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  • 1. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Proposed demographic scenarioTitle Proposed demographic scenario analysis and overview of driving forces and justification, model input parameters and allocation rules.Creator UNIGECreation date 12.06.2011Date of last revision 02.10.2011Subject Demographic scenarios at national and regional (NUTS2) scale for enviroGRIDS countriesStatus FinalizedType Word documentDescription The results are for use by WorkPackage 3.2. (climate modeling), 3.3 (land- cover modeling, 4 (hydrological modeling) and 5 (case studies), as well as for other purposes.Contributor(s) Andrea de Bono (UNIGE), Hy Dao (UNIGE), Ana Silva (UNIGE), Ana Barbosa (UMA), Emanuele Mancosu (UMA) .Rights PublicIdentifier enviroGRIDS_D3.5Language EnglishRelation D3.2, D3.6, D.3.7Abstract:This document illustrates the different phases leading to the creation of demographic scenariosfor the countries included in the Black Sea Catchment (BSC). According with the enviroGRIDSscenarios, we analyze the UN projection variants for population, and we propose a methodologyfor the downscaling from national to regional level (NUTS2). Results include urban and totalpopulation trends over the period 2010-2050 for the 214 enviroGRIDS regions, consistent withBS HOT, BS ALONE, BS COOP and BS COOL scenarios.Successively, we illustrate the methodology do define the estimation of future urban areasurfaces, based mainly on the historical and future trends of urban densities. -1-
  • 2. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentContentsINTRODUCTION ............................................................................................................................... 3PURPOSE AND SCOPE ....................................................................................................................... 41 PROPOSED DEMOGRAPHIC SCENARIOS: UN NATIONAL SCENARIOS AND ASSUMPTIONS ............................ 5 1.1 UN ASSUMPTIONS .................................................................................................................................................. 6 1.1.1 Medium-fertility assumption ...................................................................................................................... 7 1.1.2 High-fertility assumption............................................................................................................................. 7 1.1.3 Low-fertility assumption ............................................................................................................................. 7 1.1.4 Constant-fertility assumption ..................................................................................................................... 7 1.1.5 Urban Population Projections ..................................................................................................................... 7 1.2 INTEGRATION INTO THE ENVIROGRIDS SCENARIOS ......................................................................................................... 9 1.2.1 Global Competitive/trend (A1): BS HOT .................................................................................................... 11 1.2.2 BS Region fragmented (A2): BS ALONE ..................................................................................................... 13 1.2.3 BS Region strong cooperation (B1): BS COOP ........................................................................................... 15 1.2.4 Adaptive/ Sustainable (B2): BS COOL ........................................................................................................ 172 DEMOGRAPHIC SCENARIOS QUANTIFICATION: REGIONAL DEMOGRAPHIC MODEL .................................. 19 2.1 CHOICE OF REGIONS .............................................................................................................................................. 19 2.2 REGION BOUNDARIES ............................................................................................................................................. 21 2.3 POPULATION FIGURES ............................................................................................................................................ 21 2.4 URBAN POPULATION AND CITIES ............................................................................................................................... 23 2.5 REGIONAL PROJECTIONS AND ASSUMPTIONS ................................................................................................................ 26 2.6 DOWNSCALING PROJECTIONS FROM UN NATIONAL DATA ............................................................................................... 273 METRONAMICA INTEGRATED TOOL FOR LAND COVER CHANGE MODELLING: DEMOGRAPHIC INPUTS .......... 33 3.1 DEMOGRAPHIC DATA INPUTS ................................................................................................................................... 33 3.2 CALIBRATION ....................................................................................................................................................... 33 3.2.1 Cells allocation for MODIS 2008: the Corine Land Cover approach .......................................................... 34 3.3 SCENARIOS .......................................................................................................................................................... 36 3.3.1 Projections of urban land cover surface.................................................................................................... 364 CONCLUSION AND PERSPECTIVES ............................................................................................... 41REFERENCES ................................................................................................................................. 42ANNEXES ..................................................................................................................................... 44 TERMINOLOGY ................................................................................................................................................................ 44 REGION NAMES AND POPULATION ....................................................................................................................................... 47 -2-
  • 3. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment IntroductionenviroGRIDS1 is a EU FP7 project, aiming to address the subjects of ecologically unsustainabledevelopment and inadequate resource management in the Black Sea Catchment area.The Workpackage 3 (WP3) is going to implement a set of models and tools for the production ofdemographic (Task 3.1), climatic (Task 3.2) and land cover change (Task 3.3) scenarios at theBlack Sea Catchment scale. Scenarios hold a number of plausible alternatives (storylines) whichare based on a coherent set of assumptions, with key relationships and driving forces. Thestorylines are based on the IPCC – SRES (Nakicenovic et al., 2000) four marker scenarios whichrepresent different global socio-economic development pathway (UAB, 2010).These scenarios provide different views on the future of the BSC study through the explorationof what might happen given certain assumptions about the development of society andenvironmental change. The enviroGRIDS simulation of the future scenarios for land use is basedon a dynamic spatial system based on a cellular automata model. The inputs to the model aredifferent types of spatially referenced digital data including:• Land use maps showing the distribution of land use types in the area of interest. These mapsinclude MODIS 2001 and 2008, respectively the start and end of the calibration period.• Suitability maps showing the inherent suitability of the area of interest for different land usetypes. These maps are created using an overlay analysis of maps of various physical,environmental and institutional factors.• Zoning maps showing the zoning status (i.e. protected areas) for various land uses in the areaof interest.• Accessibility maps showing accessibility to transportation networks for the area of interest.• Socio-economic data, for the main administrative regions of the area of interest, comprisingdemographic statistics.The model outputs consist of maps showing the quantification of the four alternative scenariosfor the predicted evolution of land use change in the area of interest, over the next fourth years.In this deliverable we will describe the different phases of demographic data collection andanalysis focusing on their integration into a regional model including land use and climaticscenarios of change. This regional Black Sea Catchment (BSC) model is created using theMetronamica (Riks, 2005, 2009; UAB, 2010) software environment.Metronamica is a unique generic forecasting tool for planners to simulate and assess theintegrated effects of their planning measures on urban and regional development. As anintegrated spatial decision support system, Metronamica models socio-economic and physical1 http://www.envirogrids.net/ -3-
  • 4. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentplanning aspects, by incorporating a mature land use change model that helps to make theseaspects spatially explicit. Purpose and ScopeThe main objective of Task 3.1 is to set up an infrastructure and to implement analyticalmodules that will enable the production of time series of prospective demographic data, to beused as input for the hydrological basin models (WP 4: SWAT) as well as for previous impactassessments on different societal benefit areas.The final outputs of Task 3.1 correspond to two main objectives:Objective 1): set-up the demographics inputs to build the integrated scenarios of change:Regional urban population (this Deliverable)The first objective is to produce the population datasets to be used in the integrated model ofpopulation, land use and climate change for the four scenarios of change. The specificity ofthese datasets will conform to the Metronamica environment. Its simplified workflow includes:  Setting up of a demographic database at regional level (“NUTS2-like”) including Urban and Total population for the base years 2001 and 2008, and cities > than 10,000 inhabitants  Population projection until 2050 according to the enviroGRIDS (task 3.4) scenarios and UN variants  Estimation of urban area surfaces until 2050 following the enviroGRIDS scenarios.Objective 2): Spatial disaggregation of population data for demographic scenarios of change(winter 2011)This second objective is to transfer the regional demographic database from the originalpolitico-administrative units to a reference grid (downscaling). By producing raster grids ofpopulation at the relevant spatial resolution, it is possible to further re-aggregate the cell valuesto any size of spatial units. In order to allow for future eventual interaction with land useproducts, the population data must be as much as possible free from information about landuse. Although the outputs of Task 3.1 will be used as input for the hydrological catchmentmodels (WP 4) and for the impact assessments in WP5, the demographic data and modelsshould also be of use for any other users interested in harmonized, disaggregated and projectedinformation on population distribution. The workflow of the second objective includes:  Setting up of a demographic database at sub-national level (“NUTS3”) and urban areas  Gathering of ancillary data (“suitability” and “zoning”) maps for population allocation  Downscaling phase based on selected methodologies already described in Deliverable 3.1 (UniGe, 2010) -4-
