Prepared By: Emergent Venture India
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User Guide Tool for Assessment of carbon footprint of Urban transport
Prepared by: Emergent Venture India
Submitted to: Institute of Urban Transport
A Shakti Sustainable Energy Foundation Supported Study
Emergent Ventures India Pvt. Ltd LIST OF ABBREVIATIONS
ADB Asian Development Bank ARAI Automotive Research Association of India BAU Business As Usual BRTS Bus Rapid Transit Systems CMP Comprehensive Mobility Plan CO2 Carbon dioxide GEF Grid Emission Factor GOI Government of India GHG Green House Gas HOV High Occupancy Vehicles ICT Information and Communication Technology IPCC Intergovernmental Panel on Climate Change IPT Intermediate Public Transport IUT Institute of Urban Transport, India JNNURM Jawaharlal Nehru National Urban Renewal Mission KM Kilometre LOS Level of Service MRTS Metro Rapid Transport Systems NAPCC National Action Panel on Climate Change NUTP National Urban Transport Policy PT Public Transport PV Private Vehicles PPKM Per Passenger Kilo Meter SOV Single Occupancy Vehicles TCO2 Tonnes of CO2 TEEMP Transport Emission Evaluation Model for Projects TDM Transport Development Manager UNFCCC United Nations Framework Convention on Climate Change
Emergent Ventures India Pvt. Ltd Table of Contents
1. Introduction to tool for Assessment of carbon footprint from City Transport...... 4 2. About the User Guide...... 4 3. Guide for City Officials to use the tool...... 5 3.1 Details of scenarios built into the tool...... 5 3.2 Sub-scenario built into the tool...... 6 3.3 Impact of Induced Demand...... 6 3.4 Guide to change default values for Modified Scenarios and Additional Green Measures...... 7 3.5 Overview of the Output Result...... 8 3.6 Details of the Output Result...... 9 3.7 Results analysis and appropriate action on Results...... 9 4. Guide for Transport Planners for Detailed Assessment...... 12 4.1 Guidance for data input from CMP...... 12 4.2 Sub-scenario selection i.e. Well to Wheel or Gate to Wheel...... 18 4.3 Assessment of Induced Demand for Input...... 18 4.4 Guide to change default values for vehicle, metro and electricity (grid) related emission factors for Baseline and 2030 scenario...... 19 4.5 Guide to change default values for modified scenarios and additional green measures...... 22 4.6 Guide to change default values for Fixed Parameters...... 23 Annex 1: Structure of the tool...... 25 Annex 2: Parameters that are variable and are based on City Characteristics...... 26 Annex 3: Parameter that are fixed but may change over time due to technology improvements ...... 31 Annex 4: Default values for Measures / Assumptions to mitigate emissions...... 33 Annex 5: Guide for Assessment of Generated Traffic...... 36
Emergent Ventures India Pvt. Ltd 1. Introduction to tool for Assessment of carbon footprint from City Transport
The ‘Assessment of carbon footprint’ Tool is an MS-Excel based tool for estimating the greenhouse gas emission from urban transport. The tool can be used to calculate GHG emission from alternative scenarios. Assessment of carbon footprint is essential to achieve the overall objective of mitigation of climate change impact.
The transport sector in India is the third fastest growing sector for GHG emissions after electricity generation and cement production. India’s current transport emissions are 7.5% of country’s total emissions which are forecast to grow to 15% by 20251 increasing at 4.5% Cumulative Annual Growth Rate (Source: IPCC). With 87% of current transport related GHG emissions coming from road transportation, the road transportation becomes the key area to target for emissions reduction for India to achieve its voluntary emissions reduction goals.
The National Urban Transport Policy (NUTP), 2006 focuses on urban transportation as an important parameter at the urban planning stage, therefore better integration of land use and transport planning, greater use of public transport, use of cleaner technologies, capacity building, road safety and reducing pollution levels through changes in travelling practices among others. As a result, many Indian cities are developing Comprehensive Mobility Plans (CMPs) to meet the future transport demand of their cities.
Assessment of carbon footprint has not been included as one of the parameters in the guide for preparing Comprehensive Mobility Plans. Since all major Indian cities are developing their CMPs, factoring carbon emission reduction and sustainability will lead to the Indian urban transport sector to contribute significantly in achieving India’s GHG emission reduction target.
Therefore, the core objective of developing this tool is to assist city planners and policy makers to analyse CMP with respect to its climate change impact and derive meaningful comparisons with alternative low carbon transport scenarios.
The tool at present does not include the impact of fright traffic.
2. About the User Guide
The user guide is prepared to help city planners in effectively using the tool in their urban transport plans. It first describes the structure of the tool explaining the various sheets in the tool and their purpose. It then provides guidance to city officials on the use and application of the tool. It further provides greater details so that the designer can also use this tool to develop better CMPs. Finally, it highlights the scope of improving the CMPs for their impact on climate change.
1 India: Greenhouse Gas Emissions 2007, Ministry of Environment and Forests, Government of India
Emergent Ventures India Pvt. Ltd 3. Guide for City Officials to use the tool
3.1 Details of scenarios built into the tool
The tool provides the greenhouse gas emissions for a city for three scenarios i.e. “Current Baseline”, “Business as Usual in 2030” and “CMP implementation scenario in 2030”. The traffic data for these scenarios would have been input by the planner. Some additional measures may be possible over and above those included in the CMP to reduce GHG emissions. These along with the 3 basic scenarios are listed in the table below:
S. Scenario Name Scenario Details No
0 Present scenario Baseline Scenario which shows city’s current GHG emissions from passenger urban transport 1 Business as Usual (BAU) Emission in 2030 under BAU by assuming demand growth rate as per CMP
2 CMP As-Is Scenario Emissions in 2030 after assuming CMP interventions have been implemented. This includes construction emissions resulting from construction of various CMP measures.
3 CMP Modified Assumption Emissions in 2030 from city transport post implementation of Scenario CMP measures considering induced demand generated due to rebound effect if not included in CMP but a well established phenomenon.
4 CMP with Additional Scenario 3 + CMP with additional (a) demand elimination Measures (CMP_AM) measures (b) Modal shift measures.
5 (CMP_AM)-Load Factor Scenario 4 + CMP with (c) Load Factor improvement Improvement
6 (CMP_AM)-Green Fuels, Scenario 5 + CMP with (d) Green Fuels, Eco-Driving and Eco-Driving & Renewable Renewable Energy for MRTS Energy for Metro
7 CMP with Electric Mobility Scenario 6+ CMP with (e) 10% Electric Vehicle Penetration Scenario -10% Electric Vehicle penetration
Table 2: Scenarios built into the Tool
For scenarios 3 to 7, default values have been input into the tool. The city official may use scenario 3 to 7 if not included in the CMP. The default data for these scenarios is already inserted which can be modified to suit city’s specific characteristic. The results of these 7 scenarios are presented in 2 sub scenarios – well to wheel and gate to wheel which is described in next section.
Emergent Ventures India Pvt. Ltd 3.2 Sub-scenario built into the tool There are two sub scenarios in the overall analysis - Well to Wheel and Gate to Wheel (Local).
Well to wheel scenario considers emissions over the entire life cycle of the fuel e.g. in case of petrol/diesel it will also include emissions made during the extraction, transport, refining, delivery to pump and then combustion. Similarly for electricity, a well to wheel will include emissions made during the fuel extraction (coal etc), transportation, combustion at power plant, losses in transmission and distribution etc.
