University of Aveiro

UNIVERSITY OF AVEIROUNIVERSITY OF AVEIRO

Final Meeting and Project Review 23/24 June 2003 Gdansk

University of AveiroUniversity of Aveiro

City Case OverviewCity Case Overview

Department of Environment and PlanningDepartment of Environment and Planning

University of AveiroUniversity of Aveiro (Portugal) (Portugal)

Ana Isabel Miranda and Carlos Borrego



An introduction ...An introduction ...

The cities involved in SUTRA differ widely in terms of culture, environmental conditions , size, economic structure, social composition and demography.

But ... they face common challenges in their transportation systems:

Air quality

Employment

Traffic congestion

Noise

....

Economic competitiveness

€



An introduction ...An introduction ...

A common methodology and a set of tools were used by SUTRA cities to generate directly

comparable results for the overall evaluation.

Indicators

Scenarios

Models



TOPICS TO BE COVEREDTOPICS TO BE COVERED

City case description

Models cascade application

- baseline

- common scenarios

Final comments


Main conclusions

Models cascade application:

Baseline

Common scenarios




•location, urban structure and land-use

• demography

• meteo conditions and air pollution

• input indicators

Main conclusions


Baseline

Common scenarios

The SUTRA citiesThe SUTRA cities

Lisbon

Geneva

Gdansk

Thessaloniki

Genoa

TelAviv



Meteorological conditions and air pollution problemsMeteorological conditions and air pollution problems

GENOA mild Mediterranean climate topographic and orographic peculiarity: sea and mountains affect pollutants dispersion

LISBON complex sea breeze circulations

traffic NOx emissions represents 97% of total anthropogenic NOx emissions.

THESSALONIKI 45% of days characterized by stagnant conditions high insolation



• demography



Main conclusions


Baseline

Common scenarios



Demographic indicatorsDemographic indicators

Demographic changes are crucial to determine traffic demand

Population size

0

1000000

2000000

3000000

4000000

5000000

GDANSK GENEVA GENOA LISBON TEL AVIV THESSALONIKI

VISUM OFIS

Population size for VISUM and OFIS domains

Population size evolution

Age structure

0

200000

400000

600000

800000

1000000

1980 1985 1990 1995 2000

year

inh

abit

. Genoa

Lisbon

Gdansk

Geneva

0%

10%

20%

30%

40%

50%

60%

70%

Lisbon Genoa Gdansk Thessaloniki

0-14

15-64

+64



• demography



Main conclusions


Baseline

Common scenarios



Economic indicatorsEconomic indicators

0

10000

20000

30000

40000


GD

P (E

uros

)

VISUM OFIS NATIONAL

GDP per capita

0

20

40

60

80

100


% e

mpl

oy. i

n se

rvic

es o

ver

tota

l em

ploy

.

VISUM OFIS NATIONAL

% of employment in services over total employment



• demography



Main conclusions


Baseline

Common scenarios



Technological indicatorsTechnological indicators

passenger car peak occupancy rate

0%

10%

20%

30%

40%

50%

60%

GDANSK GENOA LISBON TEL AVIV THESSALONIKIpass

enge

rs /

pass

enge

r ca

r (p

km /

vkm

)

% of public transport over total passenger transport



• demography



Main conclusions


Baseline

Common scenarios



Which scenarios?Which scenarios?A baseline scenario (specific of each city)Common scenarios of development

Which models?Which models?

VISUMVISUM

TREMTREMVADISVADIS

OFISOFISMARKALMARKAL


Main conclusions


Baseline

Common scenarios VISUM

TREM

VADIS

OFIS

MARKAL



Common scenarios ...definitionCommon scenarios ...definition


Main conclusions


Baseline

Common scenarios VISUM

TREM

VADIS

OFIS

MARKAL

Land use

Young and virtuousYoung and viciousOld and virtuous

Old and vicious

Demography

Economic srtructure

Technology

€ € €€ € €



VISUM VISUM transportation modeltransportation model ....domain..domain AREA: 241 km2