  • 5. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment  Calibration / verification of the results using very fine settlements data for Ukraine and GeorgiaThis objective will not be discussed in this deliverable and will be further developed in a specificpublication at the end of 2011.1 Proposed Demographic scenarios: UN National scenarios and assumptionsIn deliverable 3.1, we discussed about different demographic projections adopted by severalorganizations (UN Eurostat, Espon) and we proposed the use of data from the UN PopulationDivision: the World Prospect Population (WPP), and the World Urbanization Prospectus (WUP)(UN/DESA, 2009, 2010)The most common tool in population projections, at the national level, is the so-called cohort-component method. Eurostat, the UN and most national statistical bureaus use this method fortheir population projections. The cohort-component model is a deterministic populationprojection method, which means that it does not describe uncertainty, but refers to a set ofscenarios chosen to represent plausible, possible or relevant (e.g., to investigate the impact of apolicy change) future paths of migration, fertility and mortality.In order to conduct a cohort-component population projection, one needs a description of thebase population in the initial year of the projection. Information on the number of individuals bygender in every age group is required. Age-specific (using one- or five-year age groups)assumptions on future fertility, mortality and migration rates are used to project the population,normally in one- or five-year time intervals.Cohort-component models are essentially “what-if” estimation of the future, where populationtrends are determined by a set of assumptions. These assumptions could reflect a continuationof past trends or an investigation into what would happen if there is a (constant) change in oneor more of the demographic variables.Demographic components: FertilityFertility assumptions are usually expressed as period fertility, i.e., total fertility rate (TFR) whichis the sum of age-specific fertility at one point in time. Assumed increases or reductions in TFRcould either be caused by proportional shifts in fertility across the reproductive life span orchanges in early or late childbearing patterns.Demographic components: MortalityMortality assumptions for projections are often simplified to changes in e(0), i.e., life expectancyat age 0. This involves implicit assumptions on the ages at which mortality is reduced (e(0) couldincrease due to mortality reductions primarily early or primarily late in life).Demographic components: MigrationMigration flows fluctuate strongly and may depend on a large range of factors, includingbusiness cycles in both the sending and receiving countries, family connections in a destination -5-
  • 6. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentcountry, cost of migration, destination’s reputation, attitudes to immigration and immigrationlaws. Migration is strongly influenced by policies. (Skirbekk et al., 2007) This contrasts withmortality and fertility where the impact of policy is much less evident. Migration regulations andpractices are difficult to foresee, which makes migration the possibly most difficult demographiccomponent to predict.To project the population until 2050, the United Nations Population Division (UNPD) evaluatesthe most recent information available on each of the three major components of populationchange: fertility, mortality and international migration, using assumptions regarding their futuretrends. Because future trends cannot be known with certainty, a number of projection variantsare produced.1.1 UN AssumptionsThe 2010 Revision (UN/DESA, 2011) includes eight different projection variants (Figure 1). Fiveof those variants differ among themselves only with respect to the level of fertility in each, thatis, they share the assumptions made with respect to mortality and international migration. Thefive fertility variants are: low, medium, high, constant-fertility and instant-replacement fertility.A comparison of their results allows an assessment of the effects that different fertility pathshave on other demographic projectionsFig.1: Eight projections variants from UN Population Division. The first three variants Low, Medium and High (in thered box) are kept for the Envirogrids project.Mortality assumption:Mortality is projected on the basis of models of change of life expectancy produced by theUnited Nations Population Division (UN/DESA, 2009). The selection of a model for each countryis based on recent trends in life expectancy by sex. For countries highly affected by the HIV/AIDSepidemic, the model incorporating a slow pace of mortality decline has generally been used toproject a certain slowdown in the reduction of general mortality risks not related to HIV/AIDS. -6-
  • 7. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentNormal migration assumption:Under the normal migration assumption, the future path of international migration is set on thebasis of past international migration estimates and consideration of the policy stance of eachcountry with regard to future international migration flows. Projected levels of net migration aregenerally kept constant over most of the projection period.1.1.1 Medium-fertility assumptionTotal fertility in all countries is assumed to converge eventually toward a level of 1.85 childrenper woman. However, not all countries reach this level during the projection period, that is, by2045-2050. Projection procedures differ slightly depending on whether a country had a totalfertility above or below 1.85 children per woman in 2005-2010.Fertility in high and medium fertility countries is assumed to follow a path derived from modelsof fertility decline established by the United Nations Population Division on the basis of the pastexperience of all countries with declining fertility during 1950-2000.1.1.2 High-fertility assumptionUnder the high variant, fertility is projected to remain 0.5 children above the fertility in themedium variant over most of the projection period. By 2045-2050, fertility in the high variant istherefore half a child higher than that of the medium variant. That is, countries reaching a totalfertility of 1.85 children per woman in the medium variant have a total fertility of 2.35 childrenper woman in the high variant at the end of the projection period.1.1.3 Low-fertility assumptionUnder the low variant, fertility is projected to remain 0.5 children below the fertility in themedium variant over most of the projection period. By 2045-2050, fertility in the low variant istherefore half a child lower than that of the medium variant. That is, countries reaching a totalfertility of 1.85 children per woman in the medium variant have a total fertility of 1.35 childrenper woman in the low variant at the end of the projection period.1.1.4 Constant-fertility assumptionFor each country, fertility remains constant at the level estimated for 2005-2010.1.1.5 Urban Population ProjectionsEurope is one of the most urbanized continents on Earth. Today, approximately 75 % of theEuropean population lives in urban areas (UN/DESA, 2010). The urban future of Europe,however, is a matter of great concern. More than a quarter of the European Unions territoryhas now been directly affected by urban land use. By 2020, approximately 80 % of theEuropeans will be living in urban areas, while in seven countries the proportion will be 90 % ormore (EEA, 2006). -7-
  • 8. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentThe projections calculated for Urban areas by the United Nations are included in the WorldUrban Prospectus published in 2009 (WUP09). They estimate future data using the mediumvariant fertility assumption.Urban projections are recalculated by applying the Urban/Total population ratio, using the threestandard fertility assumptions (high, medium and low), constant fertility and zero migration.Figure 2 summarizes the different scenarios for the full BS Catchment and for its urban areas. -8-
  • 9. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Fig.2: Urban and total population projections for BSC countries including six variants (WUP 2009, WPP 2010)While total population for the sum of the countries of the BSC is predicted to decrease for all theWPP10 variants, except for High fertility, the urban one shows a clearly different pattern whereonly the data estimate with a low fertility assumption has a strong decreasing trend (fig. 2).Projected values, using the medium variant, point to a generalized increase of urban populationfor all single countries in the BSC (fig.3).Fig.3: Urban population is growing everywhere in UN Projections (medium variant) in the BSC countries (WUP 2009)1.2 Integration into the enviroGRIDS scenariosThe proposed enviroGRIDS scenarios are a number of plausible alternatives (narrative storylines)which are based on a coherent set of assumptions, about key relationships and driving forces, tocreate a set of quantitative information, internally consistent and spatially explicit scenarios offuture climate, demography and land use covering full Black Sea Catchment (UAB, 2010).These scenarios provide different views on the future of the BSCC study through the explorationof what might happen given certain assumptions about the development of society andenvironmental change.Four main scenarios of the Black Sea Region based on IPCC-SRES scenarios (Nakicenovic et al.,2000) are presented here: -9-
