the application of sat to transport systems · · 2007-05-11unit process scenario bus-transport...

Laboratory for Energy Systems Analysis Brussels, 25. April 2007 M.Spielmann

The Application of SAT to "Transport Systems"

Michael SpielmannBrussels April 2007

Laboratory for Energy Systems Analysis Brussels, 25. April 2007 M.Spielmann 2

Table of Content

Introduction: Life Cycle Assessment (LCA) of Transport Systems

Overall Methodological Issues

• Scenario Modelling

• Environmental Rebound Effects

Technology Category Rules (TCR): Examples

The Economic Aspects and Eco-Efficiency

Concluding Remarks and Acknowledgements


LCI-Transport Model Structure (Transport Components)

LCA of Transport Systems Scenario Modelling Rebound Effects TCR – Examples Economic Aspects Conclusions

Vehicle Manufacturing

Vehicle DisposalVehicle

Maintenance

d3d3d3

Infrastructure Construction

Infrastructure Disposal

Infrastructure Maintenance

d4d5d4

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CO2, fossile (a)

NMHC (a)

PW Diesel Arbeitsweg [%]

Betrieb Energiebereitstellung Fzg. Herstellung u. Entsorgung Fzg. Unterhalt Infra. Unterhalt Infra. Konstrukt ion

Infrastructure Processes


LCI-Transport Model: Unit Process - Matrix - Approach


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jlj

l

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ili

l

bb

bb

bbaa

aa

aa

BA

P

Unit process Process system

A

B

Cummulative Impacts of a process system: XS = B * A-1 * rS


LCI-Transport Model Structure: Demand Factors


Example: Road Fright Transport


LCI-Transport Model: Transport ComponentsLCA of Transport Systems Scenario Modelling Rebound Effects TCR – Examples Economic Aspects Conclusions

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

lorry 32t (RER)

lorry EURO 3 (DE, 50% load)


Barge

Barge excl. river

Rail (RER)

lorry 32t RER



Barge

Barge excl. river

Rail (RER)

CO

2N

ox

operation infrastructure


Types and Scope of LCA studies: level of comparison


Transport technology singularly vs. transport system level

– transport services singularly:– a) an intra-modal comparison (e.g. diesel vs. gas bus)

and/or – b) an inter-modal comparison (e.g. bus vs. rail), provided

a complete substitution of one mode (e.g. rail) with another mode (e.g. bus) takes place.

– transport system level assessment, i.e. taking into account possible changes in the technology development of transport services and in the modal split.


Intra-Modal Comparison (Fuel Type)


Vs.


Inter-Modal Comparison


Vs.


Transport System Level Assessment



Requirements for Sustainable Assessment of Transport Technologies


A systematic and transparent approach addressing the investigation of:

• parallel changes and developments in other technologies and products that will be linked with the analysed technology in the life cycle context; background processes.

• parallel developments of the system to be substituted. For instance, if we investigate the environmental consequences of a

future substitution of rail transport with bus transport we have to account for possible future bus technologies (options) but also we have to investigate future possible train transport technologies.

• the introduction of new transport technologies/modes that are likely to reshape the balance between transport modes and services (e.g. maglev technology) should be investigated on transport system level.



Scenario Modelling


Definitions


Scenarios

• describe hypothetical possible future states of a system (at least 2)

• consist of a consistent set of key variables representing the system under investigation

• assist our understanding of possible future developments of complexsystems

• are neither predictions nor forecasts

• Are no policy recommendations

Scenario Analysis in LCA:

• Aim: Identification of a set of cornerstone scenarios (2-4) representing the entire product system *)

• Problem: Heterogeneous nature of an LCA product system


Requirements and Tool


Requirements

• Scenario development on unit process level (bottom up approach), addressing uncertainties due to different possible developments of technology options (foreground processes) and background systems.

Problem: combinatorial multitude Scenario Integration is required

Tool: Formative Scenario Analysis (FSA)

• Principle of FSA: – allows for the application of formal mathematical criteria to

a)structure the process of unit process scenario constructionb)select a set of consistent and diverse cornerstone scenarios

– variables are known, but the knowledge about the quantitative interrelation of these variables with the rest of the system is incomplete.

( )TnK

ni

nk

Kkikk ddd ,,,,11 KK=s


Case Study: Future Regional Transport vs. Bus


Options for decision maker

Possible development of public bus transport

Research Question:•Is regional rail transport in the year 2020 still the environmental preferable option,

•And if so, under which conditions.