POPULATION: 635 201

N. NODES: 936

N. LINKS: 888GENOA

AREA: 2793 km2

POPULATION: 2 682 676

N. NODES: 1124

N. LINKS: 2940

LISBON

AREA: 1447 km2

POPULATION: 2 611 500

N. NODES: 3144

N. LINKS: 11850

TEL AVIV

THESSALONIKI

AREA: 1100 km2

POPULATION: 894 435

N. NODES: 1386

N. LINKS: 2034

GENEVA

AREA: 282 km2

POPULATION: 413 585

N. NODES: 936

N. LINKS: 2900

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

-GENOA

-LISBON

-GENEVA



VISUM VISUM transportation model transportation model ...input data...input data

48.0%

13.0%

22.0%

17.0%

0% 10% 20% 30% 40% 50%

Percentage

Work

School

Business

Others

Motive

GENOA

12.1%

13.9%18.6%

41.9%

8.2%

4.1%

1.1%

0% 10% 20% 30% 40% 50%

Percentage

Leasure

Shopping/Services/Health

School

Work

Visiting relatives

On Service

Others

Motive

LISBON

Trips by purposes

OD matrixes

road category and capacity

maximum velocity allowed

+

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



VISUM VISUM transportation model .transportation model ...results..results

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

Results example: Lisbon public network



0.0E+00

2.0E+09

4.0E+09

6.0E+09

8.0E+09

1.0E+10

GDANSK GENOA LISBON THESSALONIKI

pk

m p

er

ye

ar

PRIVATE

PUBLIC

Pressure indicator: passenger transport demand (pkm/year)

VISUM VISUM transportation model transportation model ...indicators...indicators

0

100000

200000

300000

400000

GENOA LISBON

ho

urs

pe

r y

ea

r Crowding

Traffic jams

State indicator: time spent in crowding and traffic jams (hours)

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



Population data 2000total 635.201

Age groups 0-17 65+sector 3/office sector 3/tele sector 2 unemployed

population shares 13% 44% 0% 11% 7% 25%mobility rates 3,4 2,8 2,5 2,7 3 1,8

71% 0% 18% 11% 62Car occupancy rate 1,2

Transport means Ped/Bic Pub PriMode shares 29% 29% 42%

Land Use : distance changesSensitivity Factor [a] [10 ... 0 ... -1] -0,70 4,00 -0,70 4,00Form Factor [b] [0.1 ... 1 ... 4] 0,10 0,10 0,10 0,10

18-64

Ex. Genoa input data from the common scenario implementation

VISUMVISUM transportation model transportation model ...methodology...methodology

A spreadsheet (CommonScenarios.xls) was developed to modify the O/D matrixes according to the scenarios.

The obtained results reflect the characteristics of each traffic network.

Ex. Lisbon output data from the common scenario spreadsheet

OutputIndicator Analysis 1 2 3 4Population 535740 837405 837405 396287 396287Average Trip Rate 1.80 1.71 1.71 1.64 1.64PuT Share 0.32 0.47 0.32 0.47 0.32PrT Share 0.43 0.28 0.43 0.28 0.43Car Occupancy Rate 1.400 1.470 1.386 1.470 1.386PuT Matrix Sum 1308923 2850171 1940542 1296477 882708PrT Matrix Sum 2075924 1908625 3108745 868190 1414097Average Distance PuT 9.30 7.80 10.84 7.80 10.84Average Distance PrT 8.15 6.52 9.78 6.52 9.78Distance Change PuT -16.1% 16.6% -16.1% 16.6%Distance Change PrT -20.0% 20.0% -20.0% 20.0%

Young and

Virtuous

Old and Vicious

Young and

Vicious

Old and Virtuous

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



VISUM VISUM transportation transportation modelmodel ...results and analysis ...results and analysis

CS1 –Young and virtuous

CS2 – Young and vicious

CS3 – Old and virtuous CS4 – Old and vicious

Ex. Lisbon private network

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



VISUM VISUM transportation transportation modelmodel ...results and analysis ...results and analysis

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

-

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4

PrT Volume 24h

PrT Vehc*km

Ex. Comparison of scenarios results for private transport in Geneva



TREM TREM emissions emissions modelmodel ...domain and input data ...domain and input data

For every city-case TREM domain coincides with VISUM domain.