  • 10. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment- Global Competitive/trend (A1) named “BS HOT”- BS Region fragmented (A2) named “BS ALONE”- BS Region strong cooperation (B1) named “BS COOP”- Adaptive/ Sustainable (B2) named “BS COOL” Scenarios Driving forces HOT (A1) ALONE (A2) COOP(B1) COOL (B2) Population growth low highest low medium GDP growth highest low high medium Area of Forest increase decrease increase decrease Area of Grassland increase decrease increase decrease Area of Cropland decrease or stable increase decrease increase Area of Build-up area increase increase increase increase Protected Areas stable stable increase stable Climate change high high low low Technological Development high low high medium Water Demand high --- low ---Fig.4: Resume of driving forces from (UAB, 2010) - 10 -
  • 11. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment1.2.1 Global Competitive/trend (A1): BS HOT The BS HOT scenario contains the highest economic growth, with low population increase and highest increase in Greenhouse gas emissions and consequently global climate change.Fig.5a: Development pattern (UAB, 2010)  Germany, Austria and Italy, Switzerland are included in the past development countries group, showing at present the highest GDP with smallest rates of future economic growth. They are also expected to have a certain increase in population growth at least during the first period 2000-2025.  Turkey, Ukraine, Bulgaria, Georgia, Moldova are included in the future hard development pattern group. This pattern outlines the association between future highest economic growths countries expected to exhibit also the strongest depopulation.  Belarus, Romania, Serbia, Bosnia and Herzegovina, Montenegro and Albania are included in the future development pattern group which are currently states of changing economic status and also expected to have population growth, with less depopulation process in the future.  Slovenia is the only country with high economic status, also expected to undergo substantial future economic growth, but with less depopulation process in the future.  Russia, Poland, Croatia, Hungary, Slovakia, Czech Republic, Macedonia are included in the delayed development pattern which consists of different states expected to reach higher economic prospects in the future and also with depopulation processes. - 11 -
  • 12. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment - 12 -
  • 13. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment1.2.2 BS Region fragmented (A2): BS ALONE The BS ALONE scenario is characterized by lower trade flows and regionally oriented economic growth Small economic growth is expected, however the estimations for the BSC countries indicate mostly future economic growth in the Eastern Europe surrounding the Black SeaFig.5b: Development pattern (UAB, 2010)• Germany, Austria and Italy, Switzerland are included in the past development countries,showing at present the highest GDP with smallest rates of future economic growth. They arealso expected to have a certain increase in population growth at least during the first period2000-2025.• Ukraine, Georgia, Moldova are included in the future hard development pattern. Thispattern outlines the association between future highest economic growth countries expected toalso exhibit the strongest depopulation.• Turkey, Bulgaria, Belarus, Romania, Serbia, Bosnia and Herzegovina, Montenegro andAlbania are included in the future development pattern which are currently states of varyingeconomic status, also expected to have population growth, with less depopulation process inthe future.• Slovenia is the only counter with high economic status, also expected to undergosubstantial future economic growth, but with less depopulation process in the future.• Russia, Poland, Croatia, Hungary, Slovakia, Czech Republic, Macedonia are included inthe delayed development pattern which includes different states expected to reach highereconomic prospects in the future and also with depopulation processes. - 13 -
  • 14. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment - 14 -
  • 15. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment1.2.3 BS Region strong cooperation (B1): BS COOP The BS COOP Scenario combines high economic development with prospects of low population increase. The depopulation processes are only prominent during the first period (2000-2025) and nearly no further population changes are estimated afterwards.Fig.5c: Development pattern (UAB, 2010)• Germany, Austria and Italy, Switzerland are included in the past development countries,showing at present the highest GDP with smallest rates of future economic growth. They arealso expected to have a certain increase in population growth at least during the first period2000-2025.• Romania, Serbia and Montenegro, Ukraine, Bulgaria, Georgia, Moldova are included inthe future hard development pattern. This pattern outlines the association between futurehighest economic growths countries expected to exhibit also the strongest depopulation.• Turkey, Belarus, Bosnia and Herzegovina, and Albania are included in the futuredevelopment pattern which are currently states of varying economic status, also expected tohave population growth, with less depopulation process in the future.• Slovenia is the only counter with high economic status, also expected to undergosubstantial future economic growth, but with less depopulation process in the future.• Russia, Poland, Croatia, Hungary, Slovakia, Czech Republic, Macedonia are included inthe delayed development pattern which includes different states expected to reach highereconomic prospects in the future and also with depopulation processes. - 15 -
  • 16. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment - 16 -
  • 17. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment1.2.4 Adaptive/ Sustainable (B2): BS COOL The BS COOL scenario combines intermediate economic growth with medium population density increase globally. In the BSC however, population numbers are only expected to increase in the group of recent development. Generally this scenario reveals the most heterogeneous patterns of developments in the BSC countries.Fig.5d: Development pattern (UAB, 2010)• Germany, Austria and Italy, Switzerland are included in the past development countries,showing at present the highest GDP with smallest rates of future economic growth. They arealso expected to have a certain increase in population growth at least during the first period2000-2025.• Bulgaria, Albania, Macedonia are included in the future hard development pattern. Thispattern outlines the association between future highest economic growths countries expectedto exhibit also the strongest depopulation.• Turkey, Ukraine, Moldova, Georgia are included in the future development patternwhich are currently states of varying economic status, also expected to have population growth,with less depopulation process in the future.• Slovenia, Poland, Slovakia, Czech Republic, Croatia are included in the recentdevelopment pattern, showing high economic status, also expected to undergo substantialfuture economic growth, but with less depopulation process in the future.• Russia, Belarus, Romania, Hungary, Bosnia and Herzegovina, Serbia and Montenegro areincluded in the delayed development pattern which includes different states expected to reachhigher economic prospects in the future and also with depopulation processes. - 17 -
  • 18. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment - 18 -
  • 19. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment2 Demographic Scenarios Quantification: regional demographic modelThe aim of this section is to illustrate the workflow used to transpose the demographic scenariosof each country to a sub-national level, in order to build regional scenarios of urban populationthat will be used with the land use (and Climate) scenarios into the Metronamica environment.Fig.6: data processing workflowMetronamica needs the following demographic inputs:  Number of cells of urban people for 2001 and 2008 at regional level for the calibration phase,  Number of cells of urban people for 2009-2050 according to the three main UN assumptions (high-low-medium) for the scenarios phase2.1 Choice of regionsThe main reason to use a regional model is the fact that single countries are only partiallyincluded in the enviroGRIDS coverage. We need to disaggregate countries into smaller entitiesand then re-aggregate them. - 19 -
  • 20. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentAnother good reason is that regional dynamics are more active than national dynamics; theregional model simulates distribution of overall growth or decline of population. This simulationis based on a spatial interaction or gravity model operating on a regional scale. Each regionattracts people and jobs from each other region proportional to its own attractiveness.For EU countries in the EG BSC the regions correspond to the NUTS2 level (for furtherinformation about NUTS see Terminology paragraph). For those regions outside the EU theregions are selected to correspond as much as possible in terms of size and data availability withthe NUTS2 .NUTS2 level is the best compromise in terms of spatial resolution versus availability of data (i.e.projections) and timing of calculation during the calibration phase in “Metronamica.”Fig.7: the 214 regions, in red the BSCWe selected all the regions included in the Black Sea Catchment (BSC) including those that arepartially (even if only by 1 km2) comprised in the BSC, as a result of the fact that the statisticaldata associated within the regional boundaries is not (easily) spatially divisible.These 214 regions (fig 7) will be the extent of Reference for the WP3 integrated scenarios. - 20 -