Unit Process Selection


Process Matrix A Unit Processes for Scenario Modelling

Selection Criteria:considerable time dependenceenvironmental importance

Transport Rail Transport Bus

Electricity (p80)

CO2

S1 S2 S3

LFO Refining &Reg.Storage (p90)

CO2

S1 S2 S3

Diesel Refining &Reg. Storage (p100)

CO2

S1 S2 S3

Operation Train (p1)

CO2

S1 S2 S3

Infrastructure Rail(p20)

CO2

S1 S2 S3

Manufacturing Train(p10)

CO2

S1 S2 S3

Operation Bus (p30)

CO2

S1 S2 S3

Manufacturing Bus(p40)

CO2

S1 S2 S3

NG LocalDistribution (p120)

CO2

S1 S2 S3

Extraction/Processing/Transport Oil (p130)

CO2

S1 S2 S3

Extraction/Processing/Transport NG (p140)

CO2

S1 S2 S3


Steps and Moves


Bus OperationBus Manufacturing

Electricity Supply

Unit Process Scenario Development:

Incl. Scenario Variables Selection


Impact Variable Selection and Structural Analysis


Identification of driving forces forenv. interventions of a LCI unit

process

Identification of driving forces of aLCI unit process’ environment

Identification of Key Variables

1. Selection ofScenarioVariables

2. StructuralAnalysis

Moves

)d( t VariablesTechnology )d( e ariablesEconomic V-Socio

1td affectingd of tionIdentifica e

Ttd affectingd of tionIdentifica e

2td affecting d of tionIdentifica e …


Scenario Construction and Quantification


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td selected for states future posible of Definition

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et dd selected of and selected of states Linking et δδ

ed selected for states future posible of Definition

eδ




No. Name Description # of levels Future levels (levels)

Technology Impact Variable ( td ) Compressed Natural Gas (CNG)

1 Fuel Type Fuel used for the operation of bus (compressed natural gas (CNG) or diesel)

21 =m

Diesel

PM-Kat/ Euro 4 Particle-filter/ Euro 5

2 Emission Reduction Technology Technical measures to fulfill certain future emission regulation. (EURO-Norms)

32 =m

DeNOx (SCR)/ Euro 5 Urban: constant travel Rural: constant travel. Urban: stop and go (S&G). Rural: constant travel.

3 Operating Conditions Operation mode depending on type of road and traffic conditions. (trip composition: 20% urban; 80% rural)

33 =m

Urban: 50 % S&G and 50% constant travel. Rural: Constant Travel




No. Name Description # of levels Future levels (levels)

Socio-Economic Impact Variable ( ed ) Vanished Global

4 Significance of Environmental Issues

Orientation of environmental policy and general environmental awareness.

34 =m

Global and local Decline Slight increase

5 Fuel Prices Prices for conventional fossil fuels (diesel and petrol)

35 =m

Significant increase Directed promotion rail and road Promotion rail

6 Transport Policy Promotion of certain means of transport, such as rail use or use of private cars.

36 =m

Promotion road Constant Slight increase

7 Mobility Lifestyle Mobility demand; total amount of passenger kilometers.

37 =m

Significant increase


Unit Process Scenario Bus-Transport (excerpt)


Impact Factors UPS1 UPS2

Significance of Env. Issues global & local globalFuel Prices significant increase slight increaseTransport Policy Road & Rail: + Road & Rail: +Mobility Life Style constant transport demand sign. increased transp. demand

Fuel Type CNG DieselEm.Reduction Technology n.a. DeNox (SCR) Cat (Euro 5)Operating Conditions constant 20% S&G urban

Socio-economic

Technolgy

Linking states of impact variables


Scenario Integration: Identification of Cornerstone Sc.

Integration is based on all unit process specific socio-economic impact variablesProblem:

• 10 impact variables and • each variable only has 2 stateswe will have 210 different scenarios.

Tool for the reduction of number of possible scenarios is required to single out a set of cornerstone scenarios that are charaterised as follows:

• High consistency score • No inconsistencies• Diversity


Tools:

Consistency Analysis:

For each pair of impact variables and possible states a single consistency measure is determined.

Minimum Distance Analysis

Selection of scenarios (out of the set of consistent scenarios) that differ in a defined number of states of impact factors


Cornerstone Scenarios /Excerpt for Bus Operation


Type of Impact variable

Impact Variable World wide environmental focussed regulation

Individual satisfaction

Moderate Environmetal Regulation

Trend

Socio-Economic Transport Policy (CH)directed promotion rail&road promotion road promotion rail

directed promotion rail&road

Sign. of Environmental Issues (CH)

global & local vanished global & local gobalFuel Prices (total, CH)

sign. increase decline slight increase slight increaseMobility lifestyle constant sign. increase slight increase sign. increase

Technology Fuel CNG Diesel Diesel DieselTechnology Kat (not specified) PM-kat/ Euro 4 PM-Filter/ Euro 5 SCR-Kat/ Euro 5Operation/ Traffic S ituation

const.trav 20% S&G urban

10 % S&G and 10% steady flow urban 20% S&G urban

Scenario

Level of Impact Factor


Results/ Interpretation


0

0.01

0.02

0.03

0.04

0.05

0.06

Rail: lo

w cost

Rail: c

omfor

t

Rail: u

ltra lig

htBus

: CNG Car

Rail: lo

w cost

Rail: c

omfor

t

Rail: u

ltra lig

ht

Bus: D

iesel

PM-Kat Car

Rail: lo

w cost

Rail: c

omfor

t

Rail: u

ltra lig

ht

Bus: D

iesel

PM-Filte

rCar

Rail: lo

w cost

Rail: c

omfor

t

Rail: u

ltra lig

ht

Bus: D

iesel

SCR-Kat Car

Technology Options and Alternatives

Clim

ate

Cha

nge

Emis

sion

Sco

res

in k

g C

O2

eq.

