modelling urbanization processes in europe · 9 what is luisa? (2) linked to several upstream...

1st EC-JRC Urbanization Workshop

Modelling urbanization

processes in Europe

Filipe Batista et al.

Ispra, 27th May 2015

EC-JRC-IES, Sustainability Assessment Unit https://ec.europa.eu/jrc/en/luisa/

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Cities…

Where a plethora of good and bad things happen:

Environmental and social issues: soil imperviousness,

pollution, local climate change, congestion, crime, spread

of diseases...

Socio-economic and environmental advantages due to

scaling: economies of scale (more efficient use of

resources), concentration of critical mass and economic

opportunities, productivity, innovation, economic growth…

Cities…

Bettencourt et al. (2007) PNASOECD (2014)

Regions with big cities experience more economic growth

Higher wages and more

innovation in bigger cities

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Cities in Europe…

Have expanded greatly in the last two centuries due to

economic growth and urbanization.

But what is the future of cities in Europe in a period of

demographic decline? Which cities will win and which will

loose?

Either way, the future of Europe is tied to the future of its

cities…

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LUISA: a holistic approach for land use and activity modelling

How LUISA works

Baseline trajectory of future urbanization

Discussion

Contents

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How LUISA works


Discussion

Contents

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What is LUISA? (1)

Conceived to contribute to Territorial Impact Assessment of EU policies in an

integrated manner;

Co-funded by ENV (initiator in 2007), REGIO, CLIMA;

More than one stand-alone model. It is a platform of inter-linked data,

processes, models and indicators;

Accommodates multi-policy scenarios to represent interacting dimensions of

the EU policies (e.g.: regional and investment policies + CAP + transport +

environment + …), ensuring coherence and consistency of assumptions;

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What is LUISA? (2)

Linked to several upstream models to capture sector dynamics and policies

(emissions, energy, economy, demography, agriculture/forestry, water, …);

A core, state-of-the-art, ‘spatial allocation module’ simulates future land use

changes given a set of scenario specifications, and resolving competition for

land by the different sectors;

Beyond conventional land use/cover modelling: new approach towards

land use functions and activity-based modelling:

Endogenous dynamic allocation of population (implemented), activities, and services (foreseen);

Endogenous modelling of effects of infrastructure improvements and other spatial planning policies.

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Macro and exogenous dynamicsDemography, Economy

(define demand for land)

Local conditionsGeography, neighborhood effects, accessibility, soil

characteristics.(define local suitabilities)

Land allocation

Policies with territorial impact

Current Land Uses;Conversion rules,

transition costs, etc..

Eg. Zoning, spatial planning

regulations

Eg. Policies influencing macro dynamics (usually through scenarios)

What is LUISA? (3)

LUISA merges top-down and bottom-up dynamics:

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Main land use categories in LUISA

Urban areas Industrial/commercial areas

Forested areasAgricultural areas

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What is LUISA? (4)

Main technical characteristics

Originally based on the CLUE model family (EUClueScanner)

Supported by the GeoDMS Data Model Server (GeoDMS)

Spatial extent: EU-28

Spatial resolution: 100 meters

Thematic resolution: 8 fully simulated land use classes (+ agricultural breakdown +

‘abandoned’ land uses)

Temporal resolution / time span: yearly / up to 2050

Base-map: CORINE Land Cover 2006 ‘refined’

Primary outputs: Maps of land use, population, and accessibility

Secondary outputs: Spatially explicit thematic indicators

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How LUISA works


Discussion

Contents

Land requirements from different sectors are

estimated given exogenous macro-drivers:

Economy and Demography.

Computes land function indicators.

Resolves the spatial arrangement of land uses at

fine pixel scale, taking into consideration

competition for land, land suitability, and

neighborhood.

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Demand

module

Allocation

module

Indicator

module

Accessib

ilityNr. of

people

3 tier structure

1521 May 2015

100 m pixels

Biophysical & economic suitability

Regions (NUTS2)

Regional land demands

Allow rules + discrete allocationNeighbourhood

dependencies

How does LUISA work?

1621 May 2015

Allow rules + discrete allocation

AccessibilityNr of people

100 m

Land-use pattern

Outputs from LUISA

at pixel level

1721 May 2015

AccessibilityNr of people

100 m

Land-use pattern

Indicators obtained through combination and/or

aggregation

}50+ indicators

- Land use- Resources- Productivity- Air quality- Ecosystems- Hydrology- Accessibility

100 m

Outputs of LUISA

1921 May 2015

Outputs of LUISA

Population Urban sprawl

Potential accessibility

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Defined as:

The expected land surface required to support future societal

and economic activities.