  • 21. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment2.2 Region boundariesThree main sources were used to prepare the GIS regional layers at level NUTS2  GISCO/Eurogeographics2  FAO Global Administrative Unit Layers (GAUL)3  Local enviroGRIDS partners (UNO and Geographics).The procedure to assemble these heterogeneous sets of data consists in 10 differentgeoprocessing steps and a final validation phase.Unfortunately several regions have changed status or delimitation boundaries between theedition on the GAUL layer and the date of publication of the statistical data. The necessarymanual corrections have been carried out.2.3 Population figuresThe data on population per region were collected for all available years from several sourcesincluding Eurostat, several National statistical offices, and enviroGRIDS partners (e.g. Ukraine(ONU) and Georgia (Geographics)).Data were heterogeneous and needed a robust process of harmonization before the integrationin the regional geodatabase.The integration process consists in three main steps  Formatting the original data from the statistical offices in a uniform way. Sometimes it is not in tabular format or it is in a local language; moreover in some cases administrative boundaries do not correspond with the statistical nomenclature.  Match the statistical and geometric data together (except for EU 27) by using regions names ‘like’ and often a manual join.  Generate a unique ID.  Filling gaps for missing years.Years 2001 and 2008 correspond to the start and end of the calibration period used in theMetronamica calibration (see next chapters). Our effort was to concentrate on collecting datafor these periods as far as possible directly from the sources.Once the two years of the calibration period were completed, a time series was built for theperiod 2000-2010:2 http://epp.eurostat.ec.europa.eu/portal/page/portal/gisco_Geographical_information_maps/introduction3 http://www.fao.org/geonetwork/srv/en/metadata.show?id=12691 - 21 -
  • 22. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentWe calculate the annual growth ratio using the difference of population figures between knownyears. Successively the ratio where applied to estimate the missing years. For the majority ofregions calculations were filled by interpolation: estimations within the range of a discrete set ofknown years. The first set of results includes regional maps at circa NUTS2 level for 2001 and2008The same process was also applied to generate data at circa NUTS3 level for the year 2001.These data were further utilized in the spatial disaggregation of population data.Fig.8: regional population data for 2001 and 2008, within sources - 22 -
  • 23. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment2.4 Urban population and citiesThe land use data used in the Metronamica regional model is the Moderate Resolution ImagingSpectroradiometer (MODIS) data at 1-km spatial resolution which is included as part of theMODIS Collection 4 (C4). The Global Land Cover Product MODIS (USGS, 2010) has the advantagethat it comprises several releases over the time. Data for 2001 and 2008 were utilized during theMetronamica calibration phase (deliverable 3.6, UMA in progress). MODIS has another greatinterest: it has the best accuracy to detect “artificial surfaces” (Potere and Schneider, 2007;Schneider et al., 2009)Fig.9: Comparisons in artificial class detection between six classical datasets from (Schneider et al., 2009)On the MODIS land use (2001 and 2008) the built environment is represented by the artificialsurfaces class (Af). It includes all non-vegetative, human-constructed elements, such as roads,buildings, runways, etc (i.e. human made surfaces), and ‘dominated’ implies a coverage greaterthan 50% of the cell size. Therefore, this class includes both residences and infrastructure.The number of the people who live in these regions is known. It can reasonably be supposedthat the urban part of the total population of the region is concentrated (to live or work) inthese artificial surfaces or “urban land”. - 23 -
  • 24. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.10: total and urban population & urban land cover: example from Crimea (Ukraine)However, we cannot assume that all urban land identified in the MODIS land cover mapcorresponds exactly to cities and towns. Since the map designates all land with impervioussurfaces as ‘urban’, it must necessarily include considerable amounts of village and farm land as”urban” (Schneider et al., 2010). Our goal consists therefore to estimate the urban fraction ofthe population in the BS regions. We have to deal with two main challenges: the lack of urbandata at regional level for several countries, and the different definitions of “Urban“ betweencountries (see terminology for more detail). The Eurostat urban data for example wasrecalculated by using the ratio of households living in urban areas delimited as zones with morethan 500 inhabitants per km2). Russian and Belarus define urban-area according to the criteria ofnumber of inhabitants and predominance of agricultural, or number of non-agricultural workersand their families. Switzerland considers communes of more than 10,000 inhabitants, includingsuburbs as urban. Other national statistical offices deliver data without giving a clear definitionof the “urban” data.To solve the lack of harmonization from the different sources of the urban dataset, we proposeto find a single and common way to define the urban population.Geopolis (e-Geopolis, 2011) is a global database that allows an international comparison of citysizes, beyond the diversity of national official definitions of urban units; they define an urbanagglomeration as a continuous built-up area where at least 10.000 inhabitants live. Keeping inmind the amount of data available and our final objectives, we suggest to use the populationsize of cities as the discriminating parameter to define the limit between urban and rural areasin a region. - 24 -
  • 25. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFinally, we have created a geodatabase of cities containing their population and geo-localizationfor cities having more than 10k inhabitants in 2001.Fig.11: map showing the 3130 cities with more than 10k inhabitants in 2001 and the ratio between urban and totalpopulation per regionThe cities geodatabase was created by using several sources both for spatial location of citiesand their population (mainly collected for the period 2000-2010)The urban population for a region (PuRegCty) was defined as: PuRegCty(2001) = ∑ Pcities(2001)Where ∑ Pcities(2001) is the sum of all the cities of the region having more than 10,000 inhabitantsin 2001Population from cities and from urban areas as defined by national statistical offices showgenerally a very strong correlation (R2 > 0.95, figure 12). However few values are discordant;most countries (90 percent EU27) display more inhabitants for cities than urban (ratio < 1) dueto a difference of Eurostat definition. Non EU27 countries have generally more urban populationthan cities population. - 25 -
  • 26. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.12: Relationships between population in cities (over 10k ) and urban areas as definited by National statisticalofficiesData for cities were collected mainly for the years 2001 and 2008. A time series for the period2000-2010 is planned. Unfortunately we do not dispose actually of population projections atcities level: UNSD/WUPP has released a database including projections until 2050 only for citieshaving more than 750,000 inhabitants in 2005.2.5 Regional projections and assumptionsProjections at regional level are scarce. Eurostat produced regional population projectionsfigures for EU 27 countries and Switzerland for the period 2008-2030 included in the EUROPOPproject (Eurostat, 2010). In EUROPOP, Population projections are “what-if” scenarios about thelikely future size and structure of populations compiled using the standard demographic cohort-component model. Population growth is the result of two components: (N) natural change(births minus deaths) and (M) total net migration (international and internal migration).Assumptions were made about national residential mobility and the degree of attractiveness ofthe regions; therefore, assumptions were made about internal mobility as a whole (intra- plusinterregional moves) plus the convergence/divergence of the regions in terms of attractiveness(full convergence would signify that net inter-regional migration is zero). In the current regionalEUROPOP2008 population projections, internal mobility and regional differences are assumednot to change from the recent situation (calculated as an average of internal migration flows inrecent years depending on countries’ data availability). - 26 -
  • 27. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.13: Range of the regions relative population change between 2008 and 2030 (from EUROPOP). Yellow colourindicates population increase while the blue one the decrease. This figure clearly illustrates the difference of regionspopulation dynamics inside the same country.Outside the EUROPOP countries coverage we only have projections at regional level for theRussian Federation. The Federal statistical office published regional data (oblast) for the period2010-2016 and for separate years including 2021, 2026 and 2031.For all the BSC countries during the period 2030 to 2050 we do not have any data at regionallevel; for non EU 27 no data is available for after 2010.2.6 Downscaling projections from UN national dataThe objective is to estimate regional projections for the period from 2030 to 2050 for EUROPOPcountries and from 2010 to 2050 for all other countries. Estimations will be based on theextrapolation and downscaling from UN national data.Extrapolation builds on the assumption that future demographic developments can be derivedfrom past population trends and hence, a continuation of observed demographic change isassumed. This method is widely used and refers to approaches where one uses past values ofonly the factor of interest and the error term (and disregards other variables) to calculate futuretrends.In order to estimate the regional projections we analyze how population varies between regionsinside the same country: we calculate the “regional share” across the time (see text box). Wewill base all our further analyses on this parameter. - 27 -
  • 28. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentRegional share: Rsh(t) = Pr(t) / Pn(t)Where Rsh represents the fraction of the total population living in the region at time t; Prrepresents the population living in the region and Pn is the national population (as defined bythe National statistical office). - 28 -
  • 29. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment - 29 -