Similarly, a Gate to Wheel scenario only deals with emissions made during the fuel combustion process. The tool will automatically use emissions factors (default data) as appropriate for sub scenario selected. For electricity, gate to wheel will include emissions made only at power plant.
The city official can made the Well to Wheel or Gate to Wheel sub scenario selection in the “Scenario and CMP Data” Worksheet Cell - E5
3.3 Impact of Induced Demand If road capacity increases, the number of peak-period trips also increases until congestion reaches the original level. The additional travel is called “generated traffic.”
Research has conclusively proven that the concept of Generated Traffic exists. It consists of two main components
1) Diverted vehicle traffic
trips shifted in time
route and
destination
2) Induced vehicle traffic
shifts from other modes,
longer trips and
new vehicle trips
Research indicates that generated traffic fills the capacity added over a period of 5-10 years. The benefits of new capacity additions are soon lost. CMP’s made in current form sometimes do not take “generated traffic” into account leading to exaggerate future traffic speeds. The city official can input the traffic elasticity by overriding the default
Emergent Ventures India Pvt. Ltd value provided in the tool (Refer Annex 5 for more details on induced vehicle demand).
3.4 Guide to change default values for modified scenarios and additional green measures These are the measures proposed to improve effectiveness of CMP. CMP may include suggestion based on the trends in the traffic on demand elimination measures using land-use.
Certain measures include promotion of Internet and Communication technologies (ICT) to reduce travel demand. A list of various measures has been discussed in the table 5 given at the end of this chapter.
1. Trips that can be totally avoided through improved land use and other measures. To estimate the overall impact of both the measures it has been assumed that first reduction will happen and then remaining trips will be eliminated using improved land use.
Trips Reduction Percentage - Other measures (ICT etc) 10% Trips elimination using Improve Land Use 25%
2. Additional modal shifts from PV or IPT to public transport
Users can add the percentage of trips that can be shifted to Public Transport (PT) using additional modal shift measures.
Additional Modal Shift From PV/IPT To PT % 10%
3. Additional Green Measures – Electric Vehicles and Eco Driving
Designer can also understand the impact of measures by changing default values of additional measures that has been modeled into the tool.
Additional Green Measures Emission reduction due to Eco-Driving 20% Eco Diving Adoption Rate 15% Electric Vehicle Emission factor per km per passenger as a fraction of gasoline vehicle 0.96 Electric Vehicle Penetration 10% Initial Travel Demand 100%
Emergent Ventures India Pvt. Ltd Detailed modeling has been done to estimate improvements using load factor improvement of metro systems only. Consultants interested in the detailed modeling can see it the calculations worksheet.
3.5 Overview of the Output Result The results can be obtained from the tool through the sheet named: “Results” as shown in the graph below:
Table 3: Results
Reduction in Emissions in 2031 over BAU Well to Wheel Sub-Scenario Tonnes of CO2 (%) Emissions in Baseline Scenario “0” (year 2009) 4,01,044 Emissions in Business As Usual Scenario “1” (year 2030) 15,31,759 Emissions in CMP As Is Scenario “2” 13,77,647 10.1% Emissions in CMP Modified Assumption Scenario “3” 16,19,038 -5.7% CMP AM - Demand elimination & Modal Shift Measures “4” 12,82,632 16.3% CMP AM - Load Factor Improvement “5” 12,54,109 18.1% CMP AM - Green Fuels, Eco Driving, 20% renewable for Metro “6” 10,65,676 30.4% CMP AM - Additional green measures “7” 10,53,668 31.2% Overall CMP Additional Measures Improvement over CMP on comparative boundaries 10,53,668 31.2%
Figure 1: Overall Graphical Results
Emergent Ventures India Pvt. Ltd 3.6 Details of the Output Result
The overall result for scenario “0” to “4” is shown duly broken up into UT components in the figure below.
Figure 2: Breakup of results UT component wise
Emergent Ventures India Pvt. Ltd The cities may obtain the results of other scenarios (5, 6 and 7) is the table below.
Note: The figures are shown for a case study and may be ignored.
Table 4: Impact of scenarios 5, 6 and 7
Overall Impact of Results of Additional Measures impact measure Net Emissions Load factor improvement (Scenario 5) 2.2% 12,54,109 Other green measures (Scenario 6) 15.0% 10,65,676 Electric Cars (Scenario 7) 1.1% 10,53,668 17.6% Net Additional Measures Impact
3.7 Results analysis and appropriate action on results
The city official can use these results to identify steps to mitigate GHG emissions. For example, contribution to GHG emissions is high by MRTS then possibilities of reducing the same can be considered. Table 5 given on the next page, lists possible steps for this purpose.
Emergent Ventures India Pvt. Ltd PROVISIONS IN THE CMP AND ADDITIONAL STEPS THAT CAN BE TAKEN WITH AN ESTIMATE OF THE CORRESPONDING EMISSION REDUCTION FOR A CITY Table 5
S. Additional steps Emission Measure Provision in CMP No possible Reduction MODAL SHIFT TO PUBLIC TRANSPORT through USER- FRIENDLY FEATURES Public transport load factor enhancement: Matching 1 None Include 2 %2 demand to supply of buses and coaches 2 Passenger Information system None Include Increasing frequency of public 10% 3 None Include transport reduction Increasing reliability of public depending on 4 None Bus priority schemes transport GHG intensity 5 Security/Gender Friendliness None Include of public Promotional activities: Road Only safety events transport 3 6 Add more events safety , bicycle events proposed 180 Bus bays Improve ambience Note: The 7 Bus Shelters & Bus Bays proposed around bus stops analysis is Park & Ride; Cycle heavily 8 Improve Last mile connectivity lanes & Pedestrian dependent of facilities the MODAL SHIFT TO PUBLIC TRANSPORT through MANAGEMENT MEASURES performance Flexible Work Schedule to of Metro 1 reduce network congestion at None Promote systems. peak hours In specific Fiscal measures to control use Include Parking case of 2 None of personal vehicles charges, taxes etc. Jaipur, the Restricted Zones for motorized mode shift to 3 None Promote vehicles metro systems from 4 Congestion Charging None Promote private Trip Reduction; HOVs vehicles does Carpooling/Vanpooling 5 None Promote not improve measures; incentives to use GHG public transport emissions MODAL SHIFT TO PUBLIC TRANSPORT through INFRASTRUCTURAL MEASURES due to high Building new roads, Metro & Ensure Integrated multi 1 Included per Monorail modal network passenger 2 Bus rapid Transits Included Integrated network per KM emissions Ensure Minimum time 3 Intermodal terminals Included from Jaipur penalty Metro System
MODAL SHIFT TO NON MOTORIZED TRANSPORT (NMT) Included ; Proposed Ensure continuity of the Zero 1 Footpaths 1.5 -2m footpaths for pedestrian facilities emissions 110 Km mode Grade separated pedestrian One skywalk 2 crossing, Skywalks proposed
2 Appendix 5 of the main report 3 Based on Emission Evaluation Modelling using a average Metro per passenger per KM emissions factor for 3 Indian cities calculated using the electricity consumption and passenger km details from their Detailed Project Report.