In LUISA there demand is specified for the ‘active land use classes’:

Urban (residential, touristic, multifunctional built-up)

ICS (industry + commerce + services built-up)

Agriculture (arable + pasture + permanent crops)

Forest

Determined within LUISA exogenously;

The output of LUISA is very sensitive to the ‘demand’ definition.

The concept of ‘demand’ in the LUISA framework

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Population

projection

Nr.

households

Urban land

demand

Parameter:Average

household size

*

*

*

* Subject to scenario / policy option specification

Parameter:Urban

density

Other drivers of urban land (e.g. tourism)

Demand for urban land use

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Definition of urban demand

Urban demand

Residential Touristic

Main driver Nr. of households Nr. of beds

Key parameters

Average household size (persons/household)

assumed to converge to 1.8

Relationship between nr. of arrivals and nr. of beds

statistically derived

Land use intensity (households / ha of urban land)

follows historical trend

Land use intensity(nr. of beds / ha of urban

land use)

Share of touristic buildings nationally

based on survey (avg. 3.5%)

SourcesDemographic projections (ESTAT,

NUTS0)Touristic projections (UNWTO,

Macro-regions)

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‘Industry-commerce’ comprises :

Industrial areas, commercial areas,

public facilities

Future demand for ICS is calculated according

to an approach developed and described by

Batista e Silva et al. (2014), which relies on a

key parameter:

Land Use Intensity

Defined for each sector and for each

region.

Demand for Industry-commerce land use

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The GVA per sector is ‘translated’ into demand for industrial, commercial

and services (ICS) land use, by means of an ”Intensity approach”.

Accounts for gains in land use productivity (w), and changes in production

structure.

Variables:

L = Land use intensity

GVA = Gross Value Added

A = Land area

w = Productivity increase


Indices:

s = sector

t = time

n = number of years

j = number of sectors

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2621 May 2015

2 step allocation workflow – population and land uses

Regional land use demand

External models:Economy

DemographyAgriculture

(…)

Suitability for

population

Population allocation

Overall suitability for n land uses

Discrete allocation of n land uses

Accessibility Proximity to roads Slope Neighborhood Land uses (t-1)

Transport network investments

Spatial planning policies

Population Neighborhood Allow rules Transition costs Physical suitability (terrain, climate…)

Investments Spatial policies

1

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Allocation mechanism - POPULATION

Continuous allocation method;

Dynamically allocates inhabitants (given population projections) within

each modelling region (NUTS) with a resolution of 100 m;

Based on spatial econometric methods (see Anselin 2001), with the

factors: Potential accessibility, Proximity to road, Slope, Land uses;

Estimated at country level for t=0, based on high resolution pop map.

Const Accessibility ln(m. to roads)

Urban Industrial Oth. Arable Perm. crops

Pastures Forests λ

Germany -5,904** 0,791** -0,357** 16,619** -5,312** -4,796** -5,755** -3,169 -6,053** 0,303**

Netherlands -7,099** 0,877** -0,909** 15,344** -14,443** -7,877** -4,252 -7,570** -7,605** 0,435**

Portugal -10,591** 2,092** -0,332** 18,345** 1,106 1,986** 3,659** 6,222** 1,960** 0,323**

2821 May 2015

LUISA Population potentialsRed: highestYellow: mediumGreen: lowest

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Population allocated in year t1 includes population increase + 10%

internal migrants = (Pt1 – Pt0) + (0.1 * Pt0)

Housing supply approximated -> penalizes/incites population changes in

case of shortage/surplus;

People allocated in discrete numbers.

Allocation mechanism - POPULATION

3021 May 2015

2 step allocation workflow – population and land uses

Regional land use demand

External models:Economy

DemographyAgriculture

(…)

Suitability for

population

Population allocation

Overall suitability for n land uses

Discrete allocation of n land uses

Accessibility Proximity to roads Slope Neighborhood Land uses (t-1)

Transport network investments

Spatial planning policies

Population Neighborhood Allow rules Transition costs Physical suitability (terrain, climate…)

Investments Spatial policies

2

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Allocation mechanism – LAND USE

Discrete allocation method;

Based on multinomial logistic regression – addressing competition for

land

The highest bidder takes land (i.e. the land use type with the highest

overall suitability level at each location).