/sea

t Kilo

met

re

direct emissions pre combusition manufacturing transport infrastructure

Worldwide Environmental Regulation

Individual Satisfaction

Moderate Environmental Regulation

Trend

Spielmann M, Scholz R W, de Haan P and Tietje O (2004): Scenario Modelling in Prospective LCA of Transport Systems: Application of Formative Scenario Analysis. Int J LCA online first, 11


Conclusions (1)

The feasibility of scenario modelling for LCA could be demonstrated in a transport case study.

In order to facilitate unit process scenario modelling, the selected unit processes must fulfil two conditions:

1. different possible technology options are known and

2. these technology options can be linked to at least one variable in the socio-economic environment of a considered unit process



Conclusions (2)

The proposed procedure is• well-suited to support participatory methods, in particular to structure the process

of scenario modelling, and thus improves the credibility of LCA in the framework of participatory Technology Assessment 1)

• provides a framework for the application of various forecasting methods and allows for the integration of tools to tackle data uncertainties (e.g. Monte Carlo simulation).

• Offers the potential to link technology developments to external impact variables such as policy options, oil price developments.

In principle the concept is applicable to facilitate prospective life cycle assessment of other technologies and thus may be applicable for SAT


1: Joss S. and Bellucci S. (2002) Participatory Technology Assessment. European perspectives. Centre for the Study of Democracy (CSD) at University of Westminster in association with TA Swiss, London.



Rebound Effects


Rebound Effects: Types


Conventional:

Rebound of energy with respect to changes in energy-efficiency

Observed decrease in energy consumption lower than theoretical

decrease as derived from increase in energy-efficiency

Also:

Rebound of time with respect to changes in time-efficiency

(relevant, since time = money, and time = energy)


Rebound Effects: Examples



Transport Related Examples


Rebound effects possibly related to hybrid cars:

• (above-trend) increase in car size

• increase in average household vehicle ownership

• use hybrid car instead of mass transit“ / more and/or longer trips

New by-pass for congested roads

• more traffic

Improved (faster) train service:

• increase of commuting distance (same commuting time budget)


Environmental Rebound Effect (Time)

• The environmental rebound effect describes changes in the environmental performance of a transport system due to the introduction of a time-saving innovation in an existing the transport system.

• Two types of environmental impact changes, describing the difference of a transport system with high-speed transport (A1) and a transport system without high speed transport (A0):





∆EIcp: Environmental impact changes (∆EIcp) expressing exclusively efficiency substitution effects of selected transport service with high speed transport; i.e. the total kilometric performance of the transport system remains constant, (cp = ceteris paribus).

∆EIdc: Environmental impact changes expressing efficiency substitution effects and including environmental impacts due to demand corrections (dc). The latter describe the environmental impacts of the activities that occupies the time that is saved by using a high speed transport service instead of a conventional service.

Environmental Rebound Effect: cp

dc

EIEIERE

∆∆

−=1



The revealed Environmental Impact Changes and theoretical Environmental Impact Changes for each scenario S are derived follows:

where TDi is the daily transport demand (pkm) of a certain transport service i, and EEi is the environmental efficiency (kg/pkm) of transport service i.

Si

Si

i

Si

SSsdc EETDTDEIEIEI AdcA

dc*)( .

001

,1−=−=∆ ∑


Si

Si

i

Si

SSscp EETDTDEIEIEI AcpA

cp*)( .

001

,1−=−=∆ ∑




-10 10

-10

? EIdc

? EIcp

1b

1a

10 5 4

ERE > 0 ERE < 0

AC D III

I IV

II

32

BClassification of Rebound

Effects:1. Classical ERE (0 < ERE < 2)

2. Compensational ERE (ERE = 2)

3. Backfire Effect (ERE > 2)

4. Amplifying Effect (0 > ERE > -1)

5. Leverage Effect (ERE <= -1)


Case Study Swissmetro: Method, Concepts & Scope


Scenario Modelling: (Swiss Transport System 2040)

• Unit Process S.: Technology improvements of transport services

• Service S.: Future changes in traveler behavior

Transport Demand Modelling:• Concept: Constant Travel Time Budget (TTB) 1):

– people will allocate a fixed amount of their daily time to travel. – Consequently, if travel speed increases, the time saved will be exclusively used to travel

more or further. – The TTB only holds for all travel by all modes. The sum of the daily per capita travel

time, TT, over all modes of transport, i, which satisfy daily transport demands, TDi, at mean speed ,vi, must equal the TTB.