Emergent Ventures India Pvt. Ltd 3 LOS guidelines None Specify Bicycle Lanes 4 Cycling Ensure continuity Included LOS Based guidelines for Up-to 30% 5 None Specify Cycleways of urban 6 Cycle Rickshaw None Integrate Cycle rickshaw trips could ROAD NETWORK IMPROVEMENT through MANAGEMENT MEASURES be shifted to 1 Freight Management System Included -- Intelligent Transport Systems 2 Included -- (ITS) 3 Off street Parking Included -- 4 Area Traffic Control Included -- 5 Pavement marking, Signage’s Included -- Improved vehicular emission ~5 % in 6 None Specify norms short term5 Rerouting traffic through less One ways have been No 7 Intensive TSM needed congested areas, proposed improvemen ROAD NETWORK IMPROVEMENT through INFRASTRUCTURAL MEASURES t in long term6 Missing links in the 1 Road Widening Included network to be done on priority To develop a road 2 Building new roads Included hierarchy & network Flyovers, Rail over bridge, NMT paths to be 3 Included Underpasses secured NMT paths to be 4 Junction Improvement Included secured DEMAND ELIMINATION AND TRANSPORT DEMAND MANAGEMENT Transport Demand management 1 None Include (TDM) Manager Transit Oriented Land Use Measures can reduce Passing References Development the carbon emissions through have been made to Multiple Nuclei Cities 2 complete elimination and land use measures Integrated offices & 20-25% 7 reduction of existing trip lengths and densification residential space City Densification ICT use (E-ticketing, E-billing, 3 None Promote Work From Home etc ) 4 Teleworking None Promote TECHNOLOGICAL/BEHAVIORAL MEASURES 1 Eco Driving campaigns None Hold more campaigns ~1%8 2 Low carbon fuels (CNG) for PT None Extend use of 8%9 Procurement 3 20% RE procurement for Metro None 4%10 recommended 4 10% Electric vehicle penetration None Electric vehicle policies 1%11
4 Appendix 9 of the main report 5 Appendix 5 of the main report 6 Based on Induced Effects trend in Table 4 of the main report 7 Section 4.3.1. Avoid/Eliminate travel demand; Page 24 of the main report 8 Modeling Results 9 Ibid 10 Ibid 11 Modelling results based on latest electric automobile technology and emission factors for Indian grid electricity; Results may vary between 1%-3 % based on grid electricity factor.
Emergent Ventures India Pvt. Ltd 4. Guide for Transport Planners for Detailed Assessment
The models developed in the tool have used numerous assumptions due to data limitation in CMPs. The following section describes how the underlying assumptions in the model can be modified by Planners involved in preparing the CMPs. The steps are as follows:
1. CMP data input
2. Sub-scenario selection
3. Induced demand elasticity input
4. Steps for changing defaults values for vehicles, metro, fuels and electricity (grid) related emissions factors.
5. Step for changing defaults values for assumptions under modified scenarios and additional green measures
6. Step for changing defaults values for variables that does not change with changing cities
4.1 Guidance for data input from CMP
The data inputs by CMP Planners in the tool are given below. This data needs to be filled in “Scenario and CMP Data worksheet”:
1. Peak Hour Trips Numbers
2. Percentage of trips made in Public Transport in CMP implementation scenario.
3. Average trip lengths and average network speed as per CMP in three scenarios during peak hour
4. Details of CMP policies (transport measures) and construction related data
5. Peak Hour Trips and Total Trips in percentage
6. Vehicle Split as per Fuel Type in base-year, BAU scenario and CMP Implemented scenario
7. Vehicle and fuel type and average occupancy
8. Design Life of CMP proposed infrastructure
Data Source
While data is available in CMP for 1 to 5, CMP Planner may have to assume a split for 6 and 7 based on information gathered during the survey. Point 8 is inserted based on normal design life of transport infrastructure in India.
Emergent Ventures India Pvt. Ltd Steps for CMP Data Input
1. Peak Hour Trip Numbers - Peak hour trip numbers for baseline scenario, CMP Business as Usual and CMP Implemented scenario needs to be added in the cells highlighted as green.
Trip 2009 2031 Information Baseline 2031 - BAU CMP Two Wheeler 82300 155220 96904 Car 29377 230280 143763 AR 19342 59340 35841 Taxi 27977 87460 52826 Total - PT 70781 101040 303999 Data Source – Sample CMP
2. Percentage of Public Transport in CMP implementation scenario.
CMP_Bus_Trips of Overall PT Trips 100320 CMP_Additional_Buses_Trips 51680 CMP_Commuter_Rail 18240 CMP_BRTS_Trips 30400 CMP_MRTS_Trips 100320 Data source – Sample CMP
3. Average trip length and average network speed as per CMP in three scenarios during peak hour
Category Baseline BAU CMP Average PV Trip Length in KMs 6 7 6 Average IPT Trip Length in KMs 7 8.5 9.5 Average PT Trip Length in KMs 8 10 13 Average Network Speed in KM/H 28 14 32 Data source – Sample CMP
4. Peak Hour Trips and Total Trips in percentage
Peak Hour to Total Trips Ratio 6.6%
Emergent Ventures India Pvt. Ltd Steps 1 to 4 are shown as Tool Screen Shot below
CMP Data Modal Shift, Trip Lengths and Network Speed
Parameter Number of Trips
Year from base 0 22 22 % of PT in CMP PT Trips Breakup Trip Information 2009 Baseline 2031 - BAU 2031 CMP TW Trips 82300 155220 96904 96904 Car Trips 29377 230280 143763 143763 AR Trips 19342 59340 35841 35841 Taxi Trips 27977 87460 52826 52826 Total - PT Trips 70781 101040 303999 % Public Transport 30.80 15.95 48.00 CMP_Bus_Trips of overall PT Trips 100320 100320 CMP Additonal Buses Trips 51680 51680 CMP Commuter Rail 18240 18240 CMP BRTS Trips 30400 30400 CMP MRTS Trips 100320 100320 Total 229777 633356 633381 630293 PV Trip Length 6 7 6 IPT Trip Length 7 8.5 9.5 PT Trip Length 8 10 13 Average Network Speed 28 14 32
Peak Hour to Total Trip Ratio 6.65% This has derived as shown in row 74 below. If you have this ratio insert in the cell C26.
5. Details of CMP policies (transport measures) and construction related data
Various transport policies or measures have been inserted into the tool which is normally found in the CMPs. Also it has been linked into the tool where these transport measures have major construction impact or not. The planner needs to insert values of transport measures phase wise planned implementation as highlighted below.
CMP Policies - Construction Related Data Major Major Construction Policy measure Impact Unit 2016 2021 2031 Impact Bus Fleet Augmentation Modal Shift No. 450 1000 1000 No BRTS Modal Shift KM 93 65 7 Yes MRTS Modal Shift KM 29 15 16 Yes NMT - Separated Grade Facility Modal Shift No. 5 6 No NMT - Bicycle Lanes Modal Shift KM 0 80 80 Yes
Emergent Ventures India Pvt. Ltd Freight Facilities / Network Truck Terminals Speed No. 1 2 1 No Junction Improvements / Traffic Network Management Speed No. 10 No Signal Coordination Network and Optimization Speed No. 10 No Traffic Management / Pavements Marking Network and Signage Speed No. 13 No Bus Shelter and Bus Bays Modal Shift No. 60 60 60 No Network Ring Road Speed KM 50 50 44 Yes NMT Facilities – Walk-able Footpath Modal Shift KM 200 Yes Network Road Widening Speed KM 100 100 30 Yes Public Transport Facilities - Bus Terminals Modal Shift No. 2 2 1 No 1 2 1 Mono Rail Transit Modal Shift KM 9 1 0 Yes Intermodal Terminals Modal Shift No. 5 6 No 3 3 4-Lane Roads Modal Shift KM 0 0 0 Yes 5 Single Lane Modal Shift KM 0 0 0 No
6. Vehicle Split as per Fuel Type in the base-year, BAU scenario and CMP Implemented scenario. A sample distribution is already made for user in the sheet. User can make changes as per their city scenario.
