A Report on
Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region
Munish K. Chandel
Ishant Sharma
Rajashree Padmanabhi
Arti Soni
Anil K. Dikshit
April 2018
Contents Contents 2 List of Figures 3 Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
List of Tables 6 1. Introduction 7 1.1 Business as Usual (BAU) Scenario 7 2. Policy Evaluation 9 2.1 Population Redistribution Scenario (Policy 1) 9 2.1.1 Mode Share and VKT Calculation 10 2.2 Draft Mumbai Metropolitan Regional Plan 2016-36 scenario (Policy 2) 13 2.2.1 Population Projections 13 2.2.2 Employment Projections 13 2.2.3 Addition of Transport Infrastructure 16 2.2.4 Mode Share and VKT Calculation 16 2.3 Transport Infrastructure Development (Policy 3) 19 2.3.1 Mode Share and VKT Calculation 20 2.4 Travel Demand Management (Policy 4) 22 2.4.1 Mode Share and VKT Calculation 23 2.5 Improvement in Technology (Policy 5) 27 2.6 Evaluation of Policy Packages 29 2.6.1 Mode Share and VKT Calculation 29 3. Comparison between BAU and Policies 34 3.1 Mode Share 34 3.2 Vehicle Kilometers Travelled 35 3.3 Emissions 36 3.3.1 Uptake of EVs 42 3.3.2 As per vehicles classes 63 3.3.3 Comparison of the total emissions in Rapid Assessment, BAU, Policy Packages 73 4. Conclusions 83 References 86 Appendix – A 88
List of Figures
Figure 2.1.1: Mode Share values for BAU & Policy 1- Redistribution of population 11 Figure 2.1.2: VKTs obtained for BAU & Policy 1- Redistribution of population 12 Figure 2.2.1: Proposed Growth Centres and Regional Corridors 15
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Figure 2.2.2: Mode Share values for BAU & Policy 2 Draft Mumbai Metropolitan Regional Plan 2016-36 17 Figure 2.2.3: VKTs obtained for BAU & Policy 2 Draft Mumbai Metropolitan Regional Plan 2016- 36 18 Figure 2.3.1 Added BRTS Routes in Network 19 Figure 2.3.2: Mode Share values for BAU & Policy 3 Improvement in Transport Infrastructure 20 Figure 2.3.3: VKTs obtained for BAU & Policy 3 Improvement in Transport Infrastructure 21 Figure 2.4.1: Mode Share values for BAU & Policy 4 (a) - Travel Demand Management - Congestion Pricing + Parking Policy 23 Figure 2.4.2: VKTs obtained for BAU & Policy 4 (a) Travel Demand Management- Congestion Pricing + Parking Policy 24 Figure 2.4.3: Mode Share values for BAU & Policy 4 (b) Travel Demand Management – Increase in Registration Cost +Fuel Cost 25 Figure 2.4.4: VKTs obtained for BAU & Policy 4 (b) Travel Demand Management – Increase in Registration Cost +Fuel Cost 26 Figure 2.5.1 Share of Electric, Hybrid and Fuel Cell 4-Wheelers in India 28 Figure 2.5.2 Share of Electric and Hybrid 2-Wheelers in India 28 Figure 2.6.1: Mode Share values for BAU & Policy Bundle 1- BAU+ Policy 3+ Policy 4 30 Figure 2.6.2: VKTs obtained for BAU & Policy Bundle 1- BAU+ Policy 3+ Policy 4 31 Figure 2.6.3: Mode Share values for BAU & Policy Bundle 2- Policy 2+ Policy 3+ Policy 4 32 Figure 2.6.4: VKTs obtained for BAU & Policy Bundle 2- Policy 2+ Policy 3+ Policy 4 33 Figure 3.1 Mode Share comparison of BAU and Polices and Policy Bundles 34 Figure 3.2 Comparison of VKTs of BAU and Evaluated Polices 35 Figure 3.3 Comparison of VKTs of BAU and Policy Bundles 36
Figure 3.4 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios 40 Figure 3.5 Total CO emissions (tonne/year) in BAU and Policy scenarios 40 Figure 3.6 Total HC 41
Figure 3.7 Total NOX emissions (tonne/year) in BAU and Policy scenarios 41
Figure 3.8 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios 42
Figure 3.9 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake 45
Figure 3.10 Total CO emissions (tonne/year) in BAU and Policy scenarios with the with the Scenario 1 of the Electric Vehicles’ Uptake 45
Figure 3.12 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake 46
Figure 3.13 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake 47
Figure 3.14 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake 50
Figure 3.15 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake 50
Figure 3.16 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake 51
Figure 3.17 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake 51
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Figure 3.18 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake 52
Figure 3.19 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake 55
Figure 3.20 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake 55
Figure 3.21 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake 56
Figure 3.22 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake 56
Figure 3.23 Total PM2.5 (tonne/year) emissions in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake 57
Figure 3.24 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake 60