Functional Unit: Daily mobility demand of an average Swiss traveler

1: Schafer, A. (1998). "The global demand for motorized mobility." Transportation Research Part A: Policy and Practice, 32(6), 455-477..




0

1000

2000

3000

4000

5000

6000

EI0 EIcp EIdc EI0 EIcp EIdc EI0 EIcp EIdc EI0 EIcp EIdc

Individual Diversity Moderate Regulation Sufficiency World in Change

g C

O2

eq.*(

trav

elle

r*da

y)-1

Car Motorbike Local Public Road Transport Coach Train Aircraft Swissmetro

∆EIcp ∆EIdc




-400

-300

-200

-100

0

100

200

300

400

-400 -300 -200 -100 0 100 200 300 400

IDMR

Su WiC

? EIdc

(gCO2-eq./(p*d)-1)

II

I

III

IV

AB C D

? EIcp

(gCO2-eq./(p*d)-1)


Conclusions (1)

Methodology:

• The foundation of the quantification of the ERE is a comprehensive environmental assessment of the new transport technology as well as possible developments of existing transport services.

• For high-speed transport technologies that require a completely new vehicle concepts as well as transport infrastructure, a life cycle approach – taking into account all transport components – is required.

• Integration of scenario modeling to address uncertainties in technological developments of existing transport services and changes in mobility patterns that may occur at the time the new transport technology is in operation has been demonstrated in a case study.



Conclusions (2)

Constrains:

• Ceteris paribus restriction of unit process paradigm of LCA:expected rebound effects on a macro-level – for instance as a consequence of changes in the regional development – cannot be considered, since they are likely to change production patterns in distant sectors as well.

• Constant travel time budget is not a commonly accepted hypothesis 1).


1: Mokhtarian, P. L. and C. Chen (2004). "TTB or not TTB, that is the question: a review and analysis of the empirical literature

on travel time (and money) budgets." Transportation Research Part A: Policy and Practice 38(9-10): 643-675


TCR Examples



General Information:

Scope: • depends on the type of investigation intra modal vs. inter modal vs. system level.

Functional Unit• depends on the type of investigation intra modal vs. inter modal vs. system level.• supply or demand oriented

Data:• Case specific data (e.g. load factors, emission standards)• Generic data should be based on same assumption (e.g. heating values) and principle (e.g.

cut-off allocation for metals).Impact Categories:

• Mandatory: Climate Change (incl. at least CO2, CH4 and N2O), regulated single pollutants: NOx, PM, NMHC

• Research: Land Use (occupation and transformation) (infrastructure and for biofuels)



Supply to Demand: Break-Even-Points of Load Factors


Bus vs. Car for NOx (incl. vehicle manufacturing)

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Demand (passengers)

Envi

ronm

enta

l Im

pact

NO

x Bus (50 seats EURO2)

Bus (50 seats, EURO4)

Car (1.59 p, Diesel)

Car (1.59 p, Petrol)


Life Cycle Inventory

Vehicle Travel:

• Emission Indices for fuel dependent emissions (e.g. CO2, SO2, HM; e.g. SO2/kgFuel)

• Type of emission measurement. (underlying driving cycle; furthercalculations).

• Emission Reduction Technology

• Inclusion of cold start emissions, varporisation ??

• Emissions due to abrasion (tyres: Zinc, PM)

• Rail Transport: electricity mix (often rail specific mixes)




Fuel Supply:• Demand Factor d = fuel consumption• Fuel type (incl. S-content, heating value, density)• Information on the feedstock and conversion processes in particular for

alternative fuels:E.g. Fischer Tropsch Diesel:

– Feedstock: – Forest wood chips and/or industrial residual wood chips– Properties: heating values (MJ/bulk-m3), potential…..– Transport activities

– Conversion Process: – Plant Size, Plant concept: e.g. Entrained Flow Gasifier vs. Choren-

concept. – Documentation of: heat use, overall process efficiency. By-

products/Allocation ?!




Vehicle Infrastructure:

• Demand Factor d, is based on vehicle life time performance.

• Material Composition (problem: numerous materials)

• Env. Burdens of Assembly Processes (Energy use !!)

• Maintenance Expenditures incl. Repair (Battery !!)