Emergent Ventures India Pvt. Ltd Vehicle Technology Distribution in 2030 in BAU and CMP % of % of Vehicle Vehicle % of Vehicle Population Population Population Vehicles in year in year 2030 in year 2030 Type Fuel Type Base year in BAU in CMP 2W 2s / 4s Petrol 100.00% 100.00% 100.00% 3W 2s/4s Petrol 100.00% 100.00% 100.00% 3W 2s/4s CNG 0.00% 0.00% 0.00% 3W 2s/4s LPG 0.00% 0.00% 0.00% 3W passenger tempo Diesel 0.00% 0.00% 0.00% Car petrol 100.00% 100.00% 100.00% Car diesel 0.00% 0.00% 0.00% Car CNG 0.00% 0.00% 0.00% Taxi petrol 0.00% 0.00% 0.00% Taxi diesel 100.00% 100.00% 100.00% Taxi CNG 0.00% 0.00% 0.00% Taxi LPG 0.00% 0.00% 0.00% Mini Bus Diesel 0.00% 0.00% 0.00% Bus Diesel 100.00% 100.00% 100.00% Bus CNG 0.00% 0.00% 0.00% BRTS Diesel 0.00% 0.00% 100.00% BRTS CNG 0.00% 0.00% 0.00% MRTS Electricity 0.00% 100.00% Monorail Electricity 0.00% 100.00% 7. Vehicle and fuel type and their average occupancy are shown in the following table
Vehicle Type Fuel Type Average Occupancy 2W 2s / 4s Petrol 1.60 3W 2s/4s Petrol 2.00 3W 2s/4s CNG 2.00 3W 2s/4s LPG 2.00 3W Passenger Tempo Diesel 2.00 Car Petrol 2.60 Car Diesel 2.60 Car CNG 2.60 Taxi Petrol 2.60 Taxi Diesel 2.60 Taxi CNG 2.60 Taxi LPG 2.60 Mini Bus Diesel 20.00 Bus Diesel 40.00 Bus CNG 40.00 BRTS Diesel 45.00 BRTS CNG 45.00
8. Design life of infrastructure measures proposed under CMP
The CMP proposes various infrastructure projects which will involve GHG emissions during project construction including the emissions involved in the raw material generation such as
Emergent Ventures India Pvt. Ltd steel. These construction emissions are normalized over the design life of the infrastructure. The consultants may change, wherever applicable, the design life of the activity based on city specific plans.
Design Activity Life BRTS 20 MRTS 20 NMT - Bicycle Lanes 20 Ring Road 20 NMT Facilities - Walkable Footpath 20 Road Widening 20 Mono Rail Transit 20 4-Lane Roads 20 Single Lane 20
Emergent Ventures India Pvt. Ltd Steps 5 to 8 are shown as Tool Screen Shot below
CMP Policies - Construction Related Data Major Construction Policy measure Major Impact Unit 2016 2021 2031 Impact Bus Fleet Augumentation Modal Shift No 450 1000 1000 No BRTS Modal Shift KM 93 65 7 Yes MRTS Modal Shift KM 29 15 16 Yes NMT - Separated Grade Facility Modal Shift No 5 6 No NMT - Bicycle Lanes Modal Shift KM 0 80 80 Yes Fright Facilities / Truck Terminals Network Speed No 1 2 1 No Junction Improvements / Traffic Management Network Speed No 10 No Signal Coordination and Optimazation Network Speed No 10 No Traffic Management / Pavements Marking and Signage Network Speed No 13 No Bus Shelter and Bus Bays Modal Shift No 60 60 60 No Ring Road Network Speed KM 50 50 44 Yes
NMT Facilities - Walkable Footpath Modal Shift KM 200 Yes Road Widening Network Speed KM 100 100 30 Yes Public Transport Facilities - Bus Terminals Modal Shift No 2 2 1 No Mono Rail Transit Modal Shift KM 19 21 10 Yes Intermodal Terminals Modal Shift No 5 6 No 4-Lane Roads Modal Shift KM 30 30 0 Yes Single Lane Modal Shift KM 0 0 50 No
Vehicle Technology Distribution in 2030 in BAU and CMP % of Vehicle % of Vehicle Population in Population in % of Vehicle Population in year 2030 in year 2030 in Vehicles Type Fuel Type year Base year BAU CMP 2W 2s / 4s Petrol 100.00% 100.00% 100.00% 3W 2s/4s Petrol 100.00% 100.00% 100.00% 3W 2s/4s CNG 0.00% 0.00% 0.00% 3W 2s/4s LPG 0.00% 0.00% 0.00% 3W passenger tempo Diesel 0.00% 0.00% 0.00% Car petrol 100.00% 100.00% 100.00% Car diesel 0.00% 0.00% 0.00% Car CNG 0.00% 0.00% 0.00% Taxi petrol 0.00% 0.00% 0.00% Taxi diesel 100.00% 100.00% 100.00% Taxi CNG 0.00% 0.00% 0.00% Taxi LPG 0.00% 0.00% 0.00% Mini Bus Diesel 0.00% 0.00% 0.00% Bus Diesel 100.00% 100.00% 100.00% Bus CNG 0.00% 0.00% 0.00% BRTS Diesel 0.00% 0.00% 100.00% BRTS CNG 0.00% 0.00% 0.00% MRTS Electricity 0.00% 0.00% 100.00% Monorail Electricity 0.00% 0.00% 100.00%
Average Occupancy of Vehicles Observed
Vehicle Type Fuel Type Average Occupancy Design Life of Infrastructure
2W 2s / 4s petrol 1.60 Transport Measure Design Life 3W 2s/4s Petrol 2.00 BRTS 20 3W 2s/4s CNG 2.00 MRTS 20
3W 2s/4s LPG 2.00 NMT - Bicycle Lanes 20 3W Passenger Tempo Diesel 2.00 Ring Road 20
NMT Facilities - Car petrol 2.60 Walkable Footpath 20 Car diesel 2.60 Road Widening 20 Car CNG 2.60 Mono Rail Transit 20 Taxi petrol 2.60 4-Lane Roads 20 Taxi diesel 2.60 Single Lane 20 Taxi CNG 2.60 Taxi LPG 2.60 Mini Bus Diesel 20.00 Bus Diesel 40.00 Bus CNG 40.00 BRTS Diesel 45.00 BRTS CNG 45.00
Emergent Ventures India Pvt. Ltd 4.2 Sub-scenario selection i.e. Well to Wheel or Gate to Wheel
9. Well to Wheel and Gate to Wheel Scenario
There are two sub scenarios in the overall analysis- Well to Wheel and Gate to Wheel (Local). Well to wheel scenario considers emissions over the entire life cycle of the fuel e.g. in case of petrol/diesel it will also include emissions made during the extraction, transport, refining, delivery to pump and then combustion. Similarly for electricity, a well to wheel will include emissions made during the fuel extraction (coal etc) transportation, combustion at power plant, losses in transmission and distribution etc.
