1. travel
2. transports
3. public transport

How to Identify MITIGATION Potentials and
ADAPTATION Requirements of Transport
Systems?
Approach to Facilitate Consideration of CLIMATE CHANGE in the
Transport Sector
Tanja Schäfer
 PTV AG Jun-13
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Content
Content
1
Introduction ............................................................................................................ 7
2
Objective, Framework & Scope of the Approach................................................. 8
3
Work Steps to Implement the Approach ............................................................ 11
3.1
3.1.1
Set-up of Excel-Dossier
12
3.1.2
Definition of Application Case
12
3.2
Work Step 2: Identification of Climate Change Characteristics ................. 15
3.3
Work Step 3: Identification of Specific Locations where Climate
Change will have Negative Impact on Transport Infrastructure
(existing and planned) .............................................................................. 16
3.4
Work Step 4: Preparation of Impact Analysis and Appraisal .................... 18
3.5
4
Work Step 1: Preparation of Assessment ................................................. 12
3.4.1
Indicators for Impact Analysis
18
3.4.2
Input Data for Impact Analysis and Appraisal
19
Work Step 5: Impact Analysis ................................................................... 26
3.5.1
Calculation of Monetisable Indicators
26
3.5.2
Calculation of Non-Monetisable Indicators
30
3.6
Work Step 6: Appraisal and Synthesis ..................................................... 30
3.7
Work Step 7: Sensitivity Analysis ............................................................. 32
3.8
Work Step 8: Compilation and Presentation of Results ............................ 32
Description of Indicators ..................................................................................... 34
4.1
4.2
Monetisable Indicators (both Application Cases) ...................................... 35
4.1.1
Fuel or Energy Consumption
35
4.1.2
Greenhouse Gas Emissions (Carbon Dioxide)
39
4.1.3
Air Pollutant N0x
42
4.1.4
Air Pollutant PM 10
42
4.1.5
Vehicle Operation Costs (VOC)
44
4.1.6
Travel Time
46
KO-Indicator (only Application Case “Adaptation Planning”) ..................... 48
4.2.1
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Cut-off Crucial Civic Infrastructure
48
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Content
4.3
5
6
Non-monetisable Indicators (only Application Case “Adaptation
Planning”) ................................................................................................ 49
4.3.1
Affected Sensitive Areas
49
4.3.2
Affected Shops, Offices
51
4.3.3
Affected Street Vendors
52
4.3.4
Affected Pedestrians and Cyclists
54
Information on Calculation of Energy Consumption and Emissions .............. 56
5.1
Rational ................................................................................................... 56
5.2
Approach ................................................................................................. 58
5.3
Selection of Consumption and Emission Factors ..................................... 59
References ........................................................................................................... 62
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Content
Images
Image 1:
Overview on working steps to implement both application cases
11
Image 2:
Example of cover-sheet for application case: adaptation
14
Image 3:
Calculated flow accumulation (blue) and reported traffic interruptions
(circles), Source: graphic by Potsdam Institute for Climate Impact
Research
17
Image 4:
Overview on indicators with respect to both application cases
19
Image 5:
Traffic on flooded road, Source: www.thisismyindia.com
22
Image 6:
Flow-bundle analysis, Source: own graphic, based on VISUM and
google maps
23
Image 7:
Processes of emission calculation
24
Image 8:
Extract of dossier-sheet “Calculation of monetisable indicators for
private transport –peak hour”, example transport model input
27
Image 9:
Example for utility function
31
Image 10:
Layout of the sheet: assessment of vulnerabilities
32
Image 11:
Layout of the sheet: summary of appraisal results
33
Image 12:
Specific C02-emissions of different modes, Source: ifeu 2008
57
Image 13:
Co-Benefits of energy-efficient/low carbon transport, Source: GTZ
2009
57
Image 14:
System boundaries, Source: own graphic
58
Image 15:
Distribution of energy consumption Well-to-Wheel, Source: ifeu 2010
59
Image 16:
Comparison of the main features of emission models HBEFA and
ARAI for road transport
60
Data sources for developed emission model
61
Data collected in parking survey: example car, Source:
Chidambaram 2011
25
Utility function for weighted benefit analysis
31
Image 17:
Tables
Table 1:
Table 2:
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Content
List of Abbreviations
APSRTC
Andrah Pradesh State Road Corporation
ARAI
Automotive Research Association of India
BA
Benefit Analysis
BCA
Benefit Cost Analysis
CC
Climate Change
CNG
Compressed Natural Gas
DPR
Detailed Project Report
EPTRI
Environment Protection Training and Research Institute
ESCI
Engineering Staff College of India
EU
European Union
€
Euro
FLAC
Flow Accumulation
GDP
Gross Domestic Product
GHG
Greenhouse Gas
GoAP
Government of Andrah Pradesh
GoI
Government of India
GTZ
Deutsche Gesellschaft für Technische Zusammenarbeit
HBEFA
Handbook emission factors for road transport
IDC
Indian Driving Cycle
IES
Integrated Environmental Strategies
INR
Indian Rupees
IPCC
Intergovernmental Panel on Climate Change
HGV
Heavy Goods Vehicle
HIS
Household Interview Survey
HMDA
Hyderabad Metropolitan Development Authority
HUDA
Hyderabad Urban Development Authority
LGV
Light Goods Vehicle
LOS
Level of Service
LPG
Liquefied Petroleum Gas
min
Minutes
MMTS
Multi-Modal Transport System
MRTS
Hyderabad Metro Rail
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Content
MoUD
Ministry of Urban Development
NITW
National Institute of Technology Warangal
OECD
Organisation for Economic Co-operation and Development
PCU
Passenger Car Units
PIK
Potsdam Institute for Climate Impact Research
PrT
Private Transport
REC
Regional Engineering College Warangal
RF
Roughness Factor
ROB
Road over Bridge
RSI
Roadside Interview Survey
SUV
Sports Utility Vehicle
TCM
Turning Count Movements
VOC
Vehicle Operation Costs
WBA
Weighted Benefit Analysis
WS
Work Step
WTW
Well-to-Wheel
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Introduction
1
Introduction
Extreme weather events like heavy rainfalls, floods, or draughts are already phemomena we
experience nowadays. And they are already causing severe problems today (e.g. Mumbai
26th July 2005). There is furthermore a growing consensus that these phenomena will
increase in the future driven by climate change (CC). Also India acknowledges the
challenges of this phenomenon with its National Action Plan for Climate Change
(Government of India, 2008).
Due to the rapid urbanization worldwide and the growing importance of cities as economic
centres - today already of 80% of Gross Domestic Products GDP are generated in cities - the
problems will more severely affect countries’ economies when cities are affected.
So there is a need for cities to prepare for climate change and adapt vulnerable infrastructure
to changed conditions and to consider climate change implications when creating new
infrastructure. This refers especially to transport infrastructure, as it plays a very important
role for the well-being of cities.
Although it might seem far-fetched to consider climate change implications for new
infrastructure in the far future this issue should not be neglected. Studies show, that usually
upgrading new (not yet-existing) infrastructure causes less incremental costs than upgrading
of existing infrastructure. Which gives more importance on the integration of adaptation
planning into early stages of transport planning (strategic planning) (GTZ, 2009; p. 20).
Another positive effect that a resilient infrastructure has is its mitigation potential. By
minimising traffic disturbances through extreme weather events, related CO2-emissions can
be avoided.
Besides the fact, that cities are most negatively affected by climate change, they are also
contributing the highest share of greenhouse-gas-emissions (GHG) to the atmosphere. So
there is high potential in cities to contribute to climate change mitigation and a unique
opportunity to shape energy-use especially in transport and urban form towards a low-carbon
pathway.
Both aspects make cities THE locations, where mitigation as well as adaptation measures to
CC are best/most effectively placed.
By addressing mitigation and adaptation-issues at the same time, synergies can be utilised in
the related planning processes (short-term, strategic). To get the best results, crossdepartmental cooperation and decision-making is required (GTZ 2009: p. 9).
To support the various planning and decision-making processes, a new approach was
developed within the scope of the Sustainable Hyderabad (work package “Sustainable
Transport Planning for Hyderabad”) research project to identify and quantify the local impacts
of climate change on the urban transport infrastructure, in order to facilitate the design and
implementation of appropriate adaptation and mitigation plans or strategies.
The manual at hand will give a detailed and user-friendly description on the scope of the
approach, the necessary Work steps to implement the approach, the chosen indicators and
how to calculate them as well as the methodological background.
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Objective, Framework & Scope of the Approach
2
Objective, Framework & Scope of the Approach
As explained in the introduction above it is important to create resilient urban transport
infrastructures to minimise the risks associated with increasing extreme weather events.
Hence, one must be aware that the adaptation of infrastructure needs to be done in all urban
infrastructure sectors to ensure the functioning of the city under extreme weather events, not
only in one sector. Adaptation in all sectors will most probably lead to budget constrains
which will make a ranking of adaptation measures necessary to allocate the budget for the
most beneficiary measures. But even intra-sectoral ranking can be appropriate when many
locations are affected negatively (Adaptation Planning).
Likewise to the adaptation all infrastructure sectors need to contribute to the exercise of
mitigating GHG-emissions. The transport sector contributes roughly 20% GHG-emissions to
climate change (International Transport Forum, 2010). But while other sectors are reducing
their fossil fuel consumption and therefore GHG-emissions due to technical improvements,
the energy-savings due to technical improvements in the transport sector are eaten away by
the dramatic increase of fuel consuming and energy-inefficient modes of transport like
individual motorized modes.
Therefore the second objective of the approach is to develop the methodology in a way that it
is simultaneously suitable to enable the urban planners to design an energy-efficient and
overall sustainable transport system (Mitigation Planning). This can be looked at as
strategy-appraisal which is commonly used in strategic transport planning.
The main objective of the newly developed approach is consequently to come-up with a
methodology that enables urban planners to
identify specific locations where climate change has negative impacts on existing or
planned transport infrastructure,
identify and if possible quantify the impacts of extreme weather events in order to identify
the most vulnerable and neuralgic locations for the functioning of the transport system,
identify and quantify the impacts of transport strategies in order to identify the most
energy-efficient mix of measures for the functioning of the transport system,
prioritise the adaptation measures for implementation (Action Plan) on strategic as well
as on operative level OR to prioritise the best transport strategy.
This is done by defining a core-set of indicators that is suitable for both application cases and
supplementation of specific indicators for the application case adaptation planning.
At this stage the approach for the urban transport infrastructure is developed with respect to
the weather event which is assumed to have the most negative impact on urban
infrastructure – extreme rainfalls and flooding associated with it. But in coming projects it is
envisaged to enhance the impact analysis to other weather phenomena like
“temperature/heat”.
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Objective, Framework & Scope of the Approach
Main features of the approach are:
Ex ante-Analysis: In Latin ex-ante means "before the event". They are by definition
predictive relying on ex-ante or forecasted information or data and are therefore limited in
their degree of certainty. Nevertheless they are a fundamental tool for effective
management. Basically ex-ante analysis is a process that supports the preparation of
proposals, plans or strategies. Its purpose is to gather information and carry out analyses
that help to define objectives, to ensure that these objectives can be met, that the
instruments used are cost-effective and that reliable later evaluation will be possible (EU
Commission, 2001).