  • 30. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Extrapolation of regional share: For each Rsh trend we extrapolate the new values using a linear regression; the choice of the known values is evaluated case by case on the basis of the Rsh function. For EUROPOP countries we have to extrapolate data based already on projections only for the period 2030-2050. For non EUROPOP countries extrapolations are based only on past data, and distributed for all the period 2010-2050. We have to keep in mind that these extrapolations only concern the distribution of peoples during time among regions; real population data (from UN) will be further calculated. Fig.14: Example of trends of regional share for Cz Republic (EUROPOP country) and Belarus - 30 -
  • 31. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Text box: workflow example for Austria National Urban Index (RUIdx): We calculate the evolution of the ratio between the urban and total population during time. (2001-2050) at national level, using WUP and WPP data Results are expressed as an Index where the base year is 2001 (2001 = 100): the RUIdx Downscaling the National Urban Index to the regions: Assumption: the urban growth rate is distributed uniformly between regions: this represents clearly a limitation of our model. RUsh(t) = (PtReg(2001) / PuRegCty(2001)) * RUIdx(t) RUsh(t is the percentage of urban population per region at time t PtReg (2001) corresponds to the regional total population in 2001 PuRegCty(2001) corresponds to the regional urban population in 2001 - 31 -
  • 32. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment downscaling total population PtReg (t,v) = Rsh(t) * Pnat(t,v) PtReg (t,v) corresponds to the regional total population at time t for the selected UN variant v (high medium low) Pnat(t,v) is the national population at time t for the selected variant v downscaling: urban population PuReg (t,v) = RUsh(t) * PtReg (t,v) PuReg (t,v) corresponds to the regional urban population at time t for the selected UN variant v (high medium low) RUsh(t is the percentage of urban population per region at time tResults from downscaling will include regional (214) projections for the period 2010-2050 forurban and total population, according with the three UNSD variants (High, Medium and lowfertility) - 32 -
  • 33. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment3 Metronamica integrated tool for land cover change modelling: demographic inputs 3.1 Demographic data inputsMetronamica (Riks, 2005, 2009)is a unique generic forecasting tool for planners to simulate andassess the integrated effects of their planning measures on urban and regional development. Asan integrated spatial decision support system, Metronamica models socio-economic andphysical planning aspects, by incorporating a mature land use change model that helps to makethese aspects spatially explicit. Metronamica allows the planner to interactively enter policy andplanning measures as well as trend lines for external pressures and scenarios.The regional model in METRONAMICA is used to allocate the total population and jobs in themain economic sectors at the national level over the regions and to simulate the migrationbetween regions (Riks, 2005, 2009).The allocation of the growth amongst the regions depends to a large extent on the relativeattractiveness of each region. In modeling the national socio-economic growth and migrationdistance between urban areas also play a crucial role. The underlying assumption is that regionscan benefit from each other attractiveness, as long as the distance is not too far. Furthermore,people and jobs are reluctant to migrate over greater distances.In MODIS Land cover, urban areas are places dominated by the built environment. The ‘builtenvironment’ includes all non-vegetative, human-constructed elements, such as roads,buildings, runways, etc. In other words, habitations and places of works are merged and is notpossible to distinguish between them.Our regional model distributes the population of the entire study area over the regions. It knowsonly a single population class (neither cohorts nor socio-economic groups).3.2 CalibrationFor each region the number of inhabitants for both base years 2001 and 2008 is essential inorder to compute these values into urban surface or the “cell demand” in the Metronamicaenvironment. For 2001 we can extract directly the values by a simple “zonal statistics” perregion from the urban land cover class of MODIS. Concerning 2008 we have a problem: thenumber of cells linked to the Artificial class (Ac) from MODIS 2001 to 2008 records only amarginal change. Is it related to effectively small change in the physical parameters? Probablyurban zones have not changed so much in seven years. Moreover they have a relative inertia torecord the change at 0.5x0.5 and 1x1 km2 cell size; but it is not enough to explain such a smallvariation in MODIS. It reflects probably a generalized error due to limitations of remote sensingat this resolution, to detect small changes in the artificial surfaces. - 33 -
  • 34. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.15: the figure summarizes the workflow used to estimate the urban cells demand.The challenge consists of estimating the effective number of cells demanded for urban surface in2008 (fig. 15).3.2.1 Cells allocation for MODIS 2008: the Corine Land Cover approachIn order to estimate the real urban surface (number of cells) for MODIS 2008, we will base ourconsiderations on the variation of urban land surface in Corine Land Cover (CLC) at 250 m for theyear 2000 and 2006Relations between CLC and MODIS artificial surfaces per regions indicate a coarse correlation (r2=0.6) with a generalized under estimation of MODIS Artificial class. (fig.16) Fig.16: Weak correlations between CLC and MODIS - 34 -
  • 35. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentSuch a weak correlation prevents the direct use of values from CLC surfaces. Outliers are morerepresented in rural areas with strong underestimation of MODIS.We plotted the variation of densities (percentage per year) from CLC 00 and CLC 06 using oururban population (01-08) against regions (fig. 17).Fig.17: Variation of densities from Corine Land Cover between 2000 and 2006Two important considerationscan be derived from the chart:  There is a general strong tendency to decrase the density according to (Angel et al., 2010c).  The regions (few) that have increased their density (Istambul, Lombardia, Trentino, Wien) are those where the space for their expansion is (now) limited.From the analysis of the correlation between densities in CLC and MODIS 2008, two otherassumptions are made:  For regions that are included inside CLC coverage the same annual ratio of change of density multiplied by the regional urban population will be used.  For regions that are outside the CLC coverage the value of density in 2008 is calculated using a linear regression, based on a strong correlation between CLC densities in 2000 and 2006 (fig.18). Urban land surface will equal the urban population in 2008 multiplied by the calculated density from CLC. - 35 -
  • 36. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.18: relationships between densities in CLC 2000 and 20063.3 ScenariosThe regional model in Metronamica requires the number of supposed future cells for each landuse class according to the selected scenario.3.3.1 Projections of urban land cover surfaceThe estimation of the future number of cells for urban land is not an easy task, since theavailable data are estimations of urban population, according to UN projections andassumptions. Furthermore it is not known how this population is distributed across the territory:if cities become denser or if, alternatively, they will extend their surface.In a recent work, the Environmental European Agency (EEA, 2006), focuses on the physicallyexpansion of urban areas also known as “urban sprawl” (see terminology for more detail),mainly characterized by a low density mix of land uses on the urban border. In Europe, citieshave traditionally been much more compact, developing a dense historical core shaped beforethe emergence of present day transport systems.The main driver leading to cities growth, historically, was the increase of urban population.However, in Europe at present, a variety of factors are still driving sprawl, even if the urbanpopulation shows small or no increase. Global socio‑economic forces are interacting with morelocalized environmental and spatial constraints to generate the common characteristics of urbansprawl evidenced throughout Europe today. EEA identified urban areas particularly at risk ofuncontrolled urban sprawl in the southern, eastern and central parts of Europe.Urban population, income, and the availability of land for urban expansion are three keyexplanations of why urban land cover varies among countries (Angel et al., 2010a; Angel et al.,2010c). This explanation is supported by several sets of multiple regressions models at globalscale with country urban land cover as the dependent variable and urban population, GDP,arable land, transport cost as independent ones. Outcomes from the model seem to confirm the - 36 -
  • 37. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentclassical economic theory of urban spatial structure predicting that urban land cover willincrease with population and income, as well as with a reduction in transport costs (Brueckner,1987).In Europe, historical trends, since the mid 1950s, show that cities areas have expanded onaverage by 78 %, whereas the population has grown by only 33 % (EEA, 2006). A majorconsequence of this trend is that European cities have become much less compact (fig. 18). Text box 1: Results from cities density patterns • Cities with rapidly growing populations have significantly slower rates of density decline. • Cities in countries with rapidly growing incomes have significantly faster rates of density decline. • Cities with high initial densities or low initial incomes have significantly faster rates of density decline. • Cities with larger populations have significantly slower rates of density decline. • Cities with no geographical constraints on their expansion in all directions have significantly faster rates of density decline. • Densities in cities in land-rich countries do not decline at faster or slower rates than cities in other countries Source: (Angel et al., 2010b)Fig.18: Built up area, population and road network density forselected EU and accession countries (EEA, 2002)Trends towards new low density environments are also evident in the space consumed perperson in the cities of Europe during the past 50 years, which has more than doubled. Inparticular, over the past 20 years the extent of built-up areas in many western and easternEuropean countries has increased by 20 % while the population has increased by only 6 %.Sprawl is greater, and in many cases significantly greater, than it would be expected on the basisof population growth alone (JRC, 2006).Future developments of urban land cover are often predicted on assumptions that are largelybased on past trends. The average density in the built-up areas of a global sample of 120 cities - 37 -
