ET2050 European Territorial Scenarios modelled by SASI Klaus Spiekermann and Michael Wegener ET2050 Project Group Meeting Barcelona, September 2013

2 The SASI Model

3 The SASI model There are three methods to model the impacts of policies on regional economic development: Multiplier effects of infrastructure invest­ ments (Aschauer, 1993) Regional production functions incorporating infrastructure as production factor (Jochimsen, 1966; Biehl, 1986, 1991) Interregional trade flows as a function of interregional transport costs (Peschel, 1981; Bröcker, 1995) and input­output linkages (Echenique, 1990) and economies of scale (Krugman, Venables,1995) SASI

4 The SASI model The SASI model is a recursive­-dynamic simulation model of socio­economic development of regions in Europe under assumptions about European economic development and external net migration, European transport policies (TEN-T), regional subsidies (ERDF, EAFRD, ESF, CF). The SASI model differs from other regional economic models by modelling not only production (the demand side of regional labour markets) but also population (the supply side of labour markets) and travel and freight transport flows.

5 Regional production function In state-­of-­the-­art models of regional development based on production functions the classic production factors land, labour and capital are replaced by location factors, such as: Economic structure Productivity Accessibility Labour supply Services Settlement structure Research and development Education Quality of life

6 Regional production function Extended production function: where A i is potential accessibility: Production AccessibilityLand LabourCapital Others Travel cost between regions i and j Destinations in region j Accessibility of region i

7 Regional net migration function Net migration function: where q i (t–3) is GDP per capita of region i q(t-3) is average European GDP per capita v i (t–3) is quality of life of region i v(t-3) is average European quality of life … all lagged by three years Attractiveness as place to work Attractiveness as place to live Net migration of region i in year t

8 GDPAccessibility Production function Employment Migration function Population Income Labour force Unemploy­ ment SASI model Transfer policies Transport policies

9 SASI model Regions

10 The SASI Model in ET2050

11 Model developments for ET2050 Conversion of the region system to the 2006 NUTS­3 classification Extension of forecasting horizon from 2030 to 2050 Extension of study area to the ESPON Space (EU27+4) Development of a simple model of regional and long-­distance travel and freight transport Calculation of environmental indicators as energy consumption and CO 2 emissions of transport

12 Model integration in ET2050 Adjustment of exogenous assumptions to those of MASST and MULTIPOLES. Translation of the NUTS-2 typology of regions pro-posed in the MASST to NUTS-3 regions. Adoption of the exploratory scenarios A, B and C defined for MASST and MULTIPOLES. Provision of NUTS-3 model results for the three exploratory scenarios to METRONAMICA.

13 Questions answered How will different spatial orientations of European infrastructure investments (TEN­-T) regional subsidies (ERDF, EAFRD, ESF, CF) affect regional economic development, regional population/migration, interregional travel and freight flows, territorial cohesion and polycentricity, energy consumption/CO 2 emissions?

14 Baseline Scenario

15 Baseline Scenario The Baseline Scenario for 2030 and 2050 is based on BAU assumptions about European economic development, European net migration, European regional subsidies, European transport policies. and produces forecasts of: regional economic development, regional population/migration, interregional travel and goods flows, energy consumption/CO 2 emissions, territorial cohesion and polycentricity.

16 Baseline Scenario Population maximum in 2035: Baseline Scenario assumption: population

17 Baseline Scenario Immigration constrained: Baseline Scenario assumption: EU27 net migration per year (1,000)

18 Baseline Scenario "Sluggish recovery": Baseline Scenario assumption: GDP (Euro of 2010)

19 Baseline Scenario Subsidies grow with EU GDP: Baseline Scenario assumption: Structural Funds (billion Euro of 2010)

20 Baseline Scenario Subsidies mainly to poorer regions: Baseline Scenario assumption: Structural Funds as % of GDP v. GDP per capita

21 Baseline Scenario Rising energy prices: Baseline Scenario assumption: Oil price per barrel (Euro of 2010)

22 Baseline Scenario Growing transport energy efficiency: Baseline Scenario assumption: MJ per pkm/tkm

23 Baseline Scenario Cleaner vehicles and more renewable energy: Baseline scenario assumption: CO 2 emission of transport per MJ

24 Exploratory Scenarios

25 Exploratory Scenarios The definition of the SASI exploratory scenarios is based on the same region typology as used by the MASST and MULTIPOLES models but translated into NUTS-3 regions: In the MEGAs Scenario A large European metro- politan areas are promoted in the interest of com- petitiveness and economic growth. In the Cities Scenario B major European cities are promoted in order to strengthen the balanced poly- centric spatial structure of the European territory. In the Regions Scenario C rural and peripheral regions are promoted to advance territorial cohesion between affluent and economically lagging regions.