Targets: The main objective of this analytical approach is to identify the impacts of
different planning activities (Adaptation Planning/Mitigation Planning). In the second
place it is meant to support the prioritisation of measures due to their impacts. Meaning
that the strategy/measure with the least negative impacts or the highest benefits should
be chosen. To be able to do this one needs to have targets/goals that need to be
achieved by the planning. The underlying normative goals or targets of the approach at
hand can be named as follows:
reduction of energy-consumption,
reduction of GHG-emissions,
reduction of air pollution,
reduction of vehicle operation costs,
reduction of travel time losses,
reduction of further negative impacts on residents and commercial units by
extreme weather events.
Method: The method used for the ex-ante impact analysis and appraisal either for an
extreme weather event or one year (typical year after implementation of the plan) is a
hybrid-method. It comprises on the one hand quantitative indicators which are
monetisable1. The synthesis of the results for the monetisable indicators is done as
benefit - analysis with default monetarisation-factors that are given in the manual. On the
other hand the method includes non-monetisable quantitative indicators, for which the
synthesis is done by summing up the benefit points2 per indicator to one weighted impact
value. All indicators are described in detail in chapter 4. A schematic overview of the
hybrid-method is shown in Image 1 in the next chapter.
If required the benefit analysis part can be expanded to an economic evaluation, as
monetarisation-factors for the benefits are given. In this respect different methods like Net
Present Value, Benefit Cost-Ratio or Internal Rate of Return Method can be applied. But
please note that the benefits have to be calculated according to the requirements of the
chosen economic evaluation method. So far the benefits are either calculated for different
events or per year (see above), not for a full planning period as it is necessary e.g. for the
Net Present Value Method.
1
2
Impacts transferred into monetary terms.
Benefit points result from a transformation of quantitative indicator results by a linear utility function.
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Objective, Framework & Scope of the Approach
Relative Comparison: Basically the location-wise impact analysis compares the situation
without disruption (base case) with the situation when disruption occurs (analysis case).
The advantage of this relative comparison is that even if effects are difficult to distinguish
in their absolute quantity, the relative differences between “with-case” and “without-case”
can still be evaluated (see also excursus on transport model in chapter 3.4.2.1).
The developed approach is the attempt to cover as many important affects and impacts
related with the flooding of transport infrastructure or infrastructure planning in the transport
sector as possible. At the same time it also aims to be a user-friendly method meaning that:
the necessary effort to apply the method is reasonable and takes constrains on dataavailability into consideration,
results are edited in a transparent and simultaneously comprehensible way.
Many years of developing assessment procedures have shown that it is very important to
consider these aspects equally to the impact analysis in order to really give helpful input into
the process of developing an appropriate adaptation or mitigation plan and the public debate
on these plans.
Nevertheless especially with respect to the application case „adaptation planning“, there is
very limited scientific evidence or know-how at the moment, how extreme precipitation
events affect the transport system and traffic demand or flow. This is on the one hand due to
the fact that this phenomenon has been neglected up to now. On the other hand, this might
be due to the fact that, these impacts depend to a great extent on local climatic and
orographic conditions and thus varies widely. But even, if not all questions related to impacts
of extreme rain events can be answered unequivocally or input data can be provided, it is still
beneficial for the planning and the decision-making process to familiarise with the issue in a
structured manner.
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Work Steps to Implement the Approach
3
Work Steps to Implement the Approach
The following section serves as a short description of the work steps, related with the two
application cases addressed by the developed approach:
analysis of climate change impacts – resp. flood events – on the transport system and
the subsequent assessment of locations and measures in order to develop an action plan
to improve the resilience of the transport system efficiently (Adaptation Planning) and
analysis of the energy-efficiency of different transport strategies in order to mitigate
transport related GHG-emissions (Mitigation Planning).
The simultaneous description of the methodology for two application cases is possible as
most work steps are very similar for both approaches.
Image 1 below visualises the various work steps associated with the implementation of the
approach for both application cases and highlights differences where they occur.
To make the description of more practical use the working steps are explained along the usecase of Hyderabad. This includes the use of the developed Excel-tool (dossier) to calculate
parts of the impact analysis or compile all necessary information and partial results to one
comprehensible, transparent result to present to the decision-makers.
Image 1:
Overview on working steps to implement both application cases
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Work Steps to Implement the Approach
3.1
Work Step 1: Preparation of Assessment
3.1.1
Set-up of Excel-Dossier
Impact analysis and appraisal is supported by an Excel-dossier that facilitates these work
steps themself but also the presentation and documentation of the gained results to decision
makers.
The dossier comprises the following tables
Cover-Sheet, which gives basic information on the evaluation case like a short
description of the planning area
Summary of appraised impacts (benefits)
Summary of impacts
Calculation of impacts for monetisable indicators: Public Transport and Private Transport,
separate for peak and off-peak hours
Calculation of impacts for not-monetisable indicators
Different sheets with necessary factors and assumptions for calculation of impacts
Basically the dossier is designed in a way that it is usable for both application cases
(mitigation and adaptation) as well as for different cities or other spatial contexts. But as the
dossier is developed along the use-case of Hyderabad the different tables reflect to some
extend the specific conditions of this use-case in calculating the results (e.g. aggregationlevel of data, reflected modes of transport, spatial context). Hence modifications of the
sheets regarding the specific use-case, will most probably be necessary. The use of the
dossier is described in chapter 3.5 mainly.
3.1.2
Definition of Application Case
First and very important step is the identification and definition of the scope. This includes
the:
Decision if you want to apply the impact analysis for the assessment of city-wide
transport strategies (including adaptation aspects) or if you want to use it for a ranking of
existing locations in order to develop an Adaptation Action Plan (operative approach).
As the approach can be used for both types of applications, it is necessary to define the
scope explicitly.
Definition of planning area: After the definition of the application case, the spatial
delineation (certain part of the city, Agglomeration area) should clearly be defined and
described. This area has to be identical for: base case and analysis case.
It is important to define the boundaries in such a way, that all possible impacts of the
application case get covered. E.g. for strategy appraisal all centres of activity (e.g. special
economic zones, satellite towns) even if they are out of the city limits should be reflected
in the planning area. To facilitate the necessary data collection, the planning area should
best reflect defined city/municipal boundaries.
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Work Steps to Implement the Approach
Definition of planning horizon: For a location-wise impact analysis aiming at the
improvement of the resilience of the existing transport infrastructure a short-term horizon
may be applicable. For a strategic planning task like a Comprehensive Transport Study
or a Comprehensive Mobility Plan the planning horizon should be 15-20 years from the
base year (IRC, 2009). It should also reflect a typical year after implementation of all
measures related to the strategy which is subject of the appraisal.
Please note that this approach calculates benefits for one extreme weather event or a
typical year of operation after implementation of the projects not for all years that lie
within the planning horizon.
Definition of base case/reference case (“without-case”): Case where no measures or only
the definitely fixed infrastructure projects/measures are included, that will be implemented
within the planning horizon even without the strategy/plan or measure you want to
assess. This case is also called the „do-nothing alternative“.
Definition of analysis case(es) (“with-cases”): Case(s) where certain measures or
combination of measures (strategies) are implemented or certain events occur (extreme
rainfall event).
Definition of common time datum/level of prices: The present and future benefits and
costs of different projects occur at different points of time. To enable comparison between
them it is necessary to transfer them to a common basis. Therefore they should be
rebased to equivalent values at a common date (=price levels for discounted cash flow
method). This can be done with the help of price indices (IRC 2009).
Most prices used to calculate indicators in this manual are based on IRC 2009, with price
levels based on March 2009 (IRC, 2009).
Definition of discount rate: In the approach presented here, the discount rate is only
needed when the Benefit-Analysis is to be enhanced to a Benefit-Cost-Analysis or other
economic evaluation method (see explanation of method in chapter 2). Generally the
selection of an appropriate discount rate is important for economic evaluations. IRC 2009
advises that in countries were capital is very scare the chosen rate should not be less
than the rate of borrowing or lending by the Government or the market rate of interest. In
India currently a discount rate of 12% percent is used by the planning commission
according to IRC 2009. IRC itself takes 10% in its example-calculation.
These definitions and settings should be briefly included in the cover-sheet of the Exceldossier introduced above and shown in Image.
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Work Steps to Implement the Approach
Image 2:
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Example of cover-sheet for application case: adaptation
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Work Steps to Implement the Approach
3.2
Work Step 2: Identification of Climate Change
Characteristics
For the use-case of Hyderabad, the climate change characteristics described below were
identified by Potsdam Institute for Climate Impact Research (PIK) by downscaling different
global scenarios for the Hyderabad region. The methodology taken by PIK can be transferred
to other cities. To do so, please follow the detailed description of the approach in Lüdecke
et.al. 2010.
PIK assessed the expected range3 of a changing global climate for possible future scenarios
in the 21st century, depending on a low (B2, global emission reduction from about 2035 on)
and a high global CO2 emission scenario (A2, business as usual) outlined in the forth
Intergovernmental Panel on Climate Change Assessment Report (Mehl et. al, 2007), and two
relatively strong differentiating climate models4. A downscaling of global climate scenarios for
the Hyderabad region has been undertaken based on these scenarios and on historical
records of climate data from the Begumpet weather station. The results for four climate
variables lead to the conclusion that Hyderabad has to prepare for an increase of 60% of
extreme precipitation events (more than 80 mm/ rainfall per day) until 2050 and a potential,
but still uncertain change in total annual precipitation. The total annual precipitation will be
distributed more unevenly, so that both longer dry periods and an increase in the amount of
rainfall are possible. This would have a range of different consequences for various functions
of the city as the water regime changes towards more rain in shorter (extreme rainfall) and
less rain over longer time periods (periods of long dryness). Average temperatures in
Hyderabad will go up with a high degree of certainty. Burdensome high night-time
temperatures will go up by a factor of two to three until 2050 and further increase until 2100.
The projected impacts will affect transport systems, agricultural production, energy
generation, and give rise to concerns about water supply, food provisioning, and public
health, especially for the poor.
The biggest problems related to weather disturbances in Hyderabad arise from extreme
rainfall events as they cause inundation of houses and roads in low-lying areas of the city.
That is why this climate characteristic is addressed in the impact analysis. A detailed
description of the climate change characteristics for Hyderabad is also given in Lüdecke
et.al. 2010.
3
The range was assessed for the four following climate variables: mean annual temperature, total annual precipitation,
frequency distribution of daily precipitation, in particular the frequency of heavy rain events plus frequency and length of heat
waves.
4
The level of certainty of the climate system representation of Hyderabad was assessed by their degree of consensus with 17
independent climate models, provided by the IPCC (Lüdecke, Kit, 2012).