Similarly, a gate to wheel scenario only deals with emissions made during the fuel combustion process. The tool will automatically use emissions factors (default data) as appropriate for sub scenario selected. This selection can be done in “BAU Assumption Sheet”
4.3 Assessment of Induced Demand for Input If road capacity increases, the number of peak-period trips also increases until congestion reaches the original level. The additional travel is called “generated traffic.”
Research has conclusively proven that the concept of Generated Traffic exists. It consists of two main components
3) Diverted vehicle traffic
trips shifted in time
route and
destination
4) Induced vehicle traffic
shifts from other modes,
longer trips and
new vehicle trips
Research indicates that generated traffic fills the capacity added over a period of 5-10 years. The benefits of new capacity additions are soon lost. CMP’s made in current form sometimes do not take “generated traffic” into account leading to exaggerate future traffic speeds. This selection can be made in “Other Data”
Emergent Ventures India Pvt. Ltd Emergent Ventures India Pvt. Ltd 4.4 Guide to change default values for vehicle, metro and electricity (grid) related emission factors for Baseline and 2030 scenario Designer may change the default value of the various parameters used in the tool. These can be changed in “Emission Data Sheet” of the tool. These:
1. Per KM GHG emissions as per vehicle type
The existing default values have been taken from ARAI and can be modified if updated values are provided.
Gate to Gate CO2 Fuel emissions (in grams) / KM Vehicle Type Type in Baseline Scenario 2W 2s / 4s petrol 24.82 3W 2s/4s Petrol 73.8 3W 2s/4s CNG 77.7 3W 2s/4s LPG 54.57 3W Passenger Tempo Diesel 131.61 Car petrol 172.95 Car diesel 154.56 Car CNG 131.19 Taxi petrol 172.95 Taxi diesel 148.76 Taxi CNG 131.19 Taxi LPG 140.05 Mini Bus Diesel 401.25 Bus Diesel 617.45 Bus CNG 806.5 BRTS Diesel 617.45 BRTS CNG 806.5 Note:
1. ARAI Vehicle - CO2 emissions per KM is gate to wheel sub scenario. The values for Well to Wheel sub scenarios are determined automatically based on these input values.
2. Savings in GHG emissions due to improvement in vehicle technology
The above information used in the model is based on case studies and secondary literature. If different data is available from any reliable source, Designer may change these values also.
Vehicle technology improvement Assumptions
Assumption based on current % reduction in GHG PER YEAR due to improvement in vehicle 1.0% trends in GHG reduction technology till 2030 intensity of vehicles
Emergent Ventures India Pvt. Ltd 3. Per Passenger Per KM Emissions from Metro
If the Planner has Per Passenger Per KM CO2 emissions for the city’s metro then they can insert it into the tool or else they can use the default value provided into the tool.
Note: Even if the planner insert the value of PPKM of metro system in the green cell, they have to select the value from the drop down as highlighted in the purple cell below.
Metro Emissions Factor Selection Insert "0" if not User City Metro "Gate to Gate" GHG emissions (grams CO2 per known and use passenger per km). The normally provided PPKM are gate to gate average of other unless otherwise stated. 0 3 metros Metro PPKM Emission Factor (Option and Value) Average of three metro's 49.06
4. Grid Emissions Factor for Gate to Gate and Well to Wheel Scenarios
The grid emission factor provided by Central Electricity Authority and Well to Wheel emissions factor12 also change over time. Designer can therefore update these default values also.
Grid Emissions CO2 KGs Gate to gate Well to Wheel % addition to Factor (2010) per kWh emissions factor 0.85 gate to gate emissions factor 1.54
5. Analysis of India’s Grid Emission Factor of 2030
The assumption is derived based on Interim Report of the Expert Group on low carbon economy by planning commission of India. Refer to the “Database sheet” (12) for detailed calculation. If the planner has updated number they can change the values in the green cell as shown below.
2030 Grid Emissions Factors Assumptions
Assumptions / Scenarios for 2030 GEF
This number shows the probable GEF in the year 2030 % reduction in GEF for natural technology improvement and increase as per current trends and renewable/nuclear share (Scenario 1) 18.47% information by GOI
2030 Grid Emission Factor (Derived) 0.69
12 User can leave well to wheel emission factor for India. This number was published in an independent research conducted by GaBi India.
Emergent Ventures India Pvt. Ltd The steps 1 to 5 are shown as tool’s screen shot below:
Electricity Grid Emission Factor Grid Emissions kWh Gate to gate emissions factor 0.85 Well to Wheel Grid Emissions 1.54 Sub Scenario Selected Gate to Gate Scenario 0.85 % reduction in GHG PER YEAR due to improvement in vehicle technology till 2030 1.00% % of improvement in grid emissions factor due to natural technology improvement and increased renewable focus till 2030 18.47%
Applied Current Grid Emission Factor as per above selection 0.85 2030 Grid Emissions Factors Assumptions
Assumptions / Scenarios for 2030 GEF ( GEF: Grid Emission Factor)
This number shows the probable GEF in the year 2030 % reduction in GEF for natural technology improvement and increase as per current trends and renewable/nuclear share (Scenario 1) 18.47% information by GOI
2030 Grid Emission Factor (Derived) 0.69 Vehicle technology improvement Assumptions
Assumption based on current 1.0% % reduction in GHG PER YEAR due to improvement in vehicle trends in GHG reduction technology till 2030 intensity of vehicles Metro Emissions Factor Selection
User City Metro "Gate to Gate" GHG emissions (grams CO2 per passenger per km). The normally provided PPKM are gate to gate Insert "0" if not known and use unless otherwise stated. 0 average of other 3 metros Metro PPKM Emission Factor (Option and Value) Average of three metro's 49.06 for sub scenario selected = Gate to Gate Scenario
Vehicle Category CO2 emissions per KM (Source: Automotive Research Association of India, ARAI) Emissions per Emissions per passenger Gate to Gate CO2 emissions / KM, CO2 emissions / KM, Average Current Occupancy, passenger per km at per km at Average Vehicle Type Fuel Type Baseline Gate to Gate Baseline Well to Wheel CO2 emissions / KM 2030 CMP ideal speed ideal speed 2010 2W 2s / 4s petrol 24.82 24.82 20.3 1.60 15.51 12.69 15.51 3W 2s/4s Petrol 73.8 73.8 60.4 2.00 36.90 30.18 3W 2s/4s CNG 77.7 77.7 63.6 2.00 38.85 31.78 3W 2s/4s LPG 54.57 54.57 44.6 2.00 27.29 22.32 3W Passenger TempoDiesel 131.61 131.61 107.6 2.00 65.81 53.82 42.21 Car petrol 172.95 172.95 141.5 2.60 66.52 54.41 Car diesel 154.56 154.56 126.4 2.60 59.45 48.62 Car CNG 131.19 131.19 107.3 2.60 50.46 41.27 Taxi petrol 172.95 172.95 141.5 2.60 66.52 54.41 Taxi diesel 148.76 148.76 121.7 2.60 57.22 46.80 Taxi CNG 131.19 131.19 107.3 2.60 50.46 41.27 Taxi LPG 140.05 140.05 114.5 2.60 53.87 44.06 57.78 Mini Bus Diesel 401.25 401.25 328.2 20.00 20.06 16.41 Bus Diesel 617.45 617.45 505.0 40.00 15.44 12.63 Bus CNG 806.5 806.5 659.6 40.00 20.16 16.49 BRTS Diesel 617.45 617.45 505.0 45.00 13.72 11.22 BRTS CNG 806.5 806.5 659.6 45.00 17.92 14.66 17.46 MRTS 49 40.0 50.04 Monorail 49 40.0 50.04
Emergent Ventures India Pvt. Ltd 4.5 Guide to change default values for Modified Scenarios and Additional Green Measures. These are the measures proposed to improve effectiveness of CMP. CMP may include suggestion based on the trends in the traffic on demand elimination measures using land-use. These are provided in the “Other Data Sheet”.