26 Exploratory Scenarios In the SASI exploratory scenarios A, B and C the assumptions about total European development and European net migration remain the same as in the Baseline Scenario. However, the exploratory scenarios differ in their assumptions about the allocation of EU Structural Funds subsidies (see next slide), European transport policies (see three following slides).

27 Baseline Scenario The A (MEGAs) B (Cities) C (Regions) 1.0 % % of total EU Structural Funds Exploratory scenarios: Structural Funds

28 Scenario A: Network improvements (if necessary) MEGA Connections between MEGAs not more than 500 km apart. Minimum speed: Road: 90 km/h Rail: 200 km/h

29 Baseline Scenario City Connections between cities not more than 300 km apart. Minimum speed: Road: 80 km/h Rail: 160 km/h Scenario B: Network improvements (if necessary)

30 Baseline Scenario Region Connections between regions not more than 200 km apart. Minimum speed: Road: 65 km/h Rail: 80 km/h Scenario C: Network improvements (if necessary)

31 Exploratory scenario results

32 Baseline Scenario: Accessibility travel road/rail 1981 Baseline Scenario: Accessibility travel road/rail 1986 Baseline Scenario: Accessibility travel road/rail 1991 Baseline Scenario: Accessibility travel road/rail 1996 Baseline Scenario: Accessibility travel road/rail 2001 Baseline Scenario: Accessibility travel road/rail 2006 Baseline Scenario: Accessibility travel road/rail 2011 Baseline Scenario: Accessibility travel road/rail 2016 Baseline Scenario: Accessibility travel road/rail 2021 Baseline Scenario: Accessibility travel road/rail 2026 Baseline Scenario: Accessibility travel road/rail 2031 Baseline Scenario: Accessibility travel road/rail 2036 Baseline Scenario: Accessibility travel road/rail 2041 Baseline Scenario: Accessibility travel road/rail 2046 Baseline Scenario: Accessibility travel road/rail 2051

33 GDÜ per capita (1000 € of 2010) Baseline Scenario: GDP per capita 2051

34 Difference to Baseline Scenario (%) 2051 Scenario A: GDP per capita Difference to Baseline Scenario 2051

35 Difference to Baseline Scenario (%) 2051 Scenario B: GDP per capita Difference to Baseline Scenario 2051

36 Difference to Baseline Scenario (%) 2051 Scenario C: GDP per capita Difference to Baseline Scenario 2051

37 Results (2) Population density (pop/sqkm) Baseline Scenario: Population density 2051

38 Difference to Baseline Scenario (%) 2051 Scenario A: Population Difference to Baseline Scenario 2051

39 Difference to Baseline Scenario (%) 2051 Scenario B: Population Difference to Baseline Scenario 2051

40 Difference to Baseline Scenario (%) 2051 Scenario C: Population Difference to Baseline Scenario 2051

41 CO2 emission (t/capita/year) Baseline Scenario: CO 2 emission 2051

42 Scenario variants

43 Scenario variants In addition, the Baseline Scenario and the exploratory scenarios A, B and C are combined with alternative framework conditions: 1.Economic decline. Globalisation and growth of emerging economies will lead to stagnation and almost decline of the European economy 2.Technology advance. New innovations in pro- duction and transport technology will result in significant growth in labour and transport productivity. 3.Energy/climate. Rising energy costs and/or greenhouse gas emission taxes will lead to strong increases of production and transport costs.

44 Scenario variants The combination of the exploratoy scenarios and the variants leads to nine additional scenarios:

45 Scenario variants Economic decline It is assumed that in Scenarios A1, B1 and C1 total GDP of EU27+4 will grow by only 0.62 per cent p.a. on average between 2011 and 2051 compared to 1.50 per cent in the Baseline. As in the Baseline Scenario, it is assumed that growth rates will gradually decrease after 2030.