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Work Steps to Implement the Approach
3.3
Work Step 3: Identification of Specific Locations where
Climate Change will have Negative Impact on Transport
Infrastructure (existing and planned)
After the identification climate change characteristics for Hyderabad in general, specific
locations, where rainwater gets accumulated (flood prone areas) need to be identified. This
was also carried out by Potsdam Institute for Climate Impact Research using two data
sources:
Flow Accumulation Maps (FLAC maps) and
Newspaper Reviews: Reviews of newspaper articles for the years 2000-2010, where
traffic interruptions caused by rainfalls were reported. The review periods were chosen
with respect to rainfall events noted by the weather station in Begumpet/Hyderabad
Based on the climate change projections (see work step 2), data sources mentioned above
and further sources PIK has developed a Climate Assessment Tool for Hyderabad (CATHY).
CATHY is a software tool, based on an open source Geographic Information System (GIS),
which shows the effect of different climate change scenarios for Hyderabad up to 2100 on
different parts of the city fabric. It comprises various information e.g. on number and spatial
distribution of population, land use (e.g. slums) and also on the existing and in future
expected flood locations.
The following picture shows areas of Hyderabad where rainwater accumulates due to
Hyderabad’s orography. These locations, marked in blue, reflect areas of high vulnerability to
strong rain events – now and in future. Please note that the blue spots do not take into
account existing drainage systems and sewage infrastructure but show the natural flow
accumulation of water.
Areas that are regularly reported having transport breakdowns due to flooding are given as
circles in green, brown and red. The colours are indicating the intensity of rain5, whereas the
signatures, e.g. R1 or B4 indicate the designation of the locations in the transport model for
the impact analysis.
5
The precipitation levels representing the different colours are as follows: 30 mm rainfall per day (green circles in Image),
70 mm rainfall per day (brown circles in Image), 110 mm rainfall per day (red circles in Image).
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Work Steps to Implement the Approach
Image 3:
Calculated flow accumulation (blue) and reported traffic interruptions (circles),
Source: graphic by Potsdam Institute for Climate Impact Research
The image shows clearly that already today, the transport infrastructure is affected by
extreme precipitation events. The newspaper analyses indicate that many roads in high flow
accumulation areas get blocked at precipitation of 30 mm/day (green spots). The main
reason for this can be seen in an inadequate and poorly maintained drainage system. Unless
the drainage system at these locations is improved and well managed these traffic
interruptions will remain and even increase, as the number of extreme precipitation events
will increase in the future due to CC. This underlines the importance of the application case
“adaptation planning” for existing locations”.
The fact that the reported traffic disruptions mostly occur at locations of flow accumulations
stresses that the approach implemented in CATHY, facilitates the adaptation of future
“transport”-infrastructure to climate change, as the accumulation give hints for future
“hotspots”.
The use of the information gained from CATHY for the consideration of climate change in the
impact analysis is described in chapter 3.4.2.1.
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Work Steps to Implement the Approach
3.4
Work Step 4: Preparation of Impact Analysis and
Appraisal
3.4.1
Indicators for Impact Analysis
The most common instruments to get a clear or clearer picture of the changes or
consequences connected with an event, an intervention or a help to assess the performance
of a system are indicators. According to OECD/DAC an indicator is “a quantitative or
qualitative factor or variable that provides a simple and reliable means to measure
achievement, to reflect changes connected to an intervention, or to help assess the
performance of a development actor” (OECD 2004).
Regarding the choice of indicators it has to be taken into account that it is not possible to
consider all aspects and interdependencies of scenarios to be analysed. It is necessary to
simplify the situation by choosing indicators that describe important consequences of the
situation to be analysed. These indicators must be representative, measurable, comparable
and feasible.
Within the selection process of indicators to be considered in the approach of the megacityproject, the following aspects have been taken into account:
The choice of indicators is based on international and so far existent Indian standards in
terms of approaches for costing and project assessment in the transport sector. This
enhances the acceptance for the developed assessment procedure.
The choice and calculation procedure of indicators reflects the fact that the impact
analysis should be used for ex-ante evaluations of strategies or redevelopment plans of
infrastructure objects in order to identify adaptation necessities and mitigation potentials.
Ex-ante evaluations can only be carried out if indicators possess the ability of prognosis.
With respect to the assessment of adaptation necessities more impacts need to be taken into
account than for the assessment of mitigation potentials, to get the full picture on the severity
of the disruption. For example is it important to know if a crucial civic infrastructure is or
would be completely cut off by the disruption - even for a few hours only. Further effects to
be considered are economic losses due to destroyed roads, railway lines or obstructions of
public transport generally, working time losses and higher vehicle operation costs.
Having the full picture is a necessity for the subsequent decision on:
the order in which the redevelopment of affected locations (ranking) that are already
existing is done or
the way, new infrastructure is getting designed and implemented.
All indicators are also described in detail in chapter 4. This description comprises also the
applied methodology to compute or appraise the impacts.
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Work Steps to Implement the Approach
Image 4:
3.4.2
Overview on indicators with respect to both application cases
Input Data for Impact Analysis and Appraisal
To compute the indicator values for the impact analysis, vulnerability analysis or appraisal of
projects, plans and strategies a lot of data input is necessary. Furthermore assumptions and
estimations need to be made, as often reliable empirical data is not available – which is
particularly the case for adaptation planning.
Some of the data input to compute the indicator values or even the indicator values
themselves can be extracted relatively easy and directly from a transport model, especially
for quantitative indicators. Transport models are standard tools used world-wide for the
planning of urban, regional and national transport systems. Many of these quantitative
indicators can even be monetised in order to include them into a cost-benefit analysis or
other economic evaluation methods. Other indicators need to be derived from site-visits and
surveys.
The following chapters describe briefly the different data input necessary for the impact
assessment according to their origin.
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3.4.2.2
Generation of Data Input for Impact Analysis from Transport
Model
For both application cases the following model-based input data are needed for the impact
analysis:
Vehicle-km by different vehicle categories
Vehicle- and person-hours
Level of services
Speeds
These model-generated data are used for subsequent post-assignment calculations, which
take place partly internal to the transport model but also partly external6. For detailed
description for computing of indicators see chapter 4.
Ideally these output data are calculated separately for each hour of a working day (24 hours).
Hence this needs reliable information on daily variations of traffic volumes which are not
available in any case, as in Hyderabad. In cases when full daily variations are not available,
at least peak-hour and off-peak-hours should be modelled. Otherwise possible differences in
traffic situations/traffic flows cannot be reflected at all. Most of the times traffic jams will occur
– if at all – at peak hours, while at off-peak traffic might go smoothly. According to the
appraisal requirements these time slices are extrapolated to daily and annual level later.
Short Description of Hyderabad Transport Model:
In the present case the transport model of Hyderabad is set-up with the state-of-the-art
Software VISUM developed by PTV and includes road and rail infrastructure. This model
is based on demand data (average daily traffic for weekday) collected during studies
carried out between 2001 and 2004 in Hyderabad by Indian Institutions. The demand data
comprises: private transport and public transport but not commercial transport. The base
year model is set-up on demand data projected for the year 2011 (workday) and with
mode split data derived from traffic counts carried out in phase 1 of the research project,
where traffic was counted at 15 mid-block locations in Hyderabad (Walther, Schäfer 2007).
Time horizon for the strategic forecast model is 2021. For the impact analysis matrices for
peak-hour and off-peak-hour were derived from the daily demand matrix. Classification
and definition of capacities of the road network is based on geometric standards for Urban
Roads in Plains according to IRC 2001. Due to date of the studies the available demand
data does not consider changes made in the last couple of years e.g. major new facilities
such as flyovers, expressways and the new airport and is therefore not fully accurate.
However the data quality is sufficient as basis of the approach developed here, especially
as the impact analysis compares the situation without extreme weather event / or strategy
(base case) with the situation when the extreme weather event occurs or strategies are
implemented (analysis cases). The advantage of this relative comparison is that even if
effects are difficult to distinguish in their absolute quantity, the relative differences between
possible scenarios/strategies can still be evaluated. Nevertheless in case more recent
data are available the model can easily be up-dated. A detailed description of the model
6
Please note: the model-data is computed link-wise. But all models are capable to deliver aggregated outputs for the external
can be found in Schäfer et.al. 2010.
calculations if necessary.
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In the following sections important issues that have to be addressed in the transport model,
in order to generate appropriate modelling results (data input for the impact analysis), are
explained briefly.
Integration of flooded or flood-prone areas into the transport model:
To localise the affected roads in the transport model it is necessary to integrate the locationfine climate information into the transport model.
In the use-case of Hyderabad the GIS-data from the climate model CATHY from PIK was
integrated into the transport model by a shape-file interface (see chapter 3.3 for explanation
of CATHY). Prerequisite for this data-exchange was a harmonisation of the coordinate
system, which was used by both partners. The advantage of the harmonisation activity is that
updated information from the climate model can be integrated easily into the transport model
and therewith into the transport planning process. After integration of the affected locations in
the transport model, the affected transport infrastructure (roads, railway lines etc.) can be
localised.
For the use-case of Hyderabad the following three rainfall-scenarios were taken into account:
30 mm rainfall per day (green circles in Image),
70 mm rainfall per day (brown circles in Image),
110 mm rainfall per day (red circles in Image).
Appropriate blockage/reduction of capacities of roads for flood events:
To quantify the impacts of the floods on the transport system it is vital to model the effects of
the incident – basically behaviour of road users - as realistic as possible. Given the little
number of studies that deal with the effects of floods on travel demand, traffic flow and
capacities of road or other transport infrastructure, this is not an easy task.
Nevertheless own perceptions, reviews of the limited number of newspaper articles and
studies indicate that in most cases, road floodings due to heavy rainfalls don’t cause a
complete break-down of all traffic for a full day or even days. Most times road traffic comes to
a complete halt for one or two hours only: while and shortly after the event, when the water
logging on the road is highest. Afterwards traffic starts moving again through the flooded
areas – beginning with busses and SUVs - but with a slower speed, as can be seen in Image
5 below.
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Image 5:
Traffic on flooded road,
Source: www.thisismyindia.com
Regarding effects of heavy rainfalls on the overall travel demand study-results indicate that
rainfall increases the accident rate to a higher extent than it decreases travel demand. Travel
demand is only slightly decreased (1-3%) by rainfall with lower reductions on weekdays than
on weekends (Chung, E. et. al; 2006; Simmonds, 2004). Accordingly the effect of rainfall on
travel demand can probably be neglected due to the high uncertainty and the minimal
impact. In the use-case of Hyderabad it was not taken into account in the modelling.
But the effect of rainfall on traffic flow and road capacity should be reflected in the modelling
according to the severity of the rainfall event in the future (amount of precipitation) and the
specific conditions of the locations. This can either be done by blocking of the effected roads
for some hours or reducing the capacity of the effected roads to the extend known or
assumed.