Certain measures include promotion of Internet and Communication technologies (ICT) to reduce travel demand. These measures have been discussed in the table XX shown in part B of the report.
1. Trips that can be totally avoided through improved land use and other measures. To estimate the overall impact of both the measures it has been assumed that first reduction will happen and then remaining trips will be eliminated using improved land use.
Trips Reduction Percentage - Other measures (ICT etc) 10% Trips elimination using Improve Land Use 25%
2. Additional modal shifts from PV or IPT to public transport
Users can add the percentage of trips that can be shifted to Public Transport using additional modal shift measures.
Additional Modal Shift PV / IPT From PT To 10% %
3. Additional Green Measures – Electric Vehicles and Eco Driving
Designer can also understand the impact of measures by changing default values of additional measures that has been modeled into the tool.
Additional Green Measures Emission reduction due to Eco-Driving 20% Eco Diving Adoption Rate 15% Electric Vehicle Emission factor per km per passenger as a fraction of gasoline vehicle 0.96 Electric Vehicle Penetration 10% Initial Travel Demand 100%
Emergent Ventures India Pvt. Ltd Detailed modeling has been done to estimate improvements using load factor improvement of metro systems only. Consultants interested in the detailed modeling can see it the calculations worksheet.
4.6 Guide to change default values for Fixed Parameters
This section deals with variables that normally do not change with changing cities. However, in case consultants want to change these for any reason, they can do it in the “Database” sheet. These variables are:
1. Emissions factors of fossil fuels
Interested users can modify values in the tool in cells which are highlighted in green color. It has been assumed that Well to Wheel Emissions factors for fossil fuels are 20% higher than their gate to wheel emissions.
Emission Factors for fossil fuel Well to Gate to gate Wheel % Well to Wheel Emissions emissions addition Emission Factor (KG Fuel Type UoM factor Source to gate to Factor CO2 / UoM) Petrol Litre 2.31 IPCC 20% 2.772 2.31 Diesel Litre 2.68 IPCC 20% 3.216 2.68 CNG KG 2.7 IPCC 20% 3.24 2.70 LPG KG 2.95 IPCC 20% 3.54 2.95
CNG emission Factor as a fraction of Gasoline 0.73
2. Construction related emission factors
Interested users can modify values in the tool in cells which are highlighted in green color.
Assumptions and Emissions Factors for construction activities (Source: TEEMP Defaults) 1 km of Tonnes infrastructure Description of CO2 Source Assuming material quantity - Cement -737.8 tons/km, Asphalt - 403.5 tons/km and Steel - 143.2 tons/km. A Considering only multiplier of 1.75 has been proposed for actual construction the quantity of works based on Kwangho Park, et. al. (2003),. Estimates from steel, cement and Mexico BRTS ( Lee at al.) and Transmilenio ( monitoring BRTS asphalt. 1900 report) have indicated 3475 and 1390 tons . Considering only the quantity of Assuming material quantity - Cement -15.5 tons/km, Asphalt steel, cement and - 40 tons/km and Steel - 1 tons/km for constructing 1km of Bikeways asphalt. 20 2.5 m wide bikeway Bangalore metro calculations using quantity of materials 2 lines for 80% used - steel and cement. Research from Japan as elevated and 20% summarized in TEEMP model indicates a range between 7119
MRTS underground 15600 to 19487 tons of CO2
Emergent Ventures India Pvt. Ltd Assuming a track requires 570 tons of concrete and 117 tons
Considering only of steel, 350 tons of CO2 is generated during material the quantity of production. Scotland Transport Department recommends steel and 500 tons of CO2 per track based on material production. A concrete for multiplier of 1.75 has been proposed for actual construction Railways single track 875 works based on Kwangho Park for Road works An analysis based on the quantity of construction materials used – cement, steel and bitumen indicates that the approximate emissions of a two lane to four lane improved Considering only highway is approximate 1100 tons/km. When all the the quantity of quantities are considered including the emissions generated steel, cement and by machinery, the emissions range from 2100 to 2400 asphalt for a four tons/km for high-speed roads (four-lanes) based on traffic, Roads (4 Lane) lane road 2100 topography and type of improvements suggested. Considering only the quantity of An analysis based on the quantity of construction materials Roads steel, cement and used – cement, steel and bitumen indicates that the (conversion of 2 asphalt for a four approximate emissions of a two lane to four lane improved to 4 lane) lane road 1100 highway is approximate 1100 tons/km.
3. TEEMP Vehicle’s Speed – GHG relationships.
Interested users can modify values in the tool in cells which are highlighted in green color.
Speed - Emissions Factor; Assuming 50 to be ideal Speed 2W 3W Cars LCV Bus HCV 10 -79 -79 -70 -79 -70 -70 15 -70 -70 -61 -69 -61 -61 20 -43 -43 -34 -38 -51 -51 25 -26 -26 -20 -22 -39 -39 30 -21 -21 -12 -18 -23 -23 35 -7 -7 -5 -6 -15 -15 40 -4 -4 -3 -3 -9 -9 45 -1 -1 0 0 -3 -3 50 0 0 0 0 0 0 55 0 0 -1 -1 2 2 60 -2 -2 -3 -4 5 5 65 -4 -4 -6 -7 5 5 70 -8 -8 -9 -12 6 6 75 -12 -12 -13 -16 0 0 80 -18 -18 -18 -23 -4 -4 85 -23 -23 -24 -29 -7 -7 90 -30 -30 -30 -37 -12 -12 95 -37 -37 -36 -45 -16 -16 100 -37 -37 -36 -45 -16 -16
Emergent Ventures India Pvt. Ltd Annex 1: Structure of the tool The tool includes 11 worksheets and the purpose of each of the sheet is as follows:
Sheet No Name and Purpose 1 Results This sheet provides results of various scenarios taken for assessing CMP's impact on GHG emissions. Users are advised not to insert or modify any data in this sheet. 2 Detailed Results Provided detailed results of the various scenarios and options for city official to act on it 3 Scenario and CMP data This sheet is the input sheet for CMP related data and for scenario selection for gate to gate or well to wheel. 4 Emissions Data This sheet is the input sheet for (a) current well to wheel and gate to gate emission factor (b)grid emission factor scenario for 2030 (c) metro emissions factor (d) vehicle emission factor data base 5 Other Data This sheet is the input sheet for assumption of additional measures used in the tool and generated traffic elasticity e.g. (a)Trips avoided due to demand elimination measures (b)Modal shift due to additional public transport enhancement measures (c) Increase in CNG fleets, Electric Vehicles and Adoption of Eco-Driving measures 6 BAU (2030) This sheet provides the GHG emissions in current baseline year and Business As Usual 2030 Scenario. Users are advised not to insert or modify any data in this sheet. 7 CMP As Is Scenario (2030) This sheet provides the GHG emissions in case of CMP implemented scenario by taking CMP assumptions as it is. CMP ignores the impact of induced demand while determining the network speed. Users are advised not to insert or modify any data in this sheet. 8 CMP Most Likely Scenario (2030) This sheet provides the GHG emissions in CMP most likely scenario (2030) while taking into care the impact of induced demand on the network speed. Users are advised not to insert or modify any data in this sheet. 9 CMP Additional Measures Impact (2030) This sheet provides the GHG emissions in CMP most likely scenario (2030) while taking into care the impact of induced demand on the network speed. Users are advised not to insert or modify any data in this sheet. 10 Calculations This sheet shows intermediary calculations. 11 Database This sheet has database of parameters which user which is constant for all cities. Table Annex 1: Summary of Tool Worksheets
Color coding of the cells
Data is to be input into various cells of each worksheet. For convenience the cells are color coded as follows.