For comparisons: specification and quantification of new features



Life Cycle Impact Assessment: Life Cycle Stages

In the description of the analysis and consequent impact assessment's inventory, and in the calculation and presentation of the results, the life-cycle should be divided into three/four phases:


EcoIndicator 99 H/A

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02

Petrol car Diesel car Petrol car Diesel car Petrol car Diesel car Petrol car Diesel car

2000 2005 2008 2010

EI-P

t/vkm

Vehicle Travel Fuel Chain Car Infrastructure


Life Cycle Impact Assessment: Contribution Analysis (1)

Contribution Analysis of NOx, CO2 and PM5 to the EI-99 HA


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Diesel2000

Diesel2010

Petrol2000

Petrol2010

Diesel2000

Diesel2010

Petrol2000

Petrol2010

Diesel2000

Diesel2010

Petrol2000

Petrol2010

Diesel2000

Diesel2010

Petrol2000

Petrol2010

Nitrogen Oxide PM 2.5 Carbon dioxide Sum (NOx; PM2.5; CO2)

% o

f EI-P

oint

s

Vehicle Travell Fuel Consumption Car Infrastructur Total Life Cycle


Life Cycle Impact Assessment: Contribution Analysis (2)

Relative contributions of major environmental interventions to the overall EI-point score (EI99 HA) for new registered Swiss diesel and petrol cars in 2000 and 2010


Diesel 2000 Rank Petrol 2000 Rank Diesel 2010 Rank Petrol 2010 Rank% % % %

Oil, crude, in ground 46.3 1 53.7 1 49.6 1 51.3 1Nitrogen oxides 11.4 2 5.5 5 6.4 5 4.3 5Particulates, < 2.5 um 7.7 3 3.4 6 4.3 6 4.0 6Cadmium, ion 6.4 4 6.4 3 6.8 2 6.8 3Carbon dioxide, fossil 6.2 5 7.1 2 6.8 3 7.0 2Gas, natural, in ground 5.4 6 6.1 4 6.5 4 6.7 4Arsenic, ion 2.9 7 2.9 7 3.5 7 3.4 7


Life Cycle Impact Assessment: Prospective LCA

Implications for Prospective LCA

So far the presented method for prospective LCA is only is applicable to the life cycle inventory analysis of LCA.

Further research may be focus on the identification and comparison of scenarios already inherent in

• commonly used impact categories and

• weighting factors.



Economic Dimension: Concept of Eco-Efficiency

A

B

C

D

Current Concept

0

0.5

1

1.5

2

0 0.5 1 1.5 2

Economic Expenses (EcoE)

Envi

ronm

enta

l Bur

dens

(Env

B)

inferior

win-win situation

Suh S., Lee K. M. and Ha S. (2005) Eco-efficiency for pollution prevention in small to medium-sited enterprises. In: Journal of Industrial Ecology, 9(4), pp. 223-240.



Economic Dimension EE: Costs vs. Climate Change

IS

PS1

PS2 PS3PS-Mix

Crude Oil Mix

0.00

1.00

2.00

0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80

Normalised Costs

Norm

alis

ed C

C-em

issi

ons

PS1: Logging Residues from Mature TreesPS2: Logging Residues and SD-TreesPS3: SD TreesIS: Logging Residues from Impassable Stands



Economic Dimension: Example Forest Wood Chips

A first attempt to apply the concept of EE, revealed some issues that may need to be discussed:

• Level of detail, i.e. unit process definition: The fuel chain crude oil comprises several unit processes, whilst for cost data the oil price and scenarios for the future development may be used, due to a lack of data.

• Definition of production costs: A clear definition and framework of the terms of production costs (e.g. total capital investment costs, operation and maintenance costs, feedstock costs, etc.) must be presented and match with the transport components of the environmental assessment.

• Allocation Principle: According to ISO 14040, allocation should be based on physical principles. However, in cost studies monetary units are applied. Does that imply, we have to apply economic allocation for LCA as well, in order to be consistent?



Conclusions & Recommendations: Methodology

We only identify one method for sustainable assessment of transport technologies with is characterised by:

• Scenario modelling (bottom up approach)– to tackle uncertainties in the technical development in foreground

and selected background processes.– to tackle uncertainties in the demand for transport services to allow

for an analysis on transport system level. (real impacts).

• System boundaries are imposed by the research question and the object of investigation.

• The choice of functional should be demand oriented, however, uncertainties in utilisation should be stated transparently.



Conclusions & Recommendations: Methodology

Challenges:

Current LCA approach is based on the ceteris paribus restriction 1).

it is questionable, whether LCA is applicable in cases, in which the new technology could radically change the surrounding conditions of the background system.

Rebound Effect Assessment poses new data requirements, in particular on consumers behavior. For instance how to people spend the timegained if they save commuting time. Will it be less or even more energy consuming activities.


1: Hofstetter, P. (1998). Perspectives in Life Cycle Impact Assessment: A Structured Approach to Combine Models of the Technosphere, Ecosphere and Valuesphere, Kluwer Academic Publishers, Boston.