  • 38. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentdeclined at a mean annual rate of about 2 percent between 1990 and 2000 (Angel et al., 2010b).There was no significant difference in the rate of decline between more-developed and less-developed countries.Fig.19: Density differences among selected world cities in three regional sub-groups 1990 and 2000 (Angel et al.,2010b)On the basis of these analyses the authors propose three density scenarios to project urban landarea until 2050:  “High projection”: assuming a 2% annual rate of density decline, corresponding to the average rate of decline of the global sample of 120 cities, 1990-2000;  “Medium projection”: assuming 1% annual rate of density decline, corresponding to the long-term rate of density decline in an historical sub-sample of 30 cities;  “Low projection”: assuming constant densities, or a 0% annual rate of density decline, more realistic for US cities that are more subjected to urban sprawl than European ones. They also calculate the urban land cover projections, based on the three density assumption for the whole countries of the world. The 23 enviroGRIDS countries are showed in figure 20: - 38 -
  • 39. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFig.20: density scenarios from (Angel et al., 2010c) applied to the BSC (whole) countriesIn the PLURIEL FP7 EU project (Rickebusch, 2010) artificial surfaces projections until 2025 wereestimated using a multiple regression model. In this model, the proportion of artificial surfacesper NUTS2 (EU 25 coverage only) is a function of GDP per capita, population, and urban type:large city vs smaller city/rural region. A similar model was already applied by (Reginster andRounsevell, 2006)By lack of projections data at regional level (GDP) for non EU countries, we cannot use themethodology described above. Urban land-use future trends will be based on an interpretationof qualitative storylines in terms of density variations, and in a quantitative way in terms ofpopulation by using the UN projection variants.We have quite good estimation of population trends and we have to suppose in a qualitativeway the evolution of urban densities to estimate the urban land cover. The following exampleillustrates the different possibilities for the Austrian region of Tyrol by applying severalcombinations of urban population and densities. - 39 -
  • 40. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentFigure 21: Urban land cover projections: regional example for Tyrol:Each enviroGRIDS scenario is characterized by its peculiar demographic trend, together with aspecific urban land evolution path. We derivate the regional figures for inhabitants from UN andnational statistical offices, while the urban patterns will be based in function of the differentassumption of the density. In other words, the population positive (or negative) growth will bethe same for all the regions according to the selected UN variant, although the variation ofdensity will be set in function of the characteristics of each regions according with the criteriaexplained in the figure 22The definitive designation of the regional densities is still under testing and validation. - 40 -
  • 41. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment scenario population urban land cover density assumptions growth Increase urban areas and demand in high general decline for populated regions. Strong attraction of urban - HOT low almost all regions (1-2% agriculture areas around the existing annual) settlements Urban areas and demand will increase. Dispersed urban sprawl - new settlements are expected in touristic areas. Inertia in general decline for ALONE highest existing urban areas with strong interaction almost all regions (1% neighbors gradually failing – some expansion annual) of existing small town. Easy conversion of natural areas into urban Urban areas increase in density but not in Constant to increase COOP low area - compact growth urban areas will “stick” (1% annual) for almost to its present location regions Urban areas smoothly increase. Inertia for Constant to increase COOL medium urban areas will stick to its present location – (1% annual) for almost small changes regionsFigure.22: Proposed annual trend of densities for the four EG scenarios. These values are at present still under test.4 Conclusion and PerspectivesThe main purpose of Task 3.1 was to set up an infrastructure and to implement analyticalmodules that will enable the production of time series of prospective demographic data. Thesedata will be used as input for the hydrological basin models (WP 4: SWAT) as well as for previousimpact assessments on different societal benefit areas. Afterward, our first objective consist toproduce the population datasets to be used in the integrated model of population, land use andclimate change for the four enviroGRIDS scenarios of change. The specificity of these datasetswill conform to the Metronamica environmentThe expected outputs include dataset of public domain that will be available for download at theend of 2011 1. Regional maps (NUTS2 level) for total and urban population 2001-2010 (GIS output) 2. Regional maps (NUTS2 level) for total and urban population 2011-2050 according to the three UN variants: Normal, High and Low fertility (GIS output) 3. Cities geodatabase: includes inhabited places with more than 10,000 people in 2001 (GIS output) 4. Urban land cover surfaces in MODIS 2001 and MODIS 2008 (Tabular output) 5. Projected urban land cover surfaces in MODIS for the period 2009-2050 (Tabular output)Measured data and projections of urban land areas (outputs 4 & 5): are the specificrequirements for Metronamica. The next step will consists to integrate them within the otherland Modis cover classes (D 3.7 enviroGRIDS) at spatially explicit level (1km x 1km) for the entire - 41 -
  • 42. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentBSC using the Metronamica model, according with the four alternative scenarios. TheenviroGRIDS deliverable 3.8 will focus on the result of this process of integration.Our second objective will be axed on the spatial disaggregation of population data; as previouslydescript on purpose and scope chapter, it will be developed in a further publication. It willconsist in a set of raster grids of population at 1km x 1km resolution, resulting of a downscalingfrom politico-administrative units onto a reference grid. In this deliverable we focus only onthese demographic data that were essential to produce the inputs for Metronamica in order toproduce a set of integrated land cover maps. The expected future raster grids of population willillustrate an evolution during time of people distribution independently from land use. Onedirect application will be the possibility to evaluate the number of inhabitants per spatial unitsby cell aggregation, for example to estimate the number of persons living in a specific sub-basin.The Integrated outputs of spatially explicit scenarios from WP 3 will be primarily used as inputfor the hydrological catchment models (WP 4), furthermore several end products from Task 3.1will also offer a useful and precious support to others EG workpackages. The regional maps ofpopulation (outputs 1 & 2) at NUTS2 level as well as the cities databases (output 3) will be of usefor any other users interested in harmonized, disaggregated and projected information onpopulation distribution. Especially in case of WP5: Impacts on selected Societal Benefit Areas,notably in those objectives concerned by vulnerability analysis.Probably the greatest defy and time consuming task of this work was the collection andharmonization of datasets at regional and cities level. National statistical offices andinternational organisations together with gazetteers provided the essential of demographicfigures; we aware that this heterogeneity of sources could be represent a supplementaryincertitude in the final model.Further improvements of the model can be also obtained refining the potential of each region interms of people displacement: we experienced that an estimation of urban densities andregional attractiveness could be better developed if coupled with economic data, such as grossdomestic product or unemployment figures. Unfortunately these kinds of data and especiallytheir projections at regional level are extremely scarce or inexistent for several BSC countries.The outputs 1 & 2 will be the first release, in the public domain, of a GIS dataset including pastand projected harmonized population figures at Nuts2 level (Nuts3 is under process) includingboth Western, Central and Eastern European countries.ReferencesAngel, S., Parent, J., and Civco, D., 2010a, The Fragmentation of Urban Footprints: Global Evidence of Sprawl, 1990-2000: Cambridge, USA, Lincoln Institute of Land Policy.Angel, S., Parent, J., Civco, D., and Blei, A., 2010b, The Persistent Decline in Urban Densities: Global and Historical Evidence of ‘Sprawl’: Cambridge, USA, Lincoln Institute of Land Policy. - 42 -