All cells in green in the input sheet refer to the cells where data is input. All cells in dark pink are the cells where assumed default values have been either assumed or derived from other values. In addition, some data will get automatically added based on inputs in the green cells. Drop down selection Data in all other cell or data taken from CMP not to be edited.
Emergent Ventures India Pvt. Ltd Annex 2: Parameters that are variable and are based on City Characteristics
A. Data for a city (fixed)
1. Vehicle and Trip related information (From CMP)
Name of Unit of Description Sheet in which Remarks Variable Measur it needs to be e inserted
TW_Trips Number Number of Trips made Baseline, 2030 BAU, 2030 by Two Wheelers CMP
Car_Trips Number Number of Trips made Input – CMP Baseline, 2030 BAU, 2030 by Cars Data CMP
Auto Trips Number Number of Trips made Input – CMP Baseline, 2030 BAU, 2030 by Auto Rickshaw Data CMP
Taxi Trips Number Number of Trips made Input – CMP Baseline, 2030 BAU, 2030 by Taxi Data CMP
PT Trips Number Number of Trips made Input – CMP Baseline, 2030 BAU, 2030 Total by Public Transport Data CMP
Number Number of trips made Input – CMP 2030 CMP CMP Bus by existing buses in Data Trips CMP scenario
Number Number of trips made Input – CMP 2030 CMP CMP by additionally Data Additional purchased buses in Buses Trips CMP Scenario
CMP Number Number of trips made Input – CMP 2030 CMP Commuter by Proposed Data Rail Trips Commuter Rail
Number Number of trips made Input – CMP 2030 CMP CMP BRTS by proposed BRTS in Data Trips CMP Scenario
Number Number of trips made Input – CMP 2030 CMP CMP MRTS by Proposed MRTS in Data Trips CMP Scenario
PV Trip KM Average Private Input – CMP Baseline, 2030 BAU, 2030 Length Vehicle Trip Length Data CMP
KM Average Intermediary Input – CMP Baseline, 2030 BAU, 2030 IPT Trip Private Vehicle Trip Data CMP Length Length
PT Trip KM Average Public Input – CMP Baseline, 2030 BAU, 2030
Emergent Ventures India Pvt. Ltd Length Transport Trip Length Data CMP
Average KM / Average Network Input – CMP Baseline, 2030 BAU, 2030 Network Hour Speed during peak Data CMP Speed hour
Peak Hour to % Ratio of peak hour Input – CMP This is used to extrapolate Total Trip trips to total number of Data peak hour emissions to total Ratio daily trips daily emissions
2. CMP Transport Measures related data is fixed for a city
Policies / Transport Related Data proposed to be implemented in CMP Scenario
Measure modeled into the tool for construction Unit of Construction Major Impact Area related impact Measure Impact
Bus Fleet Augumentation Modal Shift Number No
BRTS Modal Shift KM Yes
MRTS Modal Shift KM Yes
NMT - Separated Grade Facility Modal Shift Number No
NMT - Bicycle Lanes Modal Shift KM Yes
Fright Facilities / Truck Terminals Network Speed Number No
Junction Improvements / Traffic Management Network Speed Number No
Signal Coordination and Optimazation Network Speed Number No
Traffic Management / Pavements Marking and Signage Network Speed Number No
Bus Shelter and Bus Bays Modal Shift Number No
Ring Road Network Speed KM Yes
NMT Facilities - Walkable Footpath Modal Shift KM Yes
Road Widening Network Speed KM Yes
Public Transport Facilities - Bus Terminals Modal Shift Number No
Mono Rail Transit Modal Shift KM Yes
Intermodal Terminals Modal Shift Number No
4-Lane Roads Modal Shift KM Yes
Single Lane Modal Shift KM No
3. Vehicle fuel type and trips distribution (From RTO)
Vehicle fuel type and trip distribution in Base Year, 2030 in BAU and CMP scenario
Emergent Ventures India Pvt. Ltd Vehicles Type Fuel Type UoM Remarks
2W 2s / 4s Petrol % 100% Petrol is assumed in all scenario
3W 2s/4s Petrol % Can be distributed in Petrol, CNG, and LPG as per CMP. 3W 2s/4s CNG %
3W 2s/4s LPG %
3W passenger tempo Diesel % 100 % Diesel in assumed
Car petrol % Distribution in terms of Petrol, Diesel and CNG; Electric Vehicle Penetration is not assumed in CMP Car diesel %
Car CNG %
Taxi petrol % Distribution in terms of Petrol, Diesel, CNG and LPG; however CMPs does not provide such distribution and Taxi diesel % user may have to guess appropriately. Electric Vehicle Taxi CNG % Penetration is not assumed in CMP.
Taxi LPG %
Mini Bus Diesel % Bus and Mini bus distribution as per CMP, however CMPs does not provide such distribution and user may Bus Diesel % have to guess appropriately Bus CNG %
BRTS Diesel % BRTS fuel separation, however CMPs does not provide such distribution and user may have to guess BRTS CNG % appropriately
MRTS Electricity % 100% Grid Supplied Electricity is assumed in CMP scenario Monorail Electricity %
4. Occupancy Levels (From CMP)
Vehicle Type and Occupancy Levels in all three scenarios Fuel Remarks Vehicle Type Type Unit of Measure 2W 2s / 4s petrol Number Assumed constant in all three scenarios 3W 2s/4s Petrol Number Assumed constant in all three scenarios
Emergent Ventures India Pvt. Ltd 3W 2s/4s CNG Number Assumed constant in all three scenarios 3W 2s/4s LPG Number Assumed constant in all three scenarios 3W Passenger Number Assumed constant in all three scenarios Tempo Diesel Car petrol Number Assumed constant in all three scenarios Car diesel Number Assumed constant in all three scenarios Car CNG Number Assumed constant in all three scenarios Taxi petrol Number Assumed constant in all three scenarios Taxi diesel Number Assumed constant in all three scenarios Taxi CNG Number Assumed constant in all three scenarios Taxi LPG Number Assumed constant in all three scenarios Mini Bus Diesel Number Assumed constant in all three scenarios Bus Diesel Number Assumed constant in all three scenarios Bus CNG Number Assumed constant in all three scenarios BRTS Diesel Number Assumed constant in all three scenarios BRTS CNG Number Assumed constant in all three scenarios
5. Life of Infrastructure measures as proposed in CMP (Default Values)
Design Life of Infrastructure
Transport Measure Unit Of Measure Remarks
BRTS No of years without major maintenance In the model, currently design MRTS No of years without major maintenance life is assumed NMT - Bicycle Lanes No of years without major maintenance 20 years for all construction Ring Road No of years without major maintenance related activity
NMT Facilities - Walkable Footpath No of years without major maintenance
Road Widening No of years without major maintenance
Mono Rail Transit No of years without major maintenance
4-Lane Roads No of years without major maintenance
Single Lane No of years without major maintenance
6. Metro Related Per Passenger Per KM Emissions Factor
Metro Emissions Factor Selection
Sub Scenario Unit of Measure Remarks / Assumptions
Specific City Selection from a The data could be taken from Detailed Project Report for the drop down city’s metro. The calculation method of per passenger Per Average of Indian KM is shown in the “Database” sheet 12. User can also
Emergent Ventures India Pvt. Ltd Metro Systems select the average of three metros. Or they can insert the value of their city’s PPKM emission in the “Scenario & CMP Data” sheet.