Thanks to

Peter de Haan (ETHZ)

Roland W. Scholz (ETHZ)

Alexander Röder (ex-PSI)

Olaf Tietje (ex-ETHZ)

And to you, for your attention

Michael Spielmann

[email protected]


Reserve


processes

Die

sel @

R

egio

nal S

tora

ge

Gas

olin

e @

R

egio

nal S

tora

ge

NG

Hig

h Pr

essu

re

Elec

tric

ity S

uppl

y

Car

M

anuf

actu

ringd

Mai

nten

ance

Car

Ope

ratio

n C

ar

Roa

d C

onst

ruct

ione

Roa

d O

pera

tion

Transport Passenger Car

Commodity Unit

Diesel Fuel kg 1 0 0 0 0 0 4.7E-3 8.5E-1 0 0 Gasoline Fuel kg 0 1 0 0 0 0 6.2E-2 0 0 0 Natural Gas (NG)

MJ 0 0 1 6.7E-1 2.2E+3 0 0 0 0 0

Electricitya kWh 6.7E-3 6.7E-3 7.2E-5 1 2.1E+3 5.8E+2 0 6.1E-1 4.9E+1 0 Passenger Car v b 0 0 0 0 1 0 0 0 0 6.7E-06 Maintenance Car

v b 0 0 0 0 0 1 0 0 0 6.7E-06

Operation car vkm 0 0 0 0 0 0 1 0 0 6.3E-01 Road ma c 0 0 0 0 0 0 0 1 0 5.0E-04 Operation Road

ma c 0 0 0 0 0 0 0 0 1 9.0E-04

Transport Passenger Car

pkm 0 0 0 0 0 0 0 0 0 1

CO2 kg 0 0 1.4E-7 0 0 0 2.1E-1 0 0 0 Nox kg 0 0 0 0 0 0 4.9E-4 0 0 0

LCI-Transport Model: Unit Process - Matrix - Approach


Demand

Factors (d)

Reference

Flow Vector


LCI-Transport Model Structure: Demand Factors (2)


Average LoadNet Vehicle Weight

Vehicle

Ratio Gtkm/tkmVehicle

Total Gtkmperformance on road

network

Road length Specific roaddemand per Gtkm

Specific roaddemand per tkm oftransport service

Total Kilometricperformance on road

network

Road trans. service xyearly kilometric

performance lorry28t yearly kilometricperformance

lorry16t yearly kilometricperformance

Road trans. service xyearly transport

performance lorry28t yearly transportperformance

lorry16t yearly transportperformance

Specific roaddemand per vkm

Ratio vkm/tkmvehicle

Specific roaddemand per tkm oftransport service

d4

d5


Key Factors and Choice of Functional Unit

Technical Equipment oftransport vehicles/

services


Utilisation of vehicle/transport service


NOx and PM2.5 Emission Score Development NO x -Emissions

0.00E+00

1.00E-04

2.00E-04

3.00E-04

4.00E-04

5.00E-04

6.00E-04

7.00E-04

8.00E-04

9.00E-04

1.00E-03


2000 2005 2008 2010

kg/v

km


PM2.5-Emissions

0.00E+00

1.00E-05

2.00E-05

3.00E-05

4.00E-05

5.00E-05

6.00E-05

7.00E-05

8.00E-05

9.00E-05

1.00E-04


2000 2005 2008 2010

kg/v

km



CO2 and Eco-Indicator Score Development CO2-Emissions

0.00E+00

5.00E-02

1.00E-01

1.50E-01

2.00E-01

2.50E-01

3.00E-01


2000 2005 2008 2010

kg/v

km


EcoIndicator 99 H/A

0.00E+00

5.00E-03

1.00E-02

1.50E-02

2.00E-02

2.50E-02


2000 2005 2008 2010

EI-P

t/vkm



Requirements for Sustainable Assessment of Transport Technologies


Issues determining the type and scope of transport technologies:

• the actual time of penetration/introduction of the new technology: short term, mid-term or long term.

• status of technology development

• the level of comparison: transport technology singularly vs. transport system level


Levels of Technology


Transport Systems & Concepts

Technology Application

Components & Base Technologies

TA

LCA

Technology Driven TA:New technology is considered as a potential solution for a problem

Potential effects of the technology and alternatives must be assessed


Scenario Modelling: Background Uncertainty Management


Epistemological uncertainties are important for prospective LCAs due to the unpredictability of the future state of the system under investigation.

Funtowicz S O and Ravetz J R (1990): Uncertainty and quality in science for policy. Dordrecht, The Netherlands, Kluwer


Scenarios


•Scenarios (S) are neither predictions nor forecasts•S are no policy recommendations•S are no isolated assumptions about the development of certain impact variables nor a sensitivity check with just the variation of model parameters.•S are no valuation

•Scenarios (S) are images of the future or alternative futures (i.e. there must be at lest two scenarios)•S describe a hypothetical possible future state•S provide an information on a system’s development •S consist of a consistent set of key variables representing the system under investigation

What scenarios are notWhat are scenarios




0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6

passivity

activ

ity

Significance of Environmental Issues

Fuel Prices (Diesel & Petrol)

Transport Policy

Passenger TransportMobility Lifestyle Combined Mobility

Electricty Prices

Emission Reduction Technology

Fuel TypeOperating Conditions

Active Ambivalent

PassiveBuffer




OperatingConditions (3)

Combinedmobility (8)

Passengertransport (9)

Electricity Prices

Transport Policy(6)

Mobility Lifestyle(7)

Fuel Price (5)

Significance ofEnvironmental

Issues (4)

Emissionreduction

Technology (2)Fuel Type (1)


Scenario Integration


Linear Consistency

0 4 8 12 16 20time

Linear Consistency

0 4 8 12 16 200 4 8 12 16 20time

Selection Criteria:

•High consistency score

•No inconsistencies

•Diversity

Procedure: Pair-wise ratings of the consistency of the various levels of socio-economic variables of all investigated unit processes are performed. (high consistency, no inconsistencies).Minimisation of the allowed number of impact factors with the same level (Diversity).