Figure 3.25 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake 60
Figure 3.26 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake 61
Figure 3.27 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake 61
Figure 3.28 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake 62 Figure 3.29 Comparison of CO emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR 63 Figure 3.30 Comparison of CO emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR 64 Figure 3.31 Comparison of HC emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR 65 Figure 3.32 Comparison of HC emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR 66
Figure 3.33 Comparison of CO2 emissions (Million tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR 67
Figure 3.34 Comparison of CO2 emissions (Million tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR 68
Figure 3.35 Comparison of NOX emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR 69
Figure 3.36 Comparison of NOX emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR 70 Figure 3.37 Comparison of PM2.5 emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR 71 Figure 3.38 Comparison of PM2.5 emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR 72
Figure 3.39 Total CO2 emissions (Million tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031 73 Figure 3.40 Total CO emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031 74
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Figure 3.41 Total HC emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031 75 Figure 3.42 Total NOx emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031 76
Figure 3.43 Total PM2.5 emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031 77
Figure 3.44 Total CO2 emissions (Million tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050 78 Figure 3.45 Total CO emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050 79 Figure 3.46 Total HC emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050 80 Figure 3.47 Total NOx emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050 81
Figure 3.48 Total PM2.5 emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050 82
List of Tables
Table 2.1.1 Resultant CAGR in MMR as per Projected Population in Draft Regional Plan 10 Table 2.2.1 Comparison of population projections for BAU and Policy Scenarios 13 Table 2.2.2 Details of Proposed Growth Centres 14 Table 2.2.3 Details of Proposed Regional Industrial Areas 14 Table 3.1 Estimated Vehicle Emissions in BAU and Policy Scenarios for the horizon year 2031 38 Table 3.2 Estimated Vehicle Emissions in BAU and Policy Scenarios for the horizon year 2050 39 Figure 3.6 Total HC emissions (tonne/year) in BAU and Policy scenarios 41 Table 3.3 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 1 for the horizon year 2031 43 Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 1 for the horizon year 2050 44
Figure 3.11 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake 46
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Table 3.5 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 2 for the horizon year 2031 48 Table 3.6 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 2 for the horizon year 2050 49 Table 3.7 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 3 for the horizon year 2031 53 Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 3 for the horizon year 2050 54 Table 3.9 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 4 for the horizon year 2031 58 Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 4 for the horizon year 2050 59 Table 4.1 Percentage reduction in emissions across all policy scenarios and bundles after the uptake of electric vehicles 84 Table A-1 Calculated Emission factors for Conventional Vehicles 88 Table A-2 Calculated Electric Vehicles’ Emission factors (Scenario 1: Average Energy Consumption) 89 Table A-3 Calculated Electric Vehicles’ Emission factors (Scenario 2: Average Energy Consumption) 90 Table A-4 Calculated Electric Vehicles’ Emission factors (Scenario 3: Average Energy Consumption) 91 Table A-5 Calculated Electric Vehicles’ Emission factors (Scenario 4: Average Energy Consumption) 91
1. Introduction
In the earlier work packages, we use transport demand model for Mumbai Metropolitan Region (MMR) in a Business as Usual (BAU) scenario for base year 2005 and horizon years 2021, 2031 and 2050. We
also calculated CO2 and other emissions due to transportation sector. In this work package, we model alternate policy packages for mitigation of emissions for base year and the horizon years. The report includes Vehicle Kilometers Travelled (VKTs) and corresponding CO2 and other emissions for all policy scenarios and their bundles. All the assumptions are similar to BAU scenario unless specified.