After modelling the effects, new assignments have to be carried out to quantify the effects of
the disruptions on the traffic flow (peak/off-peak). As kind of a sensitivity approach, variations
of residual capacity can be modelled. The variation with the most realistic results should thus
be the one taken for the analysis. In the use-case of Hyderabad three different residual
capacities (30%, 50% and 80%) were modelled as kind of sensitivity analysis and the 50%variation was chosen as analysis case after evaluation of the results.
Image demonstrates how traffic volumes change on different roads, when the flooding of a
location (here location B1/Khairatabad Junction in Hyderabad) is modelled with 50% residual
capacity. Basically the image directly shows the difference in traffic volumes between the
base case (no flooding) and the analysis case (with flooding). The affected roads, where
capacity was reduced, are indicated with purple colour. Green colour indicates a reduction of
traffic volume due to the decreased capacity compared to the base case; red colour indicates
increase of traffic volume due to route changes to avoid the blocked road or the traffic jam
caused by the event.
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Image 6:
Flow-bundle analysis,
Source: own graphic, based on VISUM and google maps
Vehicle fleet - for calculation of fuel consumption and emission:
Vehicle fleet details are needed (apart from road category and Level of Service/LOS) for the
calculation of fuel consumption as well as emissions.
Fuel consumption and emissions in the transport sector are mainly influenced by the type of
vehicle (size, emission standard, age, fuel etc.), traffic situation (average speed, number of
stops, level of service), road categories, number of cold starts per day, air-condition, altitude
and gradient. Information on road categories and traffic situation can be gained from the
model and modelling process. Vehicle fleet is information that needs to be derived from other
sources like vehicle registration statistics or traffic/parking surveys.
Usually emission transport calculations are carried out with emission models like the
International Vehicle Emission Model (IVE for developing countries (http://www.issrc.org/ive/)
or the Handbook of Emission Factors for Road Transport (Keller 2010) in Europe, which
need to be adapted to the local conditions (fleet mix, road capacities etc.). And usually these
calculations are done with output from transport models but externally to the transport model.
This results in a lot of data exchange/handling between transport model and emission model,
as usually such processes are accompanied with many iteration steps who can be described
as follows and shown in Image 7: modelling of events or measures, extraction and transfer of
relevant data to emission model, emission calculation, analysis of emission results,
identification of potential to improve the implemented measure due to results of emission
calculation, implementation of new option in model, exchange of model data to emission
calculation, new analysis of the impacts and so on.
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Image 7:
Processes of emission calculation
In the case of the research project therefore a new approach regarding the calculation of
motorised individual transport (cars, three-wheelers, motorcycles) was chosen: calculation
embedded in the transport model with a newly implemented module “Handbook of emission
factors for road transport” (Keller, 2010).
The Features of the “Handbook of emission factor for road transport” and why its emission
factors were chosen as well as other background-information on emission modelling is given
in detail in chapter 5.
With the chosen approach the vehicle fleet has to be defined in the model itself. The
advantage of this approach is that effects of floodings or regulatory measures (e.g. zoning
restrictions for old vehicles non-compliant with new standards), like deteriorating traffic flows
or changes in level of services, can be considered more accurately and easily than in postmodelling external calculations. Another advantage is that the so gained road-wise
information on transport related emissions can also be used easily not only for strategy
appraisal but also generally for the design of emission mitigation plans.
Information regarding the current vehicle fleet in Hyderabad was gained in March/April 2011
by parking surveys carried out in at different locations in Hyderabad in the course of a PhD-
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work (Chidambaram, 2011)7. An example of the collected data can be seen in Table 1 below.
This information was used to select the appropriate vehicle types and share of the vehicle
types in the emission-calculation module embedded in VISUM.
Vehicle Type
Fuel
Air Fuel
Control
Exhaust Control
Evaporative
Control
Age (years)
Auto/Sml Truck
Petrol
Carburetor
2-Way/EGR
PCV
10
Auto/Sml Truck
Petrol
Single-Pt FI
3 Way/EGR
PCV
5
Auto/Sml Truck
Petrol
Carbureto
2-Way/EGR
PCV
10
Table 1:
Data collected in parking survey: example car, Source: Chidambaram 2011
In case VISUM is not used as transport model to generate the input data for the impact
analysis the emissions can be calculated externally with mean emission factors. In order to
get more accurate results, the emission factors used should reflect different LOS and not
only one emission factor per type of vehicle and km (for more details see chapter 5).
3.4.2.3
Generation of Data Input for Impact Analysis – non Transport
Model based
The non-model based input data refer to the application case “adaptation planning” and are
used to quantify/calculate the non-monetisable indictors of the weighted-benefit analysis.
Following the list of the necessary non-model-based input data:
Locations of important civic infrastructure, like hospitals or fire brigades near to affected
location, to see if they are cut off in case of event.
Locations of sensitive areas, like residential areas near to affected location, to see if any
disturbance by deviated traffic might occur.
Information on number and kind of shops or other business units near to the affected
location, to see if economic losses due to close-down times or missing customers might
occur.
Information on number and kind of street vendors, to see if economic losses due to closedown times or missing customers might occur.
These non-model-based data can be derived from google maps and/or empirical surveys.
The detailed description on how these input data are used for weighted benefit analysis can
be found in chapter 4.3.
7
The German Federal Ministry of Education and Research (BMBF) sponsored this work in the funding programme “Research
for the Sustainable Development of the Megacities of Tomorrow”. The particular research is also supported by the German
Academic Exchange Service (DAAD). B. Chidambaram is doctoral researcher at Humboldt University of Berlin.
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3.4.2.5
Other Input Data for Assessment/Appraisal
Besides the above described data additional input is needed for the impact analysis and
appraisal, like factors to extrapolate hourly values to daily or annual values; occupancy
factors to calculate person hours etc. Or - with respect to the location-wise adaptation
planning - assumptions on the severity of the event need to be made if possible.
These specific data or factors are used in work step 5, where the actual impact analysis is
taking place. This WS is mainly carried out in the Excel-dossier. Therefore they will be
explained in the following chapter, at the places where they are used. This is done in order to
give on the one hand a clearer picture of them and on the other hand to avoid redundancies.
3.5
Work Step 5: Impact Analysis
After all necessary input data for the impact analysis has been collected, generated,
computed etc. the systematic impact analysis, assessment and processing of the results can
start. This process is described briefly in the following chapters.
3.5.1
Calculation of Monetisable Indicators
For the final calculation of the monetisable indicators the Excel-dossier (see chapter 3.1.1)
may be used. As mentioned earlier, the set-up of the indicator calculation reflects the local
data situation of Hyderabad at the time of the study. Hence the settings have to be adjusted
most likely to available local data. Where ever possible options to adjust the setting to other
conditions are listed to facilitate this adjustment process.
The Excel-dossier provides four calculation-sheets, where data input from the transport
model or pre-calculations needs to be inserted. These sheets are differentiated as follows:
Calculation of monetisable indicators for private transport at peak-hour (sheet-name:
Calculation_mo_ind_Peak_PrT)
Calculation of monetisable indicators for private transport at off-peak-hour (sheet-name:
Calculation_mo_ind_offPeak_PrT)
Calculation of monetisable indicators for public transport at peak-hour (sheet-name:
Calculation_mo_ind_Peak_PuT)
Calculation of monetisable indicators for public transport at off-peak-hour (sheet-name:
Calculation_mo_ind_offPeak_PuT)
In all sheets, the entry fields for the transport model input are marked with yellow colour, as
can been seen in Image 8 below. Entry fields for pre-calculations with light brown and input
from other sheets of the dossier in green.
The subdivision in peak-hour and off-peak-hour follows the data availability and requirement
of the use-case Hyderabad. In case a different set of time slices is available the sheets can
be modified according to these requirements.
The sheets provide different columns where data for the base case and the respective
analysis case have to be filled in. They also provide two columns for sensitivity analysis. In
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case deeper analysis needs to be carried out, these columns can be duplicated according to
the specific requirements, analogue to the subdivision of time slices.
Image 8:
Extract of dossier-sheet “Calculation of monetisable indicators for private transport –peak hour”,
example transport model input
Furthermore fields are provided on different sheets where necessary factors/assumptions
need to be inserted. The following section explains which factors are needed to be given on
which of the Excel-sheets and for what reason.
Sheets: Calculation of monetisable indicators for private transport. As mentioned
above, this calculation is split in two –almost identical8- sheets for peak-hour and off-peakhour.
These sheets comprise from top to bottom:
No. of additional days per year with extreme rain: This field is used in application case
“adaptation planning” to extrapolate the daily results to annual level by taking the
frequency per year into account.
Here factors from the sheet “Factors and assumptions for Analysis Case” according the
considered rain scenario need to be inserted.
In the use-case of Hyderabad we distinguish three rainfall scenarios (green: 30mm/d 8,44 additional days/year, brown: 70mm/day – 3,56 add. days/year and red: 110mm/day 0,64 additional days/year). If the considered location (junction etc.) is affected at 70mm
precipitation per day, the number of additional days per year is: 3,56 + 8,44, because a
location that is already flooded at 30mm/d will also be flooded when more precipitation
occurs. Therefore the days get summed up. This has to be taken into account when
selecting this factor.
Please Note: In order to get results for the base case (both application cases) or analysis
case for the application case “mitigation planning” minimum input is 1 NOT 0.
8
Calculation for peak and off-peak only vary at two factors (congestion factors) for the calculation of the indicator “vehicle
operation costs”.
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Duration of event in days: This field is also only used in application case “adaptation
planning” to reflect the severity of the event. Here assumptions should be made
according to the local conditions and know-how on impacts that can be observed already
nowadays. In some locations 70 mm of precipitation might only cause problems for a few
hours in other locations it might be for a full day or even two. Given the limited number of
studies, this assumption is not an easy task, therefore this field is optional.
Please note that this factor is in addition to the modeling of the impacts on the traffic flow,
which is described in chapter 3.4.2.1.
Please also note: In order to get results here for base case in both application or analysis
case for mitigation planning minimum input is 1 NOT 0.
Number of peak-hours/off-peak-hours: This field/information is mandatory for both
application cases. It is required to extrapolate the hourly results to daily level. Please
note: sum of these hours must not exceed 24.
Number of days PrT/ Number of days PuT: These fields are mandatory for the
application case „mitigation planning”. They are required to extrapolate daily results to
annual level.
To calculate the impacts for the monetisable indicators of PrT in the Excel-sheet, additional
factors need to be provided, as explained above. As these factors may vary between base
case (BAC) and analysis case (ANC), two different sheets are provided. For reasons of
clarity they are not included in the “calculation sheet” but on the following separate sheets:
Factors and Assumptions for Base Case (sheet-name: Factors PrT_BAC)
Factors and Assumptions for Analysis Case (sheet-name: Factors PrT_ANC)
Sheets Factors and Assumptions
The factors from top to bottom:
Precipitation Events per Scenario: Here the different rain scenarios (= amount of
precipitation per day) that are taken into account are defined and the number of
additional days per year given. These factors are used in the calculation sheet to
extrapolate the daily results to annual level (see above).
Factors for Motorized Private Vehicle Fleet:
Modal split planning horizon: is used for calculation of vehicle operation costs
(VOC) and time saving as this is done externally to the transport model. Please
note: Modal split for this calculation should be taken from transport model.
