complicated by • peripherality • presence/absence of large towns
Some words of caution: • NUTS III geography is inadequate • size/configuration of regions • little consistency across EU and CEECs (MAUP) • “ecological fallacy” in mixed regions • need for finer-resolution data(e.g. NUTS V?) • lack of harmonised data (even at NUTS III) • few variables • lack of standardisation • need to use national data sources