Energy OptimisationModelling1)

Unit Process Scenarios

2nd level: (scenario integration):Fuel Price (level: constant, high)Emerging Technologies (conservative, optimistic)Tax regimes (no taxes, pollutants, greenhous gases AND pollutant)

Unit Process Scenarios

1st level:Powertrains (ICE, Fuel Cell)Fuels (Oil, Natural Gas, Nuclear Power, Biomass, Solar)

1: Röder, A. (2001). "Integration of Life-Cycle Assessment and Energy Planning Models for the Evaluation of CarPowertrains and Fuels," PhD, ETH, Zürich.


Service ScenariosImpact Variables and Possible States

1st level: (quantification of travel time) •Leisure mobility (level: bau, mobility focused, local oriented)•Commuting (bau, spatial separation residence and work place)•Shopping (….)•Mode choice short distance trips (…)•Mode choice short distance trips (…)•Human powered mobility (…)

2nd level: (scenario integration)•Environmental awareness (high, low)•State regulation (high, low)

Service Scenarios


Rebound Effects: Types


DIRECT rebound effect

• (also primary RE): Demand for same resource/service

INDIRECT rebound effect

• (also secondary RE): other demand fueled by additional purchasing power

MACRO-LEVEL rebound effect

• (also economy-wide/tertiary RE): effects from adjustment of

production and distribution structures


Procedure Environmental Burdens

Cumulative Environmental Interventions (CEI) for Forest Wood Chip Supply Chains (FWCS):

∑ −+=j

FWSCMAMATruckTruckFWSC jijjiAFPRCEFLCEFCEI *** 1

,

CEFTruck: Cumulative Emission Factor Truck transport (operation, fuel supply, maintenance, manufacturing & disposal)

L: Load Factor Truck

CEFMA: Cum. Em. Fact. Forest Machinery (kgint*PMH-1) (operation, fuel supply, maintenance, manufacturing & disposal)

PRMA : Productivity of Forest Machinery (bulk-m3*PMH-1)

AF: Allocation Factor: Forest Machine j operation for energy wood


Harvesting Chains/Equipment Passable Stands


Harvesting Chain/Equipment Impassable Stands

Impassable Stands

Logs

Mature and SmallDiameter Trees

Stand

Locationof

Process

Striproad

ForestRoad

Forest -and

NationalRoad

Plant

Felling w.Chainsaw

Skidding w.Cable Crane

IS

Chipping w.Mobile Chipper

Logging Residues (LR)

Preparation ofChips and

Gasification

Bundling w.Energy Wood

Ha.

Truck Transportof Bundles


Results: Forest Wood Supply Chains vs. Crude Oil: Climate Change-Emissions

Interventions Crude Oil: Jungbluth, N. Erdöl. In Sachbilanzen von Energiesystemen: Grundlagen für den ökologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in Ökobilanzen für die Schweiz; Final report ecoinvent 2000 No. 6, Paul Scherrer Institut Villigen, Swiss Centre for Life Cycle Inventories: Duebendorf, CH, 2003.

Interventions Residual Chips: modified from Werner, F. et al. "Life Cycle Inventories of Wood as Fuel and Construction Material," EMPA Duebendorf, Swiss Centre for Life Cycle Inventories, 2004

0.00E+00

5.00E+00

1.00E+01

1.50E+01

2.00E+01

2.50E+01

PS1Chipping atlanding ofLogging

ResiduesMature T

PS2: TerrainChipping of

SD Trees andLogging

Residues

PS3: TerrainChipping ofPole TreeBunches

IS: LoggingResidues

FromIMPassable

Stands

Forest WoodChips MixPassabelStands

Forest WoodChips Mix

TotalPotential

IndustrialResidual

Wood ChipsPlantMIx

Raw Oil fromAfrica at

Refinery inCH

Raw Oil fromMiddle East at

Refinery inCH

Raw Oil fromNG at

Refinery inCH

Raw OilImport Mix atRefinery in

CH

kg C

O2

eq./G

J

Production Transport


Results: Forest Wood Supply Chains vs. Crude Oil: PM2.5-Emissions

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03

2.50E-03

3.00E-03

PS1:Chipping atlanding ofLogging

Residues(Mature T.)