Occupancy rate: is used for calculation of time savings per vehicle category, as
this is done externally to the transport model (analogue above).
Distribution of fuel types for emission calculations: as fuels differ in their energyefficiency this information is necessary to calculate the full emissions (direct and
upstream).
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Sheet: Cost Factors, from top to bottom
Vehicle Operation Costs: is used to calculate VOC according to IRC 2009 for different
vehicle types, distance and time related. The factors given in IRC reflect uncongested
free-flow conditions for different road types and roughness factors (RF). In order to
consider effects of congestion IRC additionally provides equations for distance and time
related congestions effects.
The Excel-sheet provides an example for one distinguished road category and RF
including related congestion factors.
Number and kind of road categories considered need to be adapted to the specific use
case. Furthermore applicability of congestion factor has to be proved based on actual v/C
ratios.
Value of Time: is used to monetise the time savings of different vehicle types or modes
for the benefit appraisal. Default values given are based on assumptions and IRC (see
also description of indicators). These values may need to be adopted.
Fuel Costs INR per Liter Fuel without Tax: is used to monetise fuel consumption for
benefit appraisal. Default values given are according to IRC 2009 and own calculations
(see also description of indicators). These values may need to be adopted.
Costs in INR per Unit: is used to monetise emissions for benefit appraisal. Default
values given are according to HEATCO 2006 (see also description of indicators). These
values may need to be adopted if local information is available.
Sheet: Conversion_Factors, from top to bottom
Factor to calculate upstream-emissions for road Transport: these factors for different
road based vehicle categories – private transport as well as public transport - are
necessary to calculate upstream emissions. Default values given are according to
european information (see also description of indicators). These values may need to be
adopted if local information is available.
Factor to calculate upstream-emissions for electrified rail-based transport:
analogue to road transport.
Conversion factor for fuel (kg into liter and kg into MJ) to enable comparison of
energy consumption of different fuel types especially with electric powered rail systems.
Sheets: Calculation of monetisable indicators public transport.
This calculation is also split in two – almost identical - sheets for peak-hour and off-peakhour9.
According to the use-case in Hyderabad, the sheets are set-up to calculate impacts for three
different bus types and two different rail-based PuT-Types (MMTS and MRTS).
9
Calculation for peak and off-peak only vary at two factors (congestion factors) for the calculation of the indicator “vehicle
operation costs”.
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Like in the calculation of the impacts for the monetisable indicators of PrT, additional factors
also need to be provided for PuT: The different sheets provide necessary default values for
the calculation of the impacts of the so far considered types of PuT (see above).
The sheets are set-up in a similar way as the factor sheets for PrT, therefore no detailed
description is necessary. The only exemption is that for PuT also average emission factors
are provided (see sheets and description on indicators in chapter 4). Similar to the
calculation of PrT impacts, the given factors most likely need to be modified to the
requirements of the specific use-case. The sheets are named as follows:
Factors and Assumptions for Base Case (sheet-name: Factors PuT_BAC)
Factors and Assumptions for Analysis Case (sheet-name: Factors PuT_ANC)
3.5.2
Calculation of Non-Monetisable Indicators
As mentioned before this calculation is only applicable for adaptation planning. Basically the
calculation of these indicators is done outside the Excel-dossier and only the results of it are
included in the Excel-sheet: Assessment of Vulnerabilities (sheet name:
quant_indicators_adaptation) in the fields marked in light brown colour. The description how
the indicator values need to be calculated can be found in chapter 4.
3.6
Work Step 6: Appraisal and Synthesis
The appraisal of the impacts identified for the analysis case is, in the case of the
monetisable indicators, done by monetising the impacts with defined cost factors. Hence it
is possible to add-up the single values to one result (value synthesis). For this step the
Excel-dossier provides the sheet “Summery of Appraisal Results”, where the different
impacts are multiplied with the respective cost factors and summoned up to one value.
For the appraisal of the non-monetisable indicators the Excel-dossier provides the sheet
“Assessment of Vulnerabilities” (sheet name: quant_indicators_adaptation). This appraisal is
designed as weighted benefit analysis.
In a first step of this approach the physical units from the impact analysis get transformed
into benefit points by a linear utility function with scale limits of + 100 and -100. In order to
make the results comparable, the same utility function is applied to transform all indicator
results to benefit points. A linear function between the scale limits reflects the fact that each
saved unit generates the same benefit, as diminishing or increasing marginal benefits are
difficult to proof. The scale limits are set endogenously, meaning out of all evaluated analysis
cases. This is sensible because for the chosen set of indicators no exogenous limits, like
noise level limitations or political restrictions, are available. But it is necessary to note that
minimum three analysis cases need to be evaluated due to this methodology and that the
scale limits may change if new alternatives get evaluated at a later time.
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The utility function can be described as follows:
Name of Variable
Formale Description
Indicator
i
Value of indicator in Base Case
Vi,BAC
Value of indicator in Analysis Case
Vi,ANC
Difference of Indicator Analysis Case compared
to Base Case
∆ i = Wi, ANC − Wi, BAC
Maximal absolute difference of indicator
between Analysis Case and Base Case
max(i) = max ∆ i
Right scale limit
RSL=max(i) and BP (RSL) = -100
Left scale limit
LSL=-RSL and BP(LSL) = +100
Utility function
Table 2:
Image 9:
ANC
BP(i) =
∆i
∗100 LSL ≤ ∆i ≤ RSL
LSL
Utility function for weighted benefit analysis
Example for utility function
In the second step the transformed benefit points can be weighted according to the
severity of the impact the rain event has. This severity assessment can be made based
on a formalised system. In this respect a scale from -1 to + 1 is given to assess the
severity of the analysed effect in order to arrive at weighted benefit points (last column
in Image 10, below).
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Information on how to assess the severity of the effect is given in the detailed
descriptions of the respective indicators in chapter 4.
The formalisation facilitates the comparison between the different indicators, provided
that the factors are applied in the same manner for different analysis cases.
Image 10:
3.7
Layout of the sheet: assessment of vulnerabilities
Work Step 7: Sensitivity Analysis
To test the robustness of the results, different assumptions underlying the calculation of the
impacts or the assessment should be varied. This also helps to distinguish the importance of
different assumptions for the final result (ranking). According to experience, value of times
and fuel costs should be varied. Sensitivity analysis can also be used to distinguish the
minimum impact a measure or strategy must have to gain positive results.
3.8
Work Step 8: Compilation and Presentation of Results
This is the final compilation and presentation of all results in an aggregated but still
comprehensible way, to give the full picture of the impacts and benefits, the event or
strategies has.
For this purpose the Excel-dossier provides in sheet “summary of appraisal results”, which
comprises of all appraisal results: monetisable, non-monetisable and ko-criteria. The
compilation is based on input from other sheets of the Excel-dossier. Therefore no extra
data-input is needed for this sheet. The results are given as saldo of analysis case against
base case. The layout of the sheet is shown in Image 11 below. If more than one analysis
case is subject of the impact analysis or appraisal an additional sheet should be developed,
where all results are compiled in different columns.
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Image 11:
Layout of the sheet: summary of appraisal results
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Description of Indicators
4
Description of Indicators
The following chapter gives a description of the indicators by data sheets.
The indicators´ data sheets include the following content:
Relevance of indicator
In this section reasons for choosing the respective indicators are given with respect to the
system of objectives.
Range of application
This section gives information for which case of application the indicator is applicable
(adaptation planning or mitigation planning), as not all indicators are used for all application
cases. Additionally the relevant modes of transport are indicated here.
Assessment approach
The calculation procedure for subtracting the indicator’s values of analysis case from base
case is defined.
Measurand
The measurand represents the parameter value which describes the achievement of the
corresponding objective.
Calculation rules
In this section the procedure for calculating the indicator is described in detail.
Input data
This section contains a short description of the data necessary for carrying out calculations.
Additional information (optional section):
In this section particularities of the indicator are stated.
Please Note: In the use-case of Hyderabad Heavy Duty Vehicles (HDV) were not subject of
the assessment. Therefore the approach and Excel-dossier does not include them. If
necessary they can be added and should be treated like road traffic.
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Description of Indicators
4.1
4.1.1
Monetisable Indicators (both Application Cases)
Fuel or Energy Consumption
Relevance of indicator
Generally fuel or energy consumption is an important indicator to highlight the consumption
of fossil and therefore non-renewable energy-resources.
India is largely dependent on fossil fuel import to meet its energy demand — by 2030,
India's dependence on energy import is expected to exceed 53 % of the country's total
energy consumption10. In this context the indicator is also showing the potentials different
measures/strategies have, to influence foreign exchange by reduction of fuel consumption.
To compare all environmental impacts of a whole transport system – not only resource
consumption but also emissions – the total energy chain has to be considered as different
modes have very different energy-efficiency levels. See also explanation in chapter 5.
The complete chain comprises:
Exploration of primary energy carriers,
Conversion within power plant or refinery,
Energy distribution (transforming and cable losses/electricity; transport to petrol
station/fuel),
Final energy consumption (= direct consumption).
Range of application
Adaptation Planning – PrT and PuT
Mitigation Planning – PrT and PuT
Assessment approach
MeasurandAnalysis case – measurandBase case
Measurand
Measurand:
Modes:
[l fuel/year & MhW/year]
PrT and PuT
Calculation rules
Road traffic (2-Wheeler, 3-Wheeler, 4-Wheeler):
Direct/Final fuel consumption is calculated: veh-km x g fuel/veh-km,
for different vehicle and fuel types plus different road types and specific traffic situations
(e.g. peak /off-peak), according to Keller et. al. 2010.
This is done internal to the transport model and output is included in Excel-dossier.
10
http://en.wikipedia.org/wiki/Energy_policy_of_India
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Description of Indicators
Upstream fuel consumption is calculated in Excel-dossier: final fuel consumption for different
fuel types (kg/fuel) / efficiency factor for fuel production (different fuel types), according to
ifeu 2010.
Bus service (different types like Ordinary/Metro Express):
Direct/Final fuel consumption is calculated: veh-km x g fuel/veh-km (for different vehicle
types & different - average - traffic situation (peak /off-peak) provided in Excel-dossier.
Traffic situation applied for the calculation of fuel consumption should reflect fleet
characteristics and aggregated traffic situation (by e.g. V/C-Ratio).
Please note: in application case “adaptation planning” it is very unlikely that changes will
occur as bus based services will only change their routes in exceptional cases.
Upstream fuel consumption is calculated in Excel-dossier: final fuel consumption / efficiency
factor for efficiency factor for fuel production, according to ifeu 2010.
Railway traffic (MMTS/MRTS):
Direct/Final fuel consumption is calculated in Excel-dossier: veh-km x kWh /veh-km (for
different vehicle types & average traffic situation), according to ifeu 2010.
Please note: in application case “adaptation planning” it is very unlikely that changes will
occur as rail based services will only change their routes in exceptional cases.