Main inputs required by TREM: traffic volume and vehicle speed (provided by VISUM) distribution of vehicles by categories

0%

20%

40%

60%

80%

100%

Passenger cars Light dutyvehicles

Heavy dutyvehicles

Buses Motorcycles

GDANSK

GENEVA

LISBON

TEL AVIV

THESSALONIKI

GENOVA

distribution of vehicles by classes

0%

10%

20%

30%

40%

Passengercars

Light dutyvehicles

Heavy dutyvehicles

Buses Motorcycles

<1 1-2 2-3 3-4 4-5 5-10 10-15 15-20 >20

ex. Gdansk

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



0

1000

2000

3000

4000

5000

6000

7000

0 2 4 6 8 10 12 14 16 18 20 22 24

Time (h)

CO

(g

.km

-1)

Ouro street Prata street

Hourly variation of CO emissions for Lisbon (Prata and Ouro streets)

CO emission for Genoa domain

TREM TREM emissions modelemissions model ...results ...results

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



TREM TREM emissions model ...indicatorsemissions model ...indicators

0

500000

1000000

1500000

2000000

GDANSK GENOVA LISBON THESSALONIKI

(t/y

ear)

CO2

0

2000

4000

6000

8000


(t/y

ear)

NOx

0

2000

4000

6000

8000


(t/y

ear)

VOC

0

10000

20000

30000

40000

50000


(t/y

ear)

CO

0

100

200

300

GENOVA LISBON THESSALONIKI

(t/y

ear)

PM10

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



TREM TREM emissions emissions modelmodel ... ...scenarios scenarios input datainput data

Main inputs in TREM scenario application:

technological indicators new technologies penetration rates vehicle fleet changes fuel properties

VISUM outputs vehicle volume vehicle speed

0

10

20

30

40

k 10 20 30 40 50 60 70 85 96

vehicle classes

%

reference

scenario 1,3

Passenger cars LDV HDV Bus MotoNew

technologies

Ex. Fleet composition for different scenarios

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



TREM TREM emissions emissions modelmodel ...results and analysis ...results and analysis

Ex. Genoa CO emissions CS1 –Young and

virtuous


CS3 – Old and virtuous

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

CS4 – Old and vicious



TREM TREM emissions emissions modelmodel ...results and analysis ...results and analysis

CO2 emissions from Road Transport

283875

206490

81144

437406

187302

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

500000

Baseline CS1 CS2 CS3 CS4

ton

s p

er

ye

ar

NOx emissions from Road Transport

2415

27090

585

213

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750


ton

s p

er

ye

ar

VOC emissions from Road Transport

3116

1249

705

1921

1217

0

500

1000

1500

2000

2500

3000

3500


ton

s p

er

ye

ar

PM10 emissions from Road Transport

52

17

7

39

16

0

10

20

30

40

50

60

70

80


ton

s p

er

ye

ar

Ex. Thessaloniki results

Only CO2 emissions for Scenario 2 represent values above the reference situation. All other pollutants are expected to decrease primary due to emission reduction technologies and low sulphur levels in gasoline and diesel.

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

VADIS VADIS local scale model ...domainlocal scale model ...domain

2 0 0 3 - 0 2 - 1 1 M a n a g e m e n t B o a r d M e e t i n g , O c t o b e r 1 4 / 1 5 , G u m p o l d s k i r c h e n 1 3

lo c a l c o o r d in a te s s y s te m "G d a n s k 7 0 "

G e n . H a lle r d o m a in X YA 2 8 1 9 5 , 5 7 3 5 7 8 4 , 6 2B 2 8 3 5 7 , 4 7 3 5 8 2 7 , 9 9C 2 8 4 1 8 , 9 6 3 5 5 9 8 , 3 7D 2 8 2 5 7 , 0 6 3 5 5 5 5 , 0 0

P o w s t a ń c ó w W a r s z a w y X YA 2 9 1 4 7 , 9 0 3 2 6 5 3 , 2 0B 2 9 2 7 3 , 2 7 3 2 8 0 6 , 2 7C 2 9 4 5 5 , 4 1 3 2 6 5 3 , 4 9D 2 9 3 2 8 , 0 9 3 2 5 0 1 , 7 0

G D A N S K D I M E N S I O N : 2 4 0 x 2 4 0 x 6 0 ( m )

G R I D R E S . : 5 x 5 x 5 ( m )

N . B U I L D I N G S : 4

N . S O U R C E S : 2

GENEVA DIMENSION: 900 x 500 (m)

GRID RES.:

N. BUILDINGS:

N. SOURCES:

GENOVA DIMENSION: 250 x 250 (m)

GRID RES.: 5 x 5 (m)

N. BUILDINGS: 18

N. SOURCES:

Prata street

Ouro street

Betesga street

LISBON DIMENSION: 450 x 450 x 100 (m)


N. BUILDINGS: 26

N. SOURCES: 3

TEL AVIV DIMENSION: 350 x 350 x 60 (m)

GRID RES.: 5 x 5 x 3 (m)

N. BUILDINGS: 6

N. SOURCES: 1

THESSALONIKI DIMENSION: 300 x 300 (m)