Overall suitability (for each land use in each location) is a combination of

bio-physical suitability, allow rules, transition costs, neighborhood and

land demand.

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How LUISA works


Discussion

Contents

A European baseline scenario

A baseline scenario…

assumes the most likely socio-economic trends (often business-as-usual

approach) and the expected effect of established policies;

serves as benchmark for comparison of policy alternatives, allowing their

ex-ante evaluation.

The “Energy-Climate Reference Scenario 2013” is used as BASELINE:

Macro assumptions (demographic and economic);

Cohesion policy (e.g. investments in infrastructure (TEN-T));

Agriculture (CAP);

Biodiversity and habitat protection;

Renewables Directive; (…)

Sector/Theme Model Current implementation

Agriculture CAPRI RES_2_04XX_PRIMESCOR (driven by PRIMES)

Industry GEM-E3 Update 07/2013 (ECFIN Ageing Report 2012)

Cohesion Policy RHOMOLO MFF (Preliminary allocations)

Residential areas EUROPOP EUROPOP 2010

Forestry UNFCC Historical Data

Tourism UNWTO

Transport TEN-T TRANSTOOL Approved network upgrades

Climate Change Various ENSEMBLE (A1B, E1)

Crop suitability BIOMA AVEMAC

Thematic Indicators ESTIMAP, CBM, LISFLOOD/QUAL, .. Ecosystem Services, Forest, Water ..

The ‘reference’ scenario in LUISA

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On aggregate… further losses of land use intensity

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1990-2010

2010-2050

But… large spectrum of behaviors among NUTS3 regions…

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LAND TAKE / YEAR LAND TAKE / CAPITA / YEAR BUILT-UP PER MN INHABITANTS

Focus on Large Urban Zones…

Population density - 2010

Population density - 2050

Expansion

2010 2050

Stagnation / de-growth

2010 2050

Strong concentration

2010 20502050

Absolute changes (population)

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RC= Vd x Conc x GA

Pollutant concentration maps (current & future) Pollutant removal capacity

Focus on environmental/quality of life indicators (e.g. Air Quality)

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Urban population exposed to pollutant concentration exceeding the daily

limit value for more than 35 days in a year.

Focus on environmental/quality of life indicators (e.g. Air Quality)

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How LUISA works


Discussion

Contents

Up-date of reference Scenario (Dec 2015 – depending on upstream models runs)

Pan-European coverage (2015-16 – gradual extension)

Update of base year.

More economic rationale in LUISA:

spatial allocation with net present value considerations

modelling of services and employment distribution

economic-demographic feedbacks

exploit regional opportunities -> attract investments/create jobs

Urban efficiency indicators (land use, and energy/transport efficiency);

Validation activities and cross model comparison exercises.

On-going and planned activities / improvements

Demography Economy

Land demand

Allocation mechanism

Population

LU

IS

AEX

OG

.

LU Accessibility

Internalmigrations

Land use functions

Demography Economy

t+1

t

t+1

Economic gradients influence migration flows between regions.

> Urban patterns influence regional economic growth;

> ESS and natural assets can foster regional opportunities;

> Different regional growths vs. regional Profiles.

Regionalization

Under development

Implemented

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Lavalle C, et al. 2011. A High Resolution Land use/cover Modelling Framework for

Europe: introducing the EU-ClueScanner100 model. In Computational Science and

Its Applications - ICCSA 2011, Part I, Lecture Notes in Computer Science vol. 6782,

edited by B. Murgante et al. Apduhan. Berlin: Springer-Verlag.

Batista e Silva F, et al. 2014. Estimating demand for industrial and commercial

land use given economic forecasts. PLOS ONE 9 (3):e91991.

Jacobs-Crisioni C, et al. 2014. Accessibility and territorial cohesion in a case of

transport infrastructure improvements with endogenous population

distributions. Forthcoming.

Lavalle C, et al. et al. Land use and scenario modelling for Integrated

Sustainability Assessment. Forthcoming.

More at: http://sa.jrc.ec.europa.eu/?page_id=763

Some references

http://sa.jrc.ec.europa.eu/?page_id=763

Thank you

modelling urbanization processes in europe · 9 what is luisa? (2) linked to several upstream...

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