Upstream fuel consumption is calculated in Excel-dossier: final energy consumption x
efficiency factor for energy/veh-km, according to ifeu 2010.
Aggregation of mode-wise results for comparison reasons:
Conversion of kg fuel (diesel and petrol) into MJ to enable a comparison of strategies which
include electrified modes of transport.
Approach for monetisation:
Road traffic, Bus service:
litre total fuel x INR/litre total fuel, according to IRC 2009
prior to that: conversion of g of fuel into litre
Railway traffic:
kWh total x INR/kWh, according to Audinet Pierre (2002)
Input Data
Road traffic:
Fleet mix.
Mileage taken from traffic and transport models, divided into vehicle types, road links
and traffic situations.
Fuel consumption factors by vehicle types (final consumption), road links and traffic
situations taken from Keller et al. (2010). See explanation in chapter 5.
Efficiency factor to compute total consumption taken from ifeu 2010 (default values
included in Excel-dossier).
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Description of Indicators
Bus service:
Fleet mix.
Mileage taken from transport model, data from provider or detailed analyses of specific
transport routes, depending on application case.
Fuel consumption for different vehicle types and aggregated traffic situations (final
consumption) taken from Keller et al. (2010). Default values for different vehicle types
and traffic situations given in Excel-dossier.
Efficiency factor to compute total consumption taken from ifeu 2010 (default values
included in Excel-dossier).
Railway traffic:
Fleet mix.
Mileage taken from transport model, data from provider, detailed analyses of specific
transport routes, depending on application case.
Average energy consumption taken from ifeu 2010.
Efficiency factor to compute total consumption also from ifeu 2010.
Comparison:
Factors to convert kg/diesel, petrol or CNG into kWh and MJ (values included in the
dossier)
Cost rate for monetisation:
Prices of fuel per litre without taxes according to IRC 2009, price level 2009.
Price per kWh without taxes according to Audinet Pierre (2002), price level 2000
extrapolated to 2009.
Sources:
Fleet mix: vehicle technology surveys (e.g. respective parking surveys), data from
Pollution Control Board etc.
Audinet, Pierre (2002): Electricity Prices in India, in Energy Prices and Taxes, 2nd
Quarter 2002, http://mahadiscom.com/emagazine/jan06/india1%5B1%5D.pdf.
ifeu (2010): EcoPassenger, Environmental Methodology and Data, commissioned by
International Union of Railways (UIC).
IRC 2009: Manual on Economic Evaluation of Highway Projects in India, second
revision, IRC:SP: 30-2009, New Delhi.
Keller et al. (2010): Handbook emission factors for road transport (version 3.1), Bern,
2010.
See explanation in chapter 5.
Excel-dossier has default-value included to calculate upstream values.
Conversion factors: Ministry of Finance British Columbia, 2010.
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Description of Indicators
Additional information
Background information on calculation of energy consumption and emission see chapter 5.
Alternative approach to the imbedded fuel consumption modelling in the transport model
(VISUM-based) is to set-up a tool where the consumption gets calculated external to the
transport model but with output from the model regarding vehicle-km and to some extend
traffic situations.
Without consideration of the traffic situation the impacts of measures that target to improve
the traffic flow cannot be distinguished. For more information see chapter 5.
Comment on application case “adaptation planning”:
Public Transport is an important mean of transport in a sustainable transport system, as it is
more energy-efficient than private motorized transport (PrT). Individualised private transport
can react more flexible on disturbances e.g. by changing routes or destinations than public
transport can, especially rail based public transport. Yet even busses rarely change their
routes if disturbances occur, but merely get stuck in the event or close down their services.
To keep the public transport services attractive for customers, in order to avoid their shift to
PrT it is vital to reduce disturbances to a minimum. Therefore the information on the
importance of the location for public transport should be taken into account, when designing
adaptation plans.
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Description of Indicators
4.1.2
Greenhouse Gas Emissions (Carbon Dioxide)
Relevance of indicator
Increasing greenhouse gas concentration is one of the major reasons for climate change
and global warming. In terms of transportation emissions CO2 is the most important
greenhouse gas. It directly results out of combustion processes. Therefore increasing
mileage due to non-available infrastructure objects leads to increasing emissions of CO2.
To compare environmental impacts of a whole transport system the total energy chain has
to be considered as different modes have very different energy-efficiency levels.
The complete chain comprises:
Exploration of primary energy carriers,
Conversion within power plant or refinery,
Energy distribution (transforming and cable losses/electricity; transport to petrol
station/fuel),
Final energy consumption (= direct consumption).
Range of application
Adaptation Planning – PrT and PuT
Mitigation Planning – PrT and PuT
Assessment approach
MeasurandAnalysis case – measurandBase case
Measurand
Measurand:
Modes:
[kg C02/year]
PrT and PuT
Calculation rules
Road traffic (2-Wheeler, 3-Wheeler, 4-Wheeler):
Direct/Final C02-emissions are calculated: veh-km x g CO2/veh-km,
for different vehicle types, different road types and the specific traffic situations (e.g.
situations in peak /off-peak).
This is done internal to the transport model and output is included in Excel-dossier.
Upstream C02-emissisons are calculated in Excel-dossier: final fuel consumption for
different fuel types (kg/Fuel) x emission factor final fuel consumption (kg/fuel), according to
ifeu 2010.
Bus service (different types like Ordinary/Metro Express):
Direct/Final C02-emissions are calculated: km (for different vehicle types & different –
average - traffic situation (peak /off-peak) in Excel-dossier.
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Description of Indicators
Upstream C02-emissisons are calculated in Excel-dossier: final fuel consumption (kg/fuel) x
emission factor final fuel consumption.
Railway traffic (MMTS/MRTS):
Direct/Final C02-emissions are calculated in Excel-dossier: veh-km x g CO2/veh-km, for
different vehicle types & average traffic situation, according to ifeu 2010.
Upstream C02-emissisons are calculated in Excel-dossier: final fuel consumption kg/fuel x
emission factor final fuel consumption, according to ifeu 2010.
Approach for monetisation:
Road traffic, Bus service/Railway traffic:
t C02 x INR/ t C02
Input Data
Road traffic:
Fleet mix.
Mileage taken from traffic and transport models, divided into vehicle types, road links
and traffic situations per road link.
C02-emissison factors by vehicle types (final fuel consumption), road links and traffic
situations taken from Keller et al. (2010). See explanation in chapter 5.
Efficiency factor to compute total emissions taken from ifeu 2010 (default values
included in Excel-dossier).
Bus service:
Fleet mix.
Mileage taken from transport model, data from provider or detailed analyses of specific
transport routes, depending on application case
C02-emissison factors (final consumption) taken from Keller et al. (2010). Default values
for different vehicle types and traffic situations given in Excel-dossier.
Efficiency factor to compute total emissions taken from ifeu 2010 (default values
included in Excel-dossier).
Railway traffic:
Fleet mix.
Mileage taken from transport model, data from provider, detailed analyses of specific
transport routes, depending on application case.
C02-emissison factors (final consumption) taken from ifeu 2010.
Efficiency factor to compute total emissions taken from ifeu 2010 (default values
included in Excel-dossier).
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Description of Indicators
Cost rate for monetisation:
There are several studies dealing with marginal costs of CO2-emissions. Due to difficulties in
terms of forecasting impacts of climate change these studies offer a broad bandwith of
marginal costs. In order to avoid overestimations of these costs, the cost rate of 70,00 Euros
per ton CO2 stated in BMVBS (2010) was chosen. This cost rate is in line with other wellestablished European studies such as Maibach et al. (2008). Euros are converted into INR
by average exchange rates in 2009.
Sources:
Fleet mix: vehicle technology surveys (e.g. respective parking surveys), data from
Pollution Control Board etc.
BMVBS (2010): Aktualisierung von Bewertungsansätzen für
Wirtschaftlichkeitsuntersuchungen in der Bundesverkehrswegeplanung,
Essen/Freiburg/München.
ifeu 2010: EcoPassenger, Environmental Methodology and Data, commissioned by
International Union of Railways (UIC).
Keller et al. (2010): Handbook emission factors for road transport (version 3.1), Bern:
2010.
See explanation in chapter 5.
Maibach et al. (2008): Handbook on estimation of external costs in the transport sector,
Delft: 2008.
Excel-dossier has default-value included to calculate upstream values.
HEATCO (2006): Deliverable 5: Proposal for harmonised guidelines, published within
HEATCO – Developing Harmonised European Approaches for Transport Costing and
Project Assessment, second revision, 2006
Additional information
Alternative approach to the embedded emission modelling in the transport model (VISUMbased) is to set-up a tool where the emissions get calculated external to the transport model
but with output from the model regarding vehicle-km and to some extent traffic situations.
Without consideration of the traffic situation, the impacts of measures that target to improve
the traffic flow cannot be distinguished. For more information see chapter 5.
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Description of Indicators
4.1.3
Air Pollutant N0x
4.1.4
Air Pollutant PM 10
Relevance of indicator
Air pollution is a major problem for the health of urban population and the quality and
subsequently the attractiveness of a city.
Basically outdoor activities should be associated with an absolute minimum risk of damage
to health. Nevertheless human beings are exposed to extended air pollution by traffic
especially next to roads with high traffic volumes. In case of traffic disruptions or detours due
to precipitation events or inadequate road infrastructure the emissions can get even higher
than normal level. Consequently esp. neighbourhoods of major corridors are exposed to
massive noise and air pollution; non-motorized traffic modes are repressed and usually very
unattractive.
Major pollutants taken into consideration in this assessment procedure are: NOX and PM10
emissions.
NOx affects human beings, constructions as well as plants harmfully. This pollutant is the
main cause for ground level ozone and acid rain.
PM affects human beings in terms of cardiovascular diseases and respiratory tract
infections. Furthermore PM has carcinogenic effects.
To compare environmental impacts of a whole transport system the total energy chain has
to be considered as different modes have very different energy-efficiency and subsequently
emission levels.
The complete chain comprises:
Exploration of primary energy carriers,
Conversion within power plant or refinery,
Energy distribution (transforming and cable losses/electricity; transport to petrol
station/fuel),
Final energy consumption (=direct consumption).
Range of application
Adaptation Planning – PrT and PuT
Mitigation Planning – PrT and PuT
Assessment approach
MeasurandAnalysis case – measurandBase case
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Description of Indicators
Measurand
Measurand:
Modes:
[kg NOx or PM10/year]
PrT and PuT
Calculation rules
See indicator GHG-Emissions
Approach for monetisation:
See indicator GHG-Emissions
Input Data
Cost rates for monetarisation:
NOx: cost rate (5.060 Euro per ton NOX) derived from HEATCO (2006).
PM: cost rate (29.990 Euro per ton PM10) derived from HEATCO (2006).
Euros are converted into INR by average exchange rates 2009.
Sources:
HEATCO (2006): Deliverable 5: Proposal for harmonised guidelines, published within
HEATCO – Developing Harmonised European Approaches for Transport Costing and
Project Assessment, second revision, 2006
Additional information
Alternative approach to the embedded emission modelling in the transport model (VISUMbased) is to set-up a tool where the emissions get calculated external to the transport model
but with output from the model regarding vehicle-km and to some extend traffic situations.