N. BUILDINGS: 7

N. SOURCES: 7



VADIS VADIS local scale modellocal scale model ...input data ...input data

Buildings volumetry

Geneva Genoa Lisbon

0

1

2

3

4

5

6

0 2 4 6 8 10 12 14 16 18 20 22 24

Time (h)

Vel

oci

ty (

m.s

-1)

0

90

180

270

360

Wind velocity Wind direction

N

S

E

W

S

Wind velocity and direction for Lisbon simulation

Meteorological data

Emission data (provided by TREM)

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

5 0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

8 0 0

9 0 0

1 1 0 0

1 2 0 0

Wind and CO dispersion fields for Lisbon for 6p.m. of

8th of July 1997

0 50 100 150 200 250 300

W est / East (m )

0

50

100

150

200

250

300

So

uth

/No

rth

(m

)

250

500

1250

2500

5000

10000

20000

30000

40000

CO[µg.m-3]

Egnatia Str.

Ven

izel

ou

Str

.

Dra

go

um

i S

tr.

Ioustinianou Str.

Solom ou Str.

Klisouras Str.

Rem

pel

ou

Str

.

Wind and CO dispersion simulation for Thessaloniki

for 7 a.m. of 9 September 1998

VADIS VADIS local scale model local scale model ...results ...results

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



VADIS VADIS local scale local scale modelmodel ...results and analysis ...results and analysis

Ex. Lisbon CO dispersion fields CS1 –Young and

virtuousCS2 – Young and vicious


0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

5 0

1 5 0

3 0 0

5 0 0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

7 0 0 0

CO(µg.m -3)

Ou

ro S

tre

et

Pra

ta S

tre

et

Betesga Street

0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

5 0

1 5 0

3 0 0

5 0 0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

7 0 0 0

CO(µg.m -3)

Ou

ro S

tre

et

Pra

ta S

tre

et

Betesga Street

0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

5 0

1 5 0

3 0 0

5 0 0

1 0 0 0

2 0 0 0

3 0 0 0

4 0 0 0

7 0 0 0

C O(µg.m -3)

Ou

ro S

tre

et

Pra

ta S

tre

et

Betesga Street

0 50 100 150 200 250 300 350 400 450

W est/East (m )

0

50

100

150

200

250

300

350

400

450

So

uth

/No

rth

(m

)

1

50

150

300

500

1000

2000

3000

4000

7000

C O(µg.m -3)

Ou

ro S

tre

et

Pra

ta S

tre

et

Betesga Street

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



VADIS VADIS local local scale scale ..results and ..results and analysisanalysis

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKALEuropean Legislation CO CO - 10000 µg.m-3 (8-hours average limit value)

CO

0

10000

20000

30000

40000

50000

Referencesituation

Scenario 1 Scenario 2 Scenario 3 Scenario 4

CO

Co

nce

ntr

atio

n (

µg.m

-3)

Ex. Lisbon results



VADISVADIS local scale local scale modelmodel ..results and analysis ..results and analysis

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

European Legislation NONO22 - 200 µg.m-3 (not exceeding more than 18 times in a year) Hourly value for the protection of human health

Ex. Lisbon results

NOx

0

500

1000

1500

2000

2500

3000

Referencesituation


NO

x C

on

cen

trat

ion

(µg

.m-3)



VADIS VADIS local scale local scale modelmodel ..results and analysis ..results and analysis

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKALEuropean Legislation

PM10PM10 - 50 µg.m-3 (not exceeding more than 35 times in a year) Daily maximum value for the protection of human health

Ex. Lisbon results

PM10

0

50

100

150

200

Referencesituation


PM

10 C

on

cen

trat

ion

(µg

.m-3)



OFIS OFIS photochemical modelphotochemical model ...domain ...domain

AREA: 150 km x 150 km

GDANSK

GENEVA

GENOA

OFISdomain

THESSALONIKI

TEL AVIV

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



OFIS OFIS photochemical model photochemical model . ...input data..input data

Main inputs required by OFIS:

emissions: hourly non-urban , suburban and urban emissions rates

meteorological data: daily average wind speed and direction, temperature and temperature lapse rate above the mixing layer

boundary conditions: daily average regional background concentrations (NO, NO2, O3 and other species)

Wind direct ion frequenciesGeneva / Cointrin station

01.04.96 - 30.09.96

0

10

20

30

40

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



Number of days with maximum 8 hour running average ozone concentration exceeding 120 g.m-3 (IND120).