46 Scenario variants Technology advance It is assumed that in Scenarios A2, B2 and C2 labour productivity, i.e. GDP per worker, will grow by 1.94 per cent p.a. on average between 2013 and 2051 compared to 0.94 per cent in the Baseline Scenario. It is assumed that productivity will gradually con- verge between countries towards It is assumed that energy efficiency of transport will increase by 0.75 % per cent p.a. compared to 0.45 % per cent in the other scenarios.

47 Scenario variants Energy/climate It assumed that in Scenarios A3, B3 and C3 fuel costs of road vehicles will increase by 5 % per cent on average between 2013 and 2051 compared to 1.5 per cent in the Baseline Scenario. This will result in an average fuel price of Euro of 2010 in 2051 compared with 3.00 Euro in the Baseline Scenario. Energy cost of rail transport is assumed to increase by 2 per cent p.a. between 2013 and 2051.

48 Scenario variants Scenario variant assumtions: summary 1 withour generative effects

49 Scenario variants results

50 Scenario comparison Transport networks & travel cost: Accessibility of travel by road/rail

51 Scenario comparison Remaining East-West gap: GDP per capita EU15/EU12 (1,000 Euro of 2010)

52 Scenario comparison Declining overall regional disparities: Gini coefficient of GDP per capita

53 Scenario comparison Persistent urban structures in EU15 National polycentricity index

54 Scenario comparison Dynamic urban structures in EU12: National polycentricity index

55 Scenario comparison Increase in energy efficiency v. growth in volume: Energy consumption by transport per capita p.a. (MJ)

56 Scenario comparison Increase in energy efficiency & share of renewable energy: CO 2 emission by transport per capita p.a. (t)

57 Conclusions

58 Summary comparison

59 Conclusions The comparison of scenarios with respect to the three major EU goals gives a straightforward result: Competitiveness: The A scenarios (MEGAs) produce the largest growth in GDP. The C scenarios (Regions) perform worst in terms of overall economic growth. The B scenarios (Cities) lie in between. Cohesion: The C scenarios perform best in terms of cohesion and polycentricity. The A scenarios slow the convergence down. The B scenarios lie in between. Sustainability: The B scenarios are most successful environmentally. The A and C scenarios use more energy and emit more CO 2 for transport.

60 Conclusions The results of the scenario simulations with the SASI model can be summarised as follows: Promotion of metropolitan areas will maximise economic growth but increase spatial disparities and environmental damage. Promotion of rural and peripheral regions will increase spatial cohesion but reduce economic growth and sustainability. Promotion of large and medium-sized cities is a rational trade-off between competitiveness and cohesion and will be best for the environment.

61 Conclusions These results validate the balanced polycentric spatial organisation of Europe as suggested by the European Spatial Development Perspective (ESDP) and the Territorial Agenda (TA). The B scenarios (Cities) should therefore be taken as the point of departure for the territorial vision.

62 Next steps A modelling exercise is permanent work in progress. Therefor the SASI team is looking forward to receiving critique and suggestions for improvement. There are several possibilities for complementing and improving the SASI model: combining assumptions of scenario variants? more comprehensive energy/climate variant? also publish results for the Western Balkan? any other?

63 Meta Analysis in a final step the models used in ET2050 will be com- pared in a meta analysis. A meta analysis is a way to cross-validate the results of different models by systematically comparing their results to identify differences between them and, if they differ, explore the reasons why. To overcome the differences in spatial resolution and time horizon between the models, not absolute values but differences between the exploratory scenarios and the Baseline Scenario will compared.

64 Wegener, M., Bökemann, D. (1998): SASI Model: Model Structure. Berichte aus dem Institut für Raumplanung 40. Dortmund: Institute of Spatial Planning, University of Dortmund. fileadmin/irpud/content/documents/publications/ber40.pdf. Wegener, M. (2008): SASI Model Description. Working Paper 08/01. Dortmund: Spiekermann & Wegener Stadt­ und Regionalforschung. de/mod/pdf/AP_0801.pdf. Spiekermann, K. Wegener, M. (2013): The SASI Sce- narios until Project Report for the ESPON-Projekt ET2050 (Territorial Scenarios and Visions for Europe). More information