Without consideration of the traffic situation, the impacts of measures that target to improve
the traffic flow cannot be distinguished. For more information see chapter 5.
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Description of Indicators
4.1.5
Vehicle Operation Costs (VOC)
Relevance of indicator
Infrastructure measures can lead to reduced vehicle operation costs. These are the major
economic benefits for road users and therefore taken into consideration by most
assessments of transport projects. Vehicle operation costs are often considered separately
for distance-related and time-related components.
Additionally transit related projects can also cause reductions in operation costs, e.g. when
circulation frequencies can be optimised and are therefore also considered.
Range of application
Adaptation Planning – PrT and PuT
Mitigation Planning – PrT and PuT
Assessment approach
MeasurandAnalysis case – measurandBase case
Measurand
Measurand:
Modes:
[INR /year]
PrT and PuT
Calculation rules
Road traffic (2-Wheeler, 3-Wheeler, 4-Wheeler):
VOC (distance-related and time-related) per vehicle type & road type x congestion factor
x respective veh-km, according to IRC 2009.
VOC of IRC are for free flow conditions, therefore congestion factor only needs to be
applied for congested situations.
Bus service:
VOC (distance-related and time-related) per vehicle type & road type x congestion factor
x respective veh-km, according to IRC 2009.
VOC of IRC are for free flow conditions, therefore congestion factor only needs to be
applied for congested situations.
Railway traffic:
VOC (distance-related and time-related) per vehicle type x veh-km, according to own
calculations based on ratio between bus services and rail services derived FSV 2010.
Approach for monetarisation:
Not necessary, indicator is already monetised
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Description of Indicators
Input Data
Road traffic/Bus service:
Distance-related and time-related VOC - without fuel costs - for different vehicle types
(2-Wheeler, 3-Wheeler, Cars and busses), road categories and V/C-Ratios, according to
IRC 2009.
As IRC 2009 does not provide factors for 3-Wheeler, the used vales for 3-Wheeler,
which have a high mode share in Hyderabad and therefore need to be considered, were
interpolated between 2-Wheeler and Cars. Default values for road type and roughness
(RF) is given in Excel-dossier.
Definition of free flow condition according to TRB 2010.
Congestion factors for different V/C-ratios.
Mileage and V/C-ratios, taken from traffic and transport models, according to
requirements of IRC 2009.
Railway traffic:
VOC for different vehicle types. Default values are given in Excel-dossier.
Mileage taken from transport model, data from provider, detailed analyses of specific
transport routes, depending on application case.
Sources:
IRC 2009: Manual on Economic Evaluation of Highway Projects in India, second
revision, IRC:SP:30-2009, New Delhi.
FSV 2010: Entscheidungshilfen Nutzen-Kosten-Untersuchungen im Verkehrswesen
RVS 02.01.22.
Additional information
Congestion factor is only necessary to apply in saturated traffic conditions or worse, not in
free flow conditions and only for road based transport, not rail based.
Vehicle operation costs do not include maintenance cost for fixed infrastructure – this is part
of the cost and not benefit calculation -, energy costs and passenger time costs. The latter
two impacts are taken into account with the indicators “Energy consumption” and “Travel
Time”.
Please note: Operation costs for public transport are difficult to obtain as they are relevant to
competition and therefore confidential. And if they are available but from different sources it
is difficult to compare them, as one rarely get details on the cost elements that were
considered in the respective calculation. However, no viable data on operation costs for rail
based public transport in India were accessible at the time of the study. Therefore
assumption on the ratio between bus services and rail services were made to arrive at
vehicle operation costs of rail based services that are comparable to the road based VOC of
IRC.
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Description of Indicators
4.1.6
Travel Time
Relevance of indicator
Reduced travel times are major economic benefits for road users and therefore taken into
consideration by most assessments of transport projects.
Range of application
Adaptation Planning – PrT and PuT
Mitigation Planning – PrT and PuT
Assessment approach
MeasurandAnalysis case – measurandBase case
Measurand
Measurand:
Modes:
[INR /year]
PrT and PuT
Calculation rules
Road traffic/Bus service/railway traffic:
PrT: veh-h x occupancy rate for different vehicle types, according to modal spilt. Veh-h
from transport model already reflects different traffic situations.
Bus: veh-h x occupancy rates for average traffic situations (speeds peak/off-peak) and
occupancy rates factors (peak/off-peak).
Approach for monetarisation:
Pers.-h x value of time [INR/h] for different vehicle classes (Prt and PuT).
Input Data
Road traffic/Bus service/railway traffic:
Veh-h from transport model for different vehicle types and traffic situations.
Occupancy rates:
if possible local data should be use, e.g. from surveys or information given by PuTprovider. In the case of Hyderabad we have used road traffic data from an OD-survey
carried out in a study in Hyderabad (ESCI 2009) and information given by PuT-provider.
Value of Times, according IRC 2009, price level 2009
As IRC does not give values for 3W, MMTS and MRTS, assumption (based on fare
structure) is: 3-Wheeler = bus deluxe (43,5 INR), MMTS = ordinary bus (39,5), MRTS=
bus deluxe (43,5 INR).
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Description of Indicators
Sources:
ESCI 2009: Project on Small Scale Traffic Management Schemes, Final Report,
Hyderabad.
IRC 2009: Manual on Economic Evaluation of Highway Projects in India, second
revision, IRC:SP:30-2009, New Delhi.
Additional information
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Description of Indicators
4.2
KO-Indicator (only Application Case “Adaptation
Planning”)
4.2.1
Cut-off Crucial Civic Infrastructure
Relevance of indicator
It is vital for the functioning of a city that important civic infrastructures like hospitals or fire
brigades are not cut-off from the transport infrastructure.
Therefore this information needs to be taken into account, when designing adaptation plans.
Range of application
Adaptation Planning – yes
Mitigation Planning – yes
Assessment approach
Assessment of situation when event occurs
Measurand
Measurand:
No. of cut-off infrastructures by event
Calculation rules
The impact has to be assessed by analysing if any important civic infrastructure is likely to
be cut-off in the event of flooding.
If this case occurs, the location has to be reconstructed in a way that the civic infrastructure
is accessible even in case of an event. Or an alternative, resilient approach to the civic
infrastructure has to be constructed.
Input data
Information on locations of important civic infrastructure near to the location. These can
be gained from google maps for the actual situation and forecasted based on the actual
situation and by land-use plans (future situation).
Information on availability of alternative accesses to infrastructure, that is not affected by
event
Additional information
The use-case of Hyderabad showed that it is very unlikely that in a city with a dense
transport network civic infrastructure is cut-off completely.
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Description of Indicators
4.3
4.3.1
Non-monetisable Indicators (only Application Case
“Adaptation Planning”)
Affected Sensitive Areas
Relevance of indicator
Residential areas or areas where hospitals are located are sensitive areas in a way that
additional traffic in these areas will result in reduced road safety. This is especially crucial for
the children playing on residential roads. Increased noise has also negative impacts on
residents in general but especially on the recovering process of patients. Therefore this
information should be taken into account, when designing adaptation plans.
Range of application
Adaptation Planning – yes
Mitigation Planning – no
Assessment approach
Assessment of situation when event occurs
Measurand
Measurand:
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km of road in of sensitive areas with additional traffic when event occurs
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Description of Indicators
Calculation rules
Identification of km of roads in sensitive areas, where additional traffic caused by the
flood event occurs.
Transformation of km of roads into benefit points by using the provided linear utility
function. Provision of left and right scale limits can only be made after calculation of
impacts for all analysis cases. Scale limit to be included in highest result (as plus/and
minus value).
Assumption on severity of impact in case of flooding (weighting factor)
Formalised
Assumption
+1
high positive impact
+0,5
positive impact
0
No relevant impact
-0,5
negative impact
-1
high negative impact
Explanations for formalised assessment
Decrease in traffic will not be assessed in this indicator as positive effect, as it means
that residents do not get to their homes or can’t leave their homes. This actually
negative effect is considered in the indicator travel times.
See above
Minimal increase of additional traffic in sensitive areas.
(no road has increase higher than 5% compared to base case)
Medium increase in additional traffic in sensitive areas.
(no road has increase higher than 15% compared to base case)
High increase in additional traffic in sensitive areas.
(no road has increase higher than 20% compared to base case)
The assessment result is gained by multiplying the different single values to weighted
benefit points, which represents the difference to the base case.
Input data
Information on locations of sensitive areas near to the location or along of deviation
routes. These can be gained from google maps for the actual situation and forecasted
based on the actual situation and land-use plans (future situation).
Information on deviation routes and amount of deviated traffic can be gained from
transport model
Additional information
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Description of Indicators
4.3.2
Affected Shops, Offices
Relevance of indicator
Commercial activities are very vital for a cities well-being and attractiveness. Increasing
disturbances of commercial activities in events of floodings can lead to economic losses,
when customers cannot reach the affected shops or employees cannot come to work in
time. Therefore this information should be taken into account, when designing adaptation
plans.
Range of application
Adaptation Planning – yes
Mitigation Planning – no
Assessment approach
Assessment of situation when event occurs
Measurand
Measurand:
number of affected shops and offices
Calculation rules
Identification of shops and offices, which will be affected when flood event occurs.
Transformation of no. of shops and offices into benefit points by using the provided
linear utility function. Provision of left and right scale limits can only be made after
calculation of impacts for all analysis cases. Scale limit to be included in highest result
(as plus/and minus value).
Assumption on severity of impact in case of flooding (weighting factor)
Formalised
Assumption
+1
high positive impact
+0,5
positive impact
0
No relevant impact
-0,5
negative impact
Explanations for formalised assessment
Increase in customer traffic or consumption or higher productivity is not likely to occur
due to a rain event. Hence positive impacts will rarely need to be considered.
See above
Minimal disruption on customer traffic or working hours/productivity of employees in
affected shops or offices.
Or disruptions expected in some shops or offices can be compensated by shops which
might have higher customer traffic or more consumption.
This might be the case when hotels are situated in the affected area and customers
might stay longer and consume more.
No closure time for renovating is necessary.
Medium disruption on customer traffic or working hours/productivity of employees in
affected shops or offices.
It is expected that up to 50% of customers traffic or productivity losses can occur.
No closure time for renovating is necessary.
-1
high negative impact
High disruption on customer traffic or working hours/productivity of employees in
affected shops or offices.
It is expected that up to 100% of customers traffic or productivity losses can occur.
Maybe closure time for renovations are necessary.
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Description of Indicators
The assessment result is gained by multiplying the different single values to weighted
benefit points, which represents the difference to the base case.
Input data
Information on shops and offices near to the location. This can be gained from google
maps for the actual situation and forecasted based on the actual situation and land-use
plans (future situation).
Additional information
4.3.3
Affected Street Vendors
Relevance of indicator
Street vending is an important source of regular income for people that do not fit into the
formal economy. And street vendors are very vulnerable to income losses, as their income
is very low and on day-to-day basis.