OFIS OFIS photochemical modelphotochemical model ...results ...results

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



0

40

80

120

160

200

GDANSK GENEVA GENOA THESSALONIKI

AOT60(max)

AOT60(ave)

AOT60 (maximum and average)

0

30

60

90

120

GDANSK GENEVA GENOA THESSALONIKI

AOT60(sub)

AOT60(tow n)

AOT60 (suburbs and town),

OFIS OFIS photochemical modelphotochemical model ...indicators ...indicators

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



OFIS OFIS photochemical photochemical modelmodel ...results and analysis ...results and analysis

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL


CS2 –Young and vicious

CS3 –Old and virtuous

CS4 –Old and vicious

GENEVA

Ozone

AOT60



OFISOFIS photochemical photochemical modelmodel ...results and analysis ...results and analysis

Thessaloniki Indicators Significant differences between scenarios Scenario 1 presents the highest results

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL

Lisbon IndicatorsOnly Scenario 3 presents different results Scenario 3 indicators are higher due to lower ozone consumption pollutants



MARKAL MARKAL ...domain...domain techno-economic energy techno-economic energy modelmodel

Lisbon municipality 82 km2

LISBON

THESSALONIKI

Thessaloniki municipality + 18 municipalities and 2 communes 1100 km2

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



MARKAL MARKAL ...input data...input data techno-economic techno-economic energy modelenergy model Main inputs required by MARKAL:

imported energy prices demand data residual capacities techno-economic data input/output coeficients pollutants emissions associated with technologies

Ex. html file for input data

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



MARKAL MARKAL ...results...results techno-economic techno-economic energy modelenergy model Lisbon Installed capacity in the private transport sector

bas e line - private trans port

0

1000

2000

3000

4000

1 2 3 4 5 6 7 8 9

Pe riod

veh

.km

.da

y-1

TLE1

THE1

TES

TEM

TEH1

TEE3CG

TEE2CG

TEE1CG

TEE1CD

TE8

T39

T2B

Lisbon Installed capacity in the public transport sector

bas e line - public transpor t

0

20

40

60

80

1 2 3 4 5 6 7 8 9

Pe riod

veh

.km

.da

y-1

TT1

TS1

TC1

TB1

TA M2

TA 1

T4G

Main conclusions



Baseline

Common scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



MARKAL MARKAL ...methodology...methodology techno-economic techno-economic energy modelenergy model Lisbon application:

The inclusion of MARKAL in the models cascade is in progress, trying to use VISUM outputs as MARKAL scenarios input.

A simple exercise was carried out, with a new strategy in order to calculate MARKAL inputs for different scenarios

Two environmental constraints were considered:

reduction of ozone precursors

reduction of CO2 emissions in conformity with Kyoto/Marrakech agreements

Main conclusions



Baseline

Scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



MARKAL MARKAL ...results and analysis...results and analysis




Lisbon results for private transport

SC 1 - private transport

0

1000

2000

3000

4000

5000

1 2 3 4 5 6 7 8 9

Period

ve

h.k

m.d

ay-1

TLE1TLE3THE3THE1TEH1TEE4CGTEE4CDTEE3CDTEE1CGTEE1CDTE8T39T38T2CT2B


0

1000

2000

3000

4000

5000

6000

1 2 3 4 5 6 7 8 9

Period

ve

h.k

m.d

ay-1



0

1000

2000

3000

4000

1 2 3 4 5 6 7 8 9

Period

ve

h.k

m.d

ay-1



0

1000

2000

3000

4000

5000

1 2 3 4 5 6 7 8 9

Period

ve

h.k

m.d

ay-1


When comparing installed capacity, only for the Scenario 3 this parameter decreases

Clean technologies (fuel cell and hydrogen) are significant only for CS1

Main conclusions



Baseline

Scenarios

VISUM

TREM

VADIS

OFIS

MARKAL



Some final commentsSome final comments

Genoa, Lisbon and Thessaloniki are successful city-cases: the model cascade was applied for the baseline and for the 4 common scenarios

MARKAL was used to generate baseline and also common scenarios allowing to test the technological, economical and energetic approach

The produced indicators allow a easy and practical analysis of the scenario results

The Old and Virtuous City (Scenario 3) seems to be the best choice, but it is linked to a shrinking and getting older citty.

This complex approach was not completely feasible for all the cities, due to several constraints (data availability, model specificity, ...)


Main conclusions


Baseline

Common scenarios

Documents

University of Aveiro