Impact of the New Cohesion Policy 2014-2020 Workshop DG JRC/DG REGIO Assessment with the Land Use Modelling Platform (LUMP) State of the work Workshop DG JRC/DG REGIO 24 – June - 2013, Ispra, Italy Land Use Modelling Group INTESA Action / Sustainability Assessment Unit / Institute of Environment and Sustainability EC / DG JRC

Part I: Scenario construction AGENDA Part I: Scenario construction Introduction: the reference scenario and the policy scenario(s) The Reference Scenario. Main results achieved. Towards the Policy Scenario(s) Integration with Rhomolo. Preliminary results. The population allocation module. Designing two scenarios of urban development. Integrating the thematic investments in the simulations.

Part II: Ecosystem services and indicators AGENDA Part II: Ecosystem services and indicators Introduction: Ecosystem services Recreational potential and air quality Water quality

Reference and Policy scenarios (in a nutshell) Population projections Agriculture & Forestry Economy (GEM-E3) TEN-T Legislation Reference Scenario (RS0) No implementation of TEN-T Branch: Reference Scenario (RS1) Economy (Rhomolo) Implemen-tation of the TEN-T Implementation of the thematic priorities Policy Scenario Comparison of scenarios at European level Indicators on land use, ecosystem services and urban/rural systems Branch: “BAU urban development” (PS1) Branch: “Compact urban deve-lopment” (PS2)

State of the work: where are we? The “Reference Scenario” is ready; The specific Reference Scenario branch for DG Regio (no implementation of TEN-T) is being generated as we speak; The Policy Scenario is not ready yet, but there is progress on several preparatory steps: The integration between Rhomolo-LUMP has been worked-out; The dynamic population allocation module is working (major innovation in Land Use Modelling); Work on integrating several thematic priorities has progressed. Indicators to assess Ecosystem Services are being developed in parallel.

Coming up next… The Reference Scenario. Main results achieved.

Economic forecast (baseline) Economic forecast (Rhomolo) Integration with Rhomolo The protocol to integrate Rhomolo in LUMP was defined in the few last weeks in interaction with REMO-IPTS-Seville; It was necessary to develop this protocol because Rhomolo is not a forecasting model. Economic forecast (baseline) Real GVA Economic forecast (Rhomolo) Base run (1) (...) (2) / (1) Policy factor Rhomolo’s simulations Simulation run (2)

Economic forecast (baseline) Economic forecast (Rhomolo) Integration with Rhomolo Sectorial GVA forecasts in real terms are then ‘transformed’ into industrial and commercial land use demand using the validated ‘land use intensity approach’: Land Use Intensity approach Economic forecast (baseline) Industrial & Comm. land use demand Land allocation (EUCS100m) Land Use Intensity approach Economic forecast (Rhomolo) Industrial & Comm. land use demand

Integration with Rhomolo While the final simulations from Rhomolo are not ready, we ran some tests on currently available simulations. Baseline: Sectorial GVA growth generated by linear trend of observed growth rates (observations 1995-2008); Policy: ‘TFP’ policy alternative: The main assumption of the EU policy alternative is that the European Structural and Cohesion Funds raise the capacity of the regions for technical progress, catching up with the more advanced ones, taking into account the funding in the areas of research, technological development and innovation.

Approach “M3” (regional-specific land use intensities) Industrial and commercial land use 2006-2020 growth: 23% - 25% Difference between baseline and policy: 1,6%

Approach “M5” (regional and sector-specific land use intensities) Industrial and commercial land use 2006-2020 growth: 11% - 14% Difference between baseline and policy: 2,3%

Policy effect on ICS land use demand

The population allocation module New allocation core of LUMP for population and urban areas; Dynamically allocates inhabitants (given population projections) within each modelling region (NUTS) with a resolution of 100 m; Regional population projections Future urban land use Projected pixel population Threshold rules Indicators Allocation mechanism Potentially refined indicators

Kj,t+1 = ( Populationt+1 - Populationt ) + (Populationt * u) The population allocation module Allocation workflow Regional population projections (Qj,t+1) Regional internal migrations (u); People to allocate in t+1in NUTS2 region j (Kj,t+1): Kj,t+1 = ( Populationt+1 - Populationt ) + (Populationt * u)

The population allocation module Allocation workflow Define the local (pixel i) suitabilities to allocate people (population potential P): Pi,j,t+1 = f ( Accessibility, Neighboring Population, Distance to roads, Slope, Housing Supply ) k

Qi,t+1 = [ Kj,t+1 * ( Pi,j,t+1 / Σj Pi,j,t+1 ) ] + [Qi,t * (1-u)] The population allocation module Allocation workflow Allocate people in pixels: Qi,t+1 = [ Kj,t+1 * ( Pi,j,t+1 / Σj Pi,j,t+1 ) ] + [Qi,t * (1-u)] Pixel’s proportion of total suitability People allocated at pixel level People to allocate in each region People that do not migrate internally from t to t+1

The population allocation module Allocation workflow Iterative procedure to allocate discrete numbers of people and check for spatial restrictions (pixel caps); Force conversions to urban or abandoned urban according to thresholds. Proceed with the spatial allocation of the other land uses. Change in cell’s population density Decreases below T1 = 2 Remains between T1 and T2 Increases beyond T2 = 6 Land use is not urban Remains allocated land use Becomes urban Land use is urban Becomes urban abandoned Remains urban

The population allocation module Direct inputs / parameters Item Notes Notation Base population distribution 2006 Disaggregated from finest geometrical source zones availabe. Qi Demographic projections NUTS2 level. Source: Eurostat Qj Rate of inner regional migratons Arbitrarily defined. Flat parameter for all regions. u

The population allocation module Statistically calibrated suitability factors Item Notes Notation Potential accessibility Dynamic in time. Takes into account the major programmed changes in the network (provided by Transtools) as well as other smaller investments in road infrastructure (investments allocated through a modelling procedure that takes into account modelled traffic flows). -

The population allocation module Statistically calibrated suitability factors Item Notes Notation Neighborhood population Effect of neighboring population. - Distance to roads Based on current network, as given by TeleAtlas. Land uses Capacity of land uses to support the location of new houses / residents. Slope Effect of slope on housing construction.

The population allocation module Other allocation factors Item Notes Notation Housing supply Accounts for the inelastic supply of housing at the scale of the pixel. Housing supply is the same as of 2006, and it is updated every 10 years. Can be used for scenario configuration. S Power parameter It is optimized to fit initial population distribution (commonly ~ 1.5). Reflects the possibility that housing preferences are not linear to suitability. Ultimately can be used for scenario configuration. Strongly affects concentration / dispersal of population. k

The population allocation module Conversion rules Item Notes Notation Conversion to urban Threshold 1 >= 6 inhab T2 Conversion to abandoned urban Threshold 2 <= 2 inhab T1 Spatial restrictions to the allocation of people Item Notes Notation Moderate restriction N2K; CDDA V - VI. Population outside urban areas cannot grow over 6 inhab pixel-wise. - High restriction N2K; CDDA I - IV. Population outside urban areas t1 <= t0.

The population allocation module Policy preferences affecting population allocation (locspecs’ layers) Item Notes Notation Investment in airports Valid for both policy scenarios. Presumably increase attractiveness to residents. - Investment in urban multimodal transport Investment in culture and rural/urban regeneration

Coming up next… Policy alternatives: compact and disperse urban development. Investments in road network.