Extreme weather events affect them in two ways: Firstly in the time, when the rain event
occurs as most of the times they have no proper protection against the rainfall. Secondly
when the “shop” is not accessible to the customers due to flooded roads. Although the
equipment of street vendors is basically very flexible, it is difficult for them to change their
selling locations as most of them have fixed locations and regular customers. So even if
changing the selling location would be physically possibly it is still associated with income
losses.
Therefore the possible effects on street vendors should be taken into account, when
designing adaptation plans.
Range of application
Adaptation Planning – yes
Mitigation Planning – no
Assessment approach
Assessment of situation when event occurs
Measurand
Measurand:
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number of affected street vendors
 PTV AG Jun-13
Description of Indicators
Calculation rules
Identification of no. of street vendors, which will be affected when flood event occurs.
Transformation of no. of street vendors into impact points by using the provided linear
utility function. Provision of left and right scale limits can only be made after calculation
of impacts for all analysis cases. Scale limit to be included in highest result (as plus/and
minus value).
Assumption on severity of impact in case of flooding (weighting factor) according to
scheme below:
Formalised
Assumption
+1
high positive impact
+0,5
positive impact
0
No relevant impact
-0,5
negative impact
-1
high negative impact
Explanations for formalised assessment
Increase in customer traffic or consumption is not likely to occur due to a rain event.
Hence positive impacts will rarely need to be considered.
See above
Minimal disruptions on customer traffic.
Medium disruptions on customer traffic.
It is expected that up to 50% of customers traffic losses can occur.
High disruption on customer traffic occurs per hour.
It is expected that up to 100% of customers traffic or productivity losses can occur.
The assessment result is gained by multiplying the different single values to weighted
benefit points, which represents the difference to the base case.
Input data
Information on street vendors adjacent to the flooded location. This information can only
be gained by surveys and only for the actual situation. For future situations, assumptions
need to be made based on the actual situation and existing plans of the municipal
community on zoning laws for street vendors, which might restrict their activities in
certain areas.
Additional information
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Description of Indicators
4.3.4
Affected Pedestrians and Cyclists
Relevance of indicator
Pedestrians and Cyclists are very vulnerable to rain events on the one side. But they are
also important for a sustainable transport system as they are very energy-efficient,
emission-free and quiet. To encourage these modes it is necessary to improve the
infrastructure in a way that it is not affected negatively by extreme weather events.
Therefore the information on the importance of the location for pedestrians and cyclists
should be taken into account, when designing adaptation plans.
Range of application
Adaptation Planning – yes
Mitigation Planning – no
Assessment approach
Assessment of situation when event occurs
Measurand
Measurand:
number of affected pedestrians and cyclists
Calculation rules
Quantification of no. pedestrians and cyclists, which will be affected when flood event
occurs.
Transformation of no. of pedestrian and cyclists into benefit points by using the provided
linear utility function. Provision of left and right scale limits can only be made after
calculation of impacts for all analysis cases. Scale limit to be included in highest result
(as plus/and minus value).
Assumption on severity of impact in case of flooding (weighting factor) according to
scheme below
Formalised
Assumption
+1
high positive impact
+0,5
positive impact
0
No relevant impact
-0,5
negative impact
-1
high negative impact
Explanations for formalised assessment
Increase in number of pedestrians and cyclists is not likely to occur due to a rain event.
Hence positive impacts will rarely need to be considered.
See above
Minimal disruptions for pedestrians and cyclists.
Medium disruptions for pedestrians and cyclists.
It is expected that up to 50% of pedestrians and cyclists face negative effects.
High disruptions on customers traffic occurs per hour.
It is expected that up to 100% pedestrians and cyclists face negative effects.
The assessment result is gained by multiplying the different single values to weighted
benefit points, which represents the difference to the base case.
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Description of Indicators
Input data
Information on pedestrians and cyclists traveling on the affected flooded locations. This
information can only be gained by surveys and only for the actual situation. For future
situations, assumptions need to be made based on the actual situation and existing
policies or strategies of the municipality to promote walking and/or cycling.
Additional information
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Information on Calculation of Energy Consumption and Emissions
5
Information on Calculation of Energy Consumption and
Emissions
5.1
Rational
The following chapter gives a short introduction in rational and methodology on calculation of
energy consumption and emissions (greenhouse gas and air pollutants like N0x).
There are two main reasons why transport systems should be energy-efficient and low
carbon (emitting only little greenhouse gasses).
(1) Finiteness of non-renewable energy sources: So far most energy used by humans in
whatever context is based on non-renewable sources like oil or gas (fossil fuels). As it
is unlikely that a 100 % shift to renewable energy source will be possible in all
sectors, it is vital to reduce the energy consumption as much as possible.
Besides this, most countries depend on fossil fuel import to meet their energy
demand, so energy-efficiency is also a question of economic independencies. E.g. in
2030 it is expected that India will have to import 53% of the countries’ total energy
consumption (see also chapter 4.1.1)
Some sectors are already reducing their overall energy consumption by technological
improvements and are shifting to renewable energy sources as well. However in the
transport sector gained reductions by technical improvements are eaten up by
increasing motorised transport volumes. And an adequate shift to renewable sources
of energy for transport is not happening in the mid-term perspective. So especially in
the transport sector reduction of energy consumption is an important contribution to
reduce dependency on finite energy sources.
(2) Climate Change (CC) and Ambient Air Quality: The consumption of non-renewable
energy sources is directly linked to the production of greenhouse gas emissions and
air pollutants.
Continuous mitigation of climate change is important because results of CC – more
extreme weather events like floods, draughts or heat waves – will again affect the
transport systems and subsequently the quality of people’s lives and economies
negatively.
In order to design future proofed, resilient and energy-efficient transport systems, it is vital to
evaluate the impacts related with transport strategies and infrastructure plans in an early
design stage and for all motorised modes of transport (ex ante analysis). This is done by
calculating/forecasting of fuel consumptions, GHG-emissions and air pollutants from Well-toWheel.
Energy-efficient or low carbon transport systems are mostly characterised by good public
transport systems and facilities for cyclers and pedestrians. This aims at weakening the
individual motorized transport, which is the most energy-inefficient mode of transport. This
can be seen in Image 12 below, that shows a comparison of C02-emissions of different
transport modes, taking different load factors into consideration.
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Image 12:
Specific C02-emissions of different modes,
Source: ifeu 2008
Besides reduction of fossil fuel consumption and GHG-emissions, low carbon transport
systems have many co-benefits, like better road safety, better air quality etc. See Image 13
below for more of the co-benefits.
Image 13:
Co-Benefits of energy-efficient/low carbon transport, Source: GTZ 2009
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5.2
Approach
To design an energy-efficient transport system all modes have to be included in the ex ante
analysis of energy consumption or GHG-emissions related to different strategies.
Ideally an impact analysis of transport systems should include the impacts linked to vehicle
operations and to fuel production for the full life cycle of all components, because for every
step energy is required. This includes
Production, maintenance and disposal of vehicles
Construction, maintenance and disposal of transport infrastructure
Additional resource consumption like administration buildings, stations, airports
Emissions caused by energy production
extraction of fuels from ground deposits or generation of renewable energies (energy production)
conversion by refining or power generation (incl. construction and disposal of refineries and power
stations)
Emissions caused by energy distribution
Emissions directly caused by the operation of vehicles (= final energy consumption)
However due to data availability the approach use here is restricted to the quantification of
emissions and fuel consumption
directly caused by the operation of vehicles (all types) and
related to the generation of final energy (all fuels and electricity).
This covers the full energy supply (= total energy chain) of vehicles which is called: Well-toWheel (WTW). Image 14 gives an overview on the different life cycle elements as well as the
elements included in the approach.
Image 14:
System boundaries, Source: own graphic
This Well-to-Wheel (= full energy supply chain) approach is important to compare the
impacts of transport processes with different energy carriers. Energy consumption over the
total energy chain depends on the efficiency of the individual steps of the chain which vary
tremendously between the different carriers. The following image displays schematically the
contribution of each step of the energy supply chain. It clearly shows that if electricity is used,
about 2/3 of energy is required for conversion and transformation. This may vary depending
on the energy mix used as input for the electricity generation (coal, oil, gas, nuclear power,
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renewables etc.) or the efficiency of the power plants in use (combined heat and power etc.)
but it will never get close to the conversion share of fuels. Hence it is important not only to
compare the final energy use, but the full energy chain.
Image 15:
5.3
Distribution of energy consumption Well-to-Wheel, Source: ifeu 2010
Selection of Consumption and Emission Factors
The main influencing factors for fuel/energy consumption and emissions are:
Fuel type / energy mix
Fuel quality: this relates esp. to air pollutants (Fuel emission standards /e.g. Bharata in
India)
Vehicle type age and condition: well- maintained vehicles need less fuel
Road category (allowed speed)
Traffic situation (average speed, percentage of stops, Level of service)
Use of air condition
Number of cold starts per day/engine temperature
Use of air condition
Altitude
Gradient
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Fuel consumption, GHG-emissions and air pollution are usually calculated based on
emission models. They usually provide consumption and emission factors (g/vehicle-km)
gained by different methodologies and taking different influencing factors into account. The
models are normally calibrated by data from engine test benches. In the context of the
research project the emission factors provided for road transport from the Automotive
Research Association of India (ARAI, 2007) as well as from the Handbook emission factors
for road transport (HBEFA, Keller et. al 2010) were analysed, to decide which one is the
most appropriate model for the modeling of road transport. Full descriptions of the different
emission models are available at the mentioned literature sources. The comparison of the
main features is displayed in Image 16 below.
The analysis showed that both models have advantages but also disadvantages. Main
advantage of the ARAI model is that it reflects the Indian fleet more accurate than the Europe
based HBEFA but only for emission standards up to Euro III. Surveys carried out in
Hyderabad on in-use vehicle technologies showed that a high proportion of vehicles comply
already to norms higher than Euro III. But the main disadvantages of the ARAI model are,
that the emission factors do not reflect different traffic situations and that the applied driving
cycle for the work bench test (Indian Driving Cycle) is not appropriate for urban conditions.
Therefore the HBEFA model was chosen, although this model does not include the full range
of Indian vehicles especially not the important vehicle type of 3-Wheelers. This was
compensated by associating 3-Wheelers according to their engine power either to
motorcycles (small engines) or to cars with small engines (“bigger” engines, Diesel, CNG or
LPG-driven 3-Wheelers).
Image 16:
Comparison of the main features of emission models HBEFA and ARAI for road transport
To arrive at the comprehensive transport emission model, consumption and emission factors
for the rail-based transport are also necessary. In the absence of any corresponding model in
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India, this mode was covered by factors from a study called EcoPassenger, which was
commissioned in 2010 by the International Union of Railways (UIC) to ifeu.
Image 17 gives an overview on all components of the multi-modal emission-model used in
the presented approach including all data sources.
Image 17:
Data sources for developed emission model
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