The mitigation policy scenarios and bundles evaluated in this report are as follows:
1. Population Redistribution Scenario
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2. Draft Mumbai Metropolitan Regional Plan 2016-36 scenario 3. Transport Infrastructure Development 4. Travel Demand Management 5. Improvement in Technology
1.1 Business as Usual (BAU) Scenario
In Business as Usual (BAU) scenario, the base year travel pattern is modeled using Four Stage Travel Model. The calibrated models are used to forecast the trips for the horizon years with planning inputs of population, employment and incorporation of proposed transport infrastructure. In the base year 2005, only two main modes of public transport were available i.e. Suburban Rail and Bus. For horizon years, two more modes are being added into the public transportation i.e. Metro and Mono Rail. Population and employment forecast for MMR are based upon the Comprehensive Transportation Study (CTS) report. The CTS report predicts compound annual growth rate (CAGR) for the population of 1.9% for 2011 to 2031. We assumed that the CAGR would be 1.9% from 2031 to 2050. (CTS,2008)
While analyzing the spatial distribution of growth in formulating transportation strategies, CTS report considers following two extreme possibilities for Greater Mumbai and Rest of Region (RoR) of MMR:
1. Greater Mumbai will continue to dominate as a major hub for employment and will witness maximum growth in jobs and population. 2. The investment will be directed away from Greater Mumbai to RoR due to its conducive environment through cheaper land rates, SEZs, new airport etc. and RoR will benefit from the new employment generation. While considering various alternate growth scenarios that lie between these possibilities, the BAU scenario forecasts population and employment keeping in mind the future of MMR with distributed opportunities of development along the lines of the integrated land use and transportation planning.
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2. Policy Evaluation 2.1 Population Redistribution Scenario (Policy 1) The planning parameters of population in the BAU scenario are based upon the CTS report. It forecasted planning parameters for horizon years using 2005 base year planning parameters (CTS, 2008). The Draft Mumbai Metropolitan Regional Plan: 2016-36 (RP) was published almost 10 years after the CTS report and provides latest population projections for MMR. Planning parameters are essentially socio-economic data of households and employment for a recent base year. The accuracy of these socioeconomic data greatly affects the accuracy of the travel demand model. Therefore, the planning parameters need to be updated and reassessed on a regular basis (MMRDA, 2016).
Current population growth trends of MMR suggest a declining growth rate over the long term as mentioned in the Draft Regional Plan. However, MMR witnesses’ differential growth at individual corporations’ level. Some municipal corporations within MMR like Greater Mumbai, Ulhasnagar and Bhiwandi already have high density of population. They are saturated and may exhibit less scope for further densification. On the other hand, some cities like Vasai- Virar and Mira Bhayandar have more land available for development and will show higher growth rates in the future. Such recent differential growth rates can be used for updating population projections at Municipal Corporation’s level.
Therefore, in this policy we assume that there is no change in the nature of employment in MMR and the population will grow at the rates predicted in the Draft Regional Plan. These assumptions will
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay change the distribution of population and population share of constituents of MMR while keeping the total population projections used in the BAU scenario constant. The planning parameters of population projections are changed accordingly in the Travel Demand Model. Quantitative changes in the following variables are expected: ● Trip O-D Matrix ● Average Trip Length ● In Vehicle Travel Time ● Out of Vehicle Travel Time
Table 2.1.1 Resultant CAGR in MMR as per Projected