  • 43. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentAngel, S., Parent, J., Civco, D., Blei, A., and Potere, D., 2010c, A Planet of Cities: Urban Land Cover Estimates and Projections for All Countries, 2000-2050: Cambridge, USA, Lincoln Institute of Land Policy.Brueckner, J., 1987, The structure of urban equilibria: A unified treatment of the Muth-Mills model, in Mills, E., ed., Handbook of Regional and Urban Economics: New York, Elsevier, p. 821-845.e-Geopolis, 2011, Population of urban areas of 10.000 inhabitants or more.EEA, 2002, Environmental signals 2002.: Luxembourg, Office for Official Publications of the European Communities.—, 2006, Urban sprawl in Europe The ignored challenge, EEA Report No 10/2006: Copenhagen, European Environment Agency.Eurostat, 2010, Regional population projections EUROPOP2008: Most EU regions face older population profile in 2030, Eurostat: Statistics in Focus 1/2010.JRC, 2006, Monitoring Land Use/Cover Dynamics (MOLAND): Ispra.Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Grubler, A., Jung, T.Y., Kram, T., La Rovere, E.L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N., and Dadi, Z., 2000, Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change.: Cambridge University Press.Potere, D., and Schneider, A., 2007, A critical look at representations of urban areas in global maps: GeoJournal.Reginster, I., and Rounsevell, M., 2006, Scenarios of future urban land use in Europe: Environment and Planning v. 33, p. 619-636.Rickebusch, S., 2010, Maps of Land-use change scenario projections for Europe, FP6.Riks, 2005, Metronamica: A dynamic spatial land use model, in (RIKS), R.I.f.K.S.b., ed.: Maastricht.—, 2009, Metronamica documentation, in (RIKS), R.I.f.K.S.b., ed.: Maastricht.Schneider, A., Friedl, M.A., and Potere, D., 2009, A new map of global urban extent from MODIS satellite data: Environmental Reasarch Letters, v. 4, p. 1-11.—, 2010, Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’: Remote Sensing of Environment, v. 114, p. 1733–1746.Skirbekk, V., Prommer, I., KC, S., Terama, E., and Wilson, C., 2007, Report on methods for demographic projections at multiple levels of aggregation, FP6. - 43 -
  • 44. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentUAB, 2010, Existing scenarios and data compilation on integrated scenarios using demographic, climatic, land cover from global and Black Sea Basin studies, in Universitat Autonoma de Barcelona, E.d., ed.: Geneva, FP7.UN/DESA, 2009, World Population Prospects The 2008 Revision Highlights, ESA/P/WP.210: New York.—, 2010, World Urbanization Prospects The 2009 Revision Highlights, ESA/P/WP/215: New York, UN.—, 2011, World Population Prospects The 2010 Revision Highlights (in press), ESA/P/WP.210: New York.UniGe, 2010, Demographic model inputs and efficient data model with possibilities to be updated, in Université de Genève, E.d., ed.: Geneva, FP7.USGS, 2010, Land Cover Type Yearly L3 Global 500 m SIN Grid, https://lpdaac.usgs.gov/lpdaac/products/modis_products_table. Annexes TerminologyMetronamica is a unique generic forecasting tool for planners to simulate and assess theintegrated effects of their planning measures on urban and regional development. As anintegrated spatial decision support system, Metronamica models socio-economic and physicalplanning aspects, by incorporating a mature land use change model that helps to make theseaspects spatially explicit. More information at: http://www.metronamica.nl/NUTS2: The Nomenclature of Territorial Units for Statistics (NUTS) was introduced by Eurostatmore than 30 years ago in order to provide a single uniform breakdown of territorial units forthe production of regional statistics for the European Union. NUTS 2: basic regions for theapplication of regional policies. More information athttp://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-07-020/EN/KS-RA-07-020-EN.PDFUrban: (UN) Urban/rural is a derived topic based on geographic information obtained fromplace of occurrence and place of usual residence. Because of national differences in thecharacteristics that distinguish urban from rural areas, the distinction between urban and ruralpopulation is not amenable to a single definition applicable to all countries. For this reason, eachcountry should decide which areas are to be classified as urban and which as rural, inaccordance with their own circumstances. For national purposes as well as for internationalcomparability, the most appropriate unit of classification is the size of locality or, if this is notpossible, the smallest administrative division of the country. It must be recognized, however,that a distinction by urban and rural based solely on the size of the population of localities does - 44 -
  • 45. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentnot always offer a satisfactory basis for classification, especially in highly industrializedcountries. Some countries have developed a classification of localities based not on populationsize alone but on “socioeconomic structure of the population”. Others have tried to expressdegrees of urbanization by use of indices of population density etc. Urban (UN, Demographic Yearbook, table 6: http://unstats.un.org/unsd/demographic/products/dyb/dyb2008.htm): Georgia: Cities and urban-type localities, officially designated as such, usually according to the criteria of number of inhabitants and predominance of agricultural, or number of non-agricultural workers and their families. Turkey: Population of settlement places, 20 001 and over. Albania: Towns and other industrial centres of more than 400 inhabitants. Austria: Communes of more than 5 000 inhabitants. Belarus: Cities and urban-type localities, officially designated as such, usually according to the criteria of number of inhabitants and predominance of agricultural, or number of non-agricultural workers and their families. Bulgaria: Towns, that is, localities legally established as urban. Czech Republic: Localities with 2 000 or more inhabitants. Hungary: Budapest and all legally designated towns. Poland: Towns and settlements of urban type, e.g. workers settlements, fishermen’s settlements, health resorts. Republic of Moldova: Cities and urban-type localities, officially designated as such, usually according to the criteria of number of inhabitants and predominance of agricultural, or number of non-agricultural workers and their families. Romania: Cities, municipalities and other towns. Russian Federation: Cities and urban-type localities, officially designated as such, usually according to the criteria of number of inhabitants and predominance of agricultural, or number of non-agricultural workers and their families. Slovakia: 138 cities with 5 000 inhabitants or more. Switzerland: Communes of 10 000 or more inhabitants, including suburbs. Ukraine: Cities and urban-type localities, officially designated as such, usually according to the criteria of number of inhabitants and predominance of agricultural, or number of non-agricultural workers and their families.Urban areas (EEA) urban areas are defined by morphology and the distribution of urban landacross the territory based on CLC classes.Urban land use refers to land that is covered by buildings and other man-made structures, suchas services, industries, and transport infrastructure.Urban population (UN) De facto population living in areas classified as urban according to thecriteria used by each area or country. Data refer to 1 July of the year indicated.Urban agglomeration (UN) Refers to the de facto population contained within the contours of acontiguous territory inhabited at urban density levels without regard to administrativeboundaries. It usually incorporates the population in a city or town plus that in the sub-urban - 45 -
  • 46. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopmentareas lying outside of but being adjacent to the city boundaries. (Geopolis) Urban agglomerationis a continuous built-up area where at least 10.000 inhabitants live. The continuity is defined bya maximum distance of 200 meters between two constructions.(http://egeopolis.eu/spip.php?article196)Urban morphological zone (UMZ) (EEA) An UMZ is an area that includes all the man made areasthat are closer than 200 m to each other.Urban sprawl is commonly used to describe physically expanding urban areas. The EuropeanEnvironment Agency (EEA) has described sprawl as the physical pattern of low-densityexpansion of large urban areas, under market conditions, mainly into the surroundingagricultural areas. Sprawl is the leading edge of urban growth and implies little planning controlof land subdivision. Development is patchy, scattered and strung out, with a tendency fordiscontinuity. It leap-frogs over areas, leaving agricultural enclaves. Sprawling cities are theopposite of compact cities — full of empty spaces that indicate the inefficiencies in developmentand highlight the consequences of uncontrolled growth. - 46 -
  • 47. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Region names and population area regions urban area urban pop urban total pop total pop cnty id region id region name km2 km2 2001 pop 2008 2001 2008 Shkoder AL AL01 1357 17 85384 85121 185618 185046 Tropoje AL AL02 1072 1 7549 4468 27960 16549 Malesi e Madhe AL AL03 1088 - - - 36767 36122 Burgenland (AT) AT AT11 3974 171 11040 11280 276000 282000 Niederösterreich AT AT12 19203 624 400920 416260 1542000 1601000 Wien AT AT13 415 274 1547370 1664190 1563000 1681000 Kärnten AT AT21 9535 111 190400 190400 560000 560000 Steiermark AT AT22 16409 245 331800 337680 1185000 1206000 Oberösterreich AT AT31 11988 307 426250 436790 1375000 1409000 Salzburg AT AT32 7160 91 201240 205920 516000 528000 Tirol AT AT33 12636 106 175240 182780 674000 703000 Vorarlberg AT AT34 2600 55 186030 194510 351000 367000 Bosnia and BA BA01 Herzegovina 26657 215 862382 864226 2330762 2335746 Republika Srpska and BA BA02 Brcko 24557 131 502753 496291 1523495 1503913 Severozapaden BG BG31 19052 328 546960 489190 1032000 923000 Severen tsentralen BG BG32 14803 250 535140 501120 991000 928000 Severoiztochen BG BG33 14647 446 633640 615040 1022000 992000 Yugoiztochen BG BG34 19801 426 734580 708120 1166000 1124000 Yugozapaden BG BG41 20297 644 1593720 1607400 2097000 2115000 Yuzhen tsentralen BG BG42 22360 410 866700 832680 1605000 1542000 Minsk BY BY01 40189 232 877401 833226 1539300 1461800 Brest BY BY02 32649 330 355656 344424 1481900 1435100 Gomel BY BY03 40478 213 1166600 1116136 1535000 1468600 Minsk City BY BY04 87 78 390793 417404 1699100 1814800 Grodno BY BY05 25013 195 246519 232386 1173900 1106600 - 47 -
  • 48. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Mogilev BY BY06 28931 150 504378 474432 1200900 1129600 Vitebsk BY BY07 40369 139 530517 496587 1360300 1273300 Ostschweiz CH CH05 11319 181 261250 270000 1045000 1080000 Praha CZ CZ01 497 286 535900 562580 1165000 1223000 Strední Cechy CZ CZ02 11017 423 663160 717440 1124000 1216000 Jihozápad CZ CZ03 17617 368 752640 768000 1176000 1200000 Severovýchod CZ CZ05 12443 416 1472130 1486980 1487000 1502000 Jihovýchod CZ CZ06 13990 494 907500 911900 1650000 1658000 Strední Morava CZ CZ07 9229 467 826780 826110 1234000 1233000 Moravskoslezsko CZ CZ08 5428 474 748710 737500 1269000 1250000 Stuttgart DE DE11 10557 1161 1224500 1242170 3950000 4007000 Freiburg DE DE13 9403 463 2146000 2196000 2146000 2196000 Tübingen DE DE14 8919 353 637920 650520 1772000 1807000 Oberbayern DE DE21 17344 1204 1645200 1729600 4113000 4324000 Niederbayern DE DE22 10327 330 531450 536850 1181000 1193000 Oberpfalz DE DE23 9691 333 486900 488250 1082000 1085000 Oberfranken DE DE24 7232 337 511980 499560 1113000 1086000 Mittelfranken DE DE25 7248 497 1134980 1147710 1694000 1713000 Schwaben DE DE27 9989 392 1302400 1322380 1760000 1787000 Apkhazeti GE GE01 8671 55 172682 119680 253944 176000 Atchara GE GE02 2894 29 193443 193902 379300 380200 Guria GE GE03 2048 23 79475 76340 144500 138800 Imereti GE GE04 6520 206 291144 283136 519900 505600 Kakheti GE GE05 11338 68 287140 281330 410200 401900 Kvemo Kartli GE GE06 6424 125 331175 316485 509500 486900 Mtskheta-Mtianeti GE GE07 6792 25 52500 44184 125000 105200 Ratcha-Lechkhumi GE GE08 5070 5 29986 27956 51700 48200 Samegrelo-Zemo GE GE09 Svaneti 7547 118 225694 229173 460600 467700 GE GE10 Samtskhe-Javakheti - 48 -