Emergent Ventures India Pvt. Ltd Annex 3: Parameter that are fixed but may change over time due to technology improvements
1.Current Grid Emission Factor of Indian Electricity
Variable Unit of Measure Remarks / Assumptions
Gate to Gate GEF KGs CO2 emissions per kWh Gate to gate emissions factor is chosen as 0.85; Well to Wheel GEF is chosen as 1.54 as per study Well to Wheel GEF KGs CO2 emissions per kWh done by GaBi Life Cycle Analysis Software for Indian Grid Supplied Electricity
2. Assumed Year 2030 grid emission factor related assumption
Variable Unit of Measure Remarks / Assumptions
% reduction in Grid Emission Aggregate % till Natural technology improvement and Factor (GEF) for 2030 increase renewable/nuclear share (Scenario 1 of low carbon committee report of GOI)
% reduction in GHG PER % per year Natural rate of technology improvement in YEAR due to improvement in Vehicle technology till 2030 vehicle technology till 2030
Emergent Ventures India Pvt. Ltd 3. Per-passenger per KM (PPKM) GHG emissions of various vehicle and fuel types combinations
Unit of Measure Remarks / Vehicle Type Fuel Type Assumptions 2W 2s / 4s petrol CO 2 emissions in grams / KM Automotive 3W 2s/4s Petrol CO 2 emissions in grams / KM Research 3W 2s/4s CNG CO 2 emissions in grams / KM Association of CO 2 emissions in grams / India provides per 3W 2s/4s LPG KM KM emissions of 3W Passenger CO 2 emissions in grams / KM various vehicle Tempo Diesel and fuel type Car petrol CO 2 emissions in grams / KM combination. For Car diesel CO 2 emissions in grams / KM multiple options Car CNG CO 2 emissions in grams / KM within this Taxi petrol CO 2 emissions in grams / KM combination, an Taxi diesel CO 2 emissions in grams / KM average value is Taxi CNG CO 2 emissions in grams / KM used. Taxi LPG CO 2 emissions in grams / KM Mini Bus Diesel CO 2 emissions in grams / KM Bus Diesel CO 2 emissions in grams / KM Bus CNG CO 2 emissions in grams / KM BRTS Diesel CO 2 emissions in grams / KM BRTS CNG CO 2 emissions in grams / KM
Emergent Ventures India Pvt. Ltd Annex 4: Default values for Measures / Assumptions to mitigate emissions
A. These parameters are listed in “Other Data” sheet (6). These parameters will impact the performance of other measures which are selected apart from already included measures in CMP. These are:
Variable Name Unit of Impact Remarks / Assumptions Measure
Trips Reduction % Reduction of number of 10%, Generic assumption. For list Percentage trips due to measures of potential measures see table 1 apart from land use change
Trips elimination using % Reduction of number of 25%, it has been identified that improved land use trips due to better land proper land use planning has the use planning maximum impact potential in low carbon urban transportation
Additional Modal Shifts % Due to promotion of 10%, Generic assumption. For list public transport using of potential measures see table 1 additional measures apart from CMP, additional modal shift from private to public transport systems
Emission reduction due to % 20%, as per TEEMP Eco-Driving
Eco Diving Adoption Rate % Adoption of better driving 15%, generic assumption practices by commuters as a percentage of overall emissions
Electric Vehicle Emission % Reduction due to fuel 0.96, estimated using current factor per km per switch. numbers in case of gate to gate passenger as a fraction of scenario. This number however fossil fuel driven car will come down as EVs become more efficient and grid electricity becomes less carbon intensive
Electric Vehicle % 10%, generic assumption Penetration
Induced Demand Elasticity Number Reduces the benefit of .8 (elasticities are generally (impacts over time) additional capacity due to negative, but for ease of modeling
Emergent Ventures India Pvt. Ltd generated traffic over please insert the number only time ignoring the negative sign.
For high growth scenario .8 is reasonable assumption.
B. Parameters in Database Sheet (Default Data)
Database sheet contains parameters which are used in for calculation purpose. These are
Emissions Factors for fossil fuel
Fuel Type Unit of Measure Remarks
Petrol Litre IPCC Default
Diesel Litre IPCC Default
CNG KG IPCC Default
LPG KG IPCC Default
CNG emission Factor as a fraction of Ratio (number) IPCC Default Gasoline
Overall Improvement in GEF till 2030 % Assumed 18.47% improvement till 2030 at 2010 baseline by taking assumptions of Draft Report of Low Carbon Committee of GOI
Existing Indian Metro’s PPKM CO2 CO2 emissions in Delhi, Jaipur, Bangalore, taken from emissions grams per passenger their DPRs. per KM
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the BRTS tonnes database sheet of tool
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the Bikeways tonnes database sheet of tool
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the MRTS tonnes database sheet of tool
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the Railways tonnes database sheet of tool
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the Road (4 lanes) tonnes database sheet of tool
Emissions in construction of 1 KM CO2 emissions in Source is TEEMP; for details see the conversion of 2 to 4 lane tonnes database sheet of tool
Speed and Vehicle GHG emissions (-) Number Assuming 50KM/Hour is the most
Emergent Ventures India Pvt. Ltd relationships optimum speed for driving vehicles; Current numbers are taken from TEEMP
Emergent Ventures India Pvt. Ltd Annex 5: Guide for Assessment of Generated Traffic
This sheet shows how the Induced demand was derived. It requires no user inputs and hence may be ignored under normal circumstances.
Long term (10 + years) in high demand scenario as observed in Indian cities the latent demand will be almost 90%. Over 4 years + in high demand scenario the latent demand will be over 70%
Emergent Ventures India Pvt. Ltd Portion of New Capacity Absorbed by Generated Traffic
Long-term (3+ Author yrs)
SACTRA, 1994 50 - 100%
Goodwin 57%
Johnson and Ceerla 60 - 90%
Hansen and Huang, 1997 90%
Fulton, et al. 50 - 80%
Marshall, 76 - 85%
Noland, 2001 70 - 100%
Source: Victoria Transport Policy Institute (For reference purpose only)
Emergent Ventures India Pvt. Ltd