PS2: TerrainChipping ofSD Trees

and LoggingResidues


IS: LoggingResidues

FromIMPassable

Stands

ForestWood Chips

MixPassabelStands

ForestWood Chips

Mix TotalPotential

IndustrialResidual

Wood ChipsPlantMIx

Raw Oilfrom Africaat Refinery

in CH

Raw Oilfrom Middle

East atRefinery in

CH

Raw Oilfrom NG atRefinery in

CH


CH

kg P

M2.

5/G

J



Results: Forest Wood Supply Chains vs. Crude Oil: NOx-Emissions

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

6.00E-02

PS1:Chipping atlanding ofLogging

Residues(Mature T.)

PS2: TerrainChipping ofSD Trees

and LoggingResidues


IS: LoggingResidues

FromIMPassable

Stands

Forest WoodChips MixPassabelStands

Forest WoodChips Mix

TotalPotential

IndustrialResidual

Wood ChipsPlantMIx

Raw Oil fromAfrica at

Refinery inCH

Raw Oil fromMiddle Eastat Refinery

in CH

Raw Oil fromNG at

Refinery inCH


CH

kg N

Ox/

GJ



Economic Dimension: Example Forest Wood Chips


Cumulative Environmental Interventions (CEI) for Forest Wood Chips:

Cumulative Cost (CC) for Forest Wood Chip Supply Chains (FWCS):

CCFWSCi= CCTruck + ∑CCMAj

*AFFWSCi,j

CEFTruck: Cumulative Emission Factor Truck transport

L: Load Factor Truck

CEFMA: Cum. Em. Fact. Forest Machinery (kgint*PMH-1)

PRMA : Productivity of Forest Machinery (bulk-m3*PMH-1)

AF: Allocation Factor: Forest Machine j operation for energy wood

CCTruck: Cumulative Costs Truck transport

CCMA: Cumulative Costs Forest Machine j

∑ −+=j

FWSCMAMATruckTruckFWSC jijjiAFPRCEFLCEFCEI *** 1

,


Eco Efficiency: Costs vs. NOx-emissions

PS1

PS2 PS3

PS-mix

Crude Mix

IS

0

10.4 1.4

Normalised Costs

Norm

alis

ed N

Ox-

emis

sion

s

EU4-Standard for Harvest Machinery

EU4-Standard for Harvest Machinery

PS1: Logging Residues from Mature TreesPS2: Logging Residues and SD-TreesPS3: SD TreesIS: Logging Residues from Impassable Stands


Conclusion

Forest Wood Chip Chains• The operation phase of forest machinery and truck dominate the

harvesting life cycle of Forest Wood Chips.• Forest wood chips derived from IMpassable stands show worse

environmental performance than wood chips from passable stands.• Production is more important than transport (assuming a distance of

20 km)• Bundling of wood is the critical element in the impassable stand chain.

For energy wood from passable stands chipping causes the highestemissions.

• A further inclusion of environmental burdens (allocation) due to felling and skidding of trees has only a marginal impact on the outcomes of logging residues chains


Model Specifications/ Assumptions

40

45

50

55

60

65

70

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

g/vk

m fu

el c

onsu

mpt

ion

Petrol Car (CH) g/km Diesel Car (CH) g/km

Average (Diesel + Petrol) Car (CH) g/km Average (Diesel + Petrol) Car (EU) g/km

target value EU

target value CH (28.7 % diesel)

y = 18.467x - 35555R2 = 0.9755

1'100

1'200

1'300

1'400

1'500

1'600

1'700

1995 2000 2005 2010 2015

Ave

rage

veh

icle

wei

ght [

kg]

Regulated Emissions: NOx, PM, HC, CO.Euro2: 2000

Euro3: 2005

Euro4: 2008

Euro5: 2010 (Proposal)

Material Composition DevelopmentIncrease of Aluminum 4% to 22%

Fleet Fuel Consumption Development

Fleet Weight Development


Description of Technology

• Description of future rail options (three trains types, electricity mix) – Total weight, yearly performance, life span, number of seats,

electricity consumption per kg and km.

• Identification and Presentation of possible developments for competing transport services.

– Bus: Fuel Type: Diesel vs. CNG, Emission technology/Euro standard, etc, number of seats, fuel consumption.



Description of Scenario Modelling

Description of the various steps and actors involved (if participation is part of the project)

Description of technology and socio-economic impact variables for all included unit processes.

Description of all states of impact variables.

Documentation of quantification of scenarios

Overview of selected cornerstone scenarios (labeling and storyline)



General Information: Prospective LCA

Goal and Scope of Scenario Modelling:

• Research Question: – Is regional rail transport in the year 2020 still the environmental

preferable option,– And if so, under which conditions.

• Definition of transport components to be included, based on typeof comparison.

• Definition of unit processes which are included in scenario modeling.


the application of sat to transport systems · · 2007-05-11unit process scenario bus-transport...

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