  • 49. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment 6438 15 110611 110081 208700 207700 Shida Kartli GE GE11 5687 51 170640 161082 316000 298300 Tbilisi GE GE12 225 102 674870 704692 1088500 1136600 Sjeverozapadna HR HR01 Hrvatska 8667 203 1045800 1052730 1660000 1671000 Sredisnja i Istocna HR HR02 (Panonska) Hrvatska 23197 262 891000 856020 1350000 1297000 Jadranska Hrvatska HR HR03 24688 202 987390 1012230 1431000 1467000 Közép-Magyarország HU HU10 6916 776 1132000 1164400 2830000 2911000 Közép-Dunántúl HU HU21 11115 453 459610 452640 1121000 1104000 Nyugat-Dunántúl HU HU22 11323 434 431290 429140 1003000 998000 Dél-Dunántúl HU HU23 14168 330 687240 660330 996000 957000 Észak-Magyarország HU HU31 13435 366 754000 713400 1300000 1230000 Észak-Alföld HU HU32 17720 529 1451730 1402440 1561000 1508000 Dél-Alföld HU HU33 18336 612 537030 518700 1377000 1330000 Lombardia IT ITC4 23876 2416 1172470 1260090 9019000 9693000 Provincia Autonoma IT ITD1 Bolzano/Bozen 7410 72 267960 287680 462000 496000 Provincia Autonoma IT ITD2 Trento 6206 115 71400 77550 476000 517000 Veneto IT ITD3 18403 1518 1536460 1652060 4519000 4859000 Friuli-Venezia Giulia IT ITD4 7854 337 - - 1182000 1226000 Liechtenstein LI LI00 160 6 - - 33000 35000 Orhei MD MD01 2568 64 187298 180362 263800 254031 Tighina MD MD02 2490 23 139695 134051 208500 200076 Dubasari MD MD03 4991 265 360431 327972 610900 555884 Chisinau MD MD04 3273 202 1244532 1254807 1121200 1130457 Cahul MD MD05 3087 39 181784 175520 293200 283097 Balti MD MD06 3781 148 308685 305377 474900 469810 Soroca MD MD07 3142 105 174050 164291 348100 328582 Gagauzia MD MD08 1618 34 46293 42292 118700 108442 Edinet MD MD09 3318 94 144092 137105 277100 263663 MD MD10 Lapusna - 49 -
  • 50. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment 3210 36 181090 175369 278600 269798 Ungheni MD MD11 2349 47 138363 134747 271300 264209 Andrijevica ME ME01 346 1 1959 1878 5937 5690 Kolašin ME ME02 884 2 2029 1965 10144 9827 Plužine ME ME03 843 - 1996 1877 4435 4170 Pljevlja ME ME04 1279 8 39771 39828 39771 39828 Mojkovac ME ME05 318 - 5606 5492 10193 9986 Rožaje ME ME06 407 - 7959 7932 22740 22663 Berane ME ME07 689 9 15000 14558 35715 34663 Žabljak ME ME08 444 - 3675 3511 4323 4130 BijeloPolje ME ME09 1003 4 25558 24883 51115 49765 Plav ME ME10 475 1 7508 6748 14722 13232 Šavnik ME ME11 539 - 1474 1378 3071 2870 Nikšic ME ME12 2035 8 33082 33150 75186 75342 Podgorica ME ME13 1584 35 89792 92294 166281 170914 Poranesna jugoslovenska MK MK00 Republika Makedonija 25450 357 1194160 1207730 2024000 2047000 Malopolskie PL PL21 15182 591 1838820 1871310 3226000 3283000 Slaskie PL PL22 12333 1579 808690 790500 4757000 4650000 Lubelskie PL PL31 25123 359 815480 800680 2204000 2164000 Podkarpackie PL PL32 17846 281 1240770 1237820 2103000 2098000 Podlaskie PL PL34 20188 299 762300 750960 1210000 1192000 Dolnoslaskie PL PL51 19947 565 - - 2911000 2878000 Nord-Vest RO RO11 34173 291 283900 272300 2839000 2723000 Centru RO RO12 34111 549 237600 227250 2640000 2525000 Nord-Est RO RO21 36862 680 2493400 2418000 3836000 3720000 Sud-Est RO RO22 35751 1067 1761000 1693200 2935000 2822000 Sud - Muntenia RO RO31 34477 1229 865750 821500 3463000 3286000 Bucuresti - Ilfov RO RO32 1805 382 816840 809280 2269000 2248000 RO RO41 Sud-Vest Oltenia - 50 -
  • 51. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment 29246 580 431460 407520 2397000 2264000 Vest RO RO42 32029 557 934720 885960 2032000 1926000 Juzno-banatski RS RS11 4146 141 44023 42498 314451 303560 Srednje-banatski RS RS20 3249 62 43983 40986 209444 195171 Pomoravski RS RS21 2680 108 50236 47996 228345 218164 Moravicki RS RS22 3092 44 - - 225301 217130 Zajecarski RS RS23 3677 33 - - 138879 126175 Zlatiborski RS RS24 6260 39 - - 314477 299384 Rasinski RS RS27 2707 88 145867 138005 260477 246438 Kolubarski RS RS28 2557 21 - - 193012 182021 Nisavski RS RS29 2724 88 - - 382706 375833 Toplicki RS RS30 2263 9 - - 102786 95842 Pirotski RS RS31 2686 34 - - 106554 97229 Kosovsko-mitrovatski RS RS32 2063 22 - - 278392 260973 Jablanicki RS RS33 2725 43 - - 242176 229568 Kosovski RS RS34 3117 49 - - 678356 635911 Pecki RS RS35 2501 13 334338 313418 417923 391773 Raski RS RS36 3862 68 235420 241919 290642 298665 Prizremski RS RS37 1876 24 220097 206325 379477 355733 Severno-backi RS RS39 1798 29 94103 90905 200220 193415 Severno-banatski RS RS40 2304 44 131809 122708 166847 155326 Zapadno-backi RS RS41 2408 32 94519 87083 214817 197917 Sremski RS RS42 3417 106 120354 118347 334317 328743 Juzno-backi RS RS43 4107 129 312683 321331 589967 606284 Sumadijski RS RS44 2382 63 191873 186119 299802 290811 Pcinjski RS RS47 3465 24 111369 112114 227283 228804 Grad Beograd RS RS49 3198 277 847990 874695 1570352 1619805 Macvanski RS RS50 3232 65 135664 128727 330887 313969 Branicevski RS RS51 3907 91 108665 103852 201231 192318 - 51 -
  • 52. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Podunavski RS RS52 1217 92 80147 77750 210913 204605 Borski RS RS53 3482 32 141645 129036 147547 134412 Kosovsko-pomoravski RS RS54 1400 9 85679 80318 219690 205944 Saratov Region RU RU01 101479 401 565740 542640 2694000 2584000 Kursk Region RU RU02 29796 202 531468 488040 1265400 1162000 Belgorod region RU RU03 27122 218 1218240 1230390 1504000 1519000 Tambov region RU RU04 34452 144 570486 519820 1213800 1106000 Volgograd Region RU RU05 113201 923 1170546 1121870 2722200 2609000 Rostov Region RU RU06 101259 969 2038168 1957300 4430800 4255000 Republic of Kalmykia RU RU07 73776 33 286136 268840 304400 286000 Voronezh Region RU RU08 52108 298 1451760 1368000 2419600 2280000 Krasnodar Territory RU RU09 75177 1315 2101824 2100020 5126400 5122000 Orel region RU RU10 24725 86 411532 386340 875600 822000 Penza Region RU RU12 43386 164 1068912 999360 1484600 1388000 Bryansk region RU RU13 34685 140 633420 589050 1407600 1309000 Tula Region RU RU14 25517 217 1512544 1378080 1718800 1566000 Kaluga Region RU RU15 29402 89 573048 543240 1061200 1006000 Ryazan Region RU RU16 39577 108 654680 605800 1259000 1165000 Smolensk region RU RU17 49773 83 498180 452180 1083000 983000 Lipetsk region RU RU18 24303 153 673420 642950 1224400 1169000 Republic of North RU RU19 Ossetia - Alania 7953 281 299280 301860 696000 702000 Kabardino-Balkar RU RU25 Republic 12388 344 255954 258390 882600 891000 Karachay-Cherkessia RU RU26 14256 221 210720 204960 439000 427000 Stavropol Territory RU RU38 66719 1198 985104 973800 2736400 2705000 Tver region RU RU45 84698 135 714494 648600 1520200 1380000 Republic of Adygea RU RU48 7976 249 174798 171990 448200 441000 Vzhodna Slovenija SI SI01 12215 174 637200 636020 1080000 1078000 Zahodna Slovenija SI SI02 8066 151 401280 414920 912000 943000 SK SK01 Bratislavský kraj - 52 -
  • 53. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment 2045 230 227620 233320 599000 614000 Západné Slovensko SK SK02 14986 694 1178100 1174950 1870000 1865000 Stredné Slovensko SK SK03 16260 342 582220 580500 1354000 1350000 Východné Slovensko SK SK04 15721 402 716220 725880 1557000 1578000 Istanbul TR TR10 5312 875 3977220 4951894 10198000 12697164 Tekirdag TR TR21 18849 193 394690 435684 1361000 1502358 Balikesir TR TR22 23756 194 478640 497571 1544000 1605067 Manisa TR TR33 45352 347 1529000 1457055 3058000 2914110 Bursa TR TR41 29106 436 1223600 1377148 3059000 3442871 Kocaeli TR TR42 20213 583 1040060 1191891 2737000 3136555 Ankara TR TR51 24878 504 931500 1046256 4050000 4548939 Konya TR TR52 48160 330 812130 726004 2461000 2200013 Antalya TR TR61 35937 499 607680 603402 2532000 2514175 Kirikkale TR TR71 31341 163 542400 477697 1695000 1492804 Kayseri TR TR72 59782 276 1000400 919882 2501000 2299704 Zonguldak TR TR81 9539 189 273240 275607 1012000 1020767 Kastamonu TR TR82 26487 159 499380 427639 861000 737308 Samsun TR TR83 38012 504 1497500 1359977 2995000 2719954 Trabzon TR TR90 35083 228 1035210 827438 3137000 2507387 Erzurum TR TRA1 40789 122 513760 403289 1352000 1061287 Agri TR TRA2 29936 108 566440 558882 1156000 1140575 Avtonomna UA UA01 Respublika Krym/M.Si 26040 354 991488 965825 2023444 1971072 Vinnytska UA UA05 26495 549 652652 618720 1763925 1672217 Volynska UA UA07 20163 255 412138 404210 1056764 1036436 Dnipropetrovska UA UA12 31886 1045 2705604 2582782 3560005 3398398 Donetska UA UA14 26511 1673 3906316 3676524 4822612 4538918 Zhytomyrska UA UA18 29838 147 611321 574398 1389365 1305450 Zakarpatska UA UA21 12771 339 326200 323078 1254614 1242606 Zaporizka UA UA23 27295 351 1329633 1264693 1927004 1832888 - 53 -
  • 54. enviroGRIDS – FP7 European projectBuilding Capacity for a Black Sea CatchmentObservation and Assessment supporting SustainableDevelopment Ivano-Frankivska UA UA26 13940 233 435276 428607 1404115 1382603 Kyivska UA UA32 28085 175 829265 799144 1802751 1737269 Kirovohradska UA UA35 24582 147 528926 488678 1125375 1039740 Luhanska UA UA44 26684 917 1778494 1648742 2540705 2355346 Lvivska UA UA46 21843 449 1328232 1305487 2604377 2559779 Mykolaivska UA UA48 23952 160 732118 698065 1262273 1203560 Odeska UA UA51 33323 661 1447949 1412890 2454151 2394728 Poltavska UA UA53 28729 297 826897 777700 1621366 1524902 Rivnenska UA UA56 20047 124 456659 449276 1170920 1151990 Sumska UA UA59 23833 231 725670 670219 1295840 1196820 Ternopilska UA UA61 13823 147 352428 340572 1136866 1098618 Kharkivska UA UA63 31436 583 1881493 1817347 2894604 2795919 Khersonska UA UA65 26398 152 527720 498376 1172710 1107502 Khmelnytska UA UA68 20636 171 628207 594133 1427743 1350303 Cherkaska UA UA71 20935 255 671285 631435 1398510 1315490 Chernivetska UA UA73 8090 301 284478 280403 917671 904527 Chernihivska UA UA74 31899 141 582996 533873 1240417 1135899 Kyiv UA UA80 889 289 2554375 2712798 2580177 2740200 Sevastopol UA UA85 891 116 352005 352935 378500 379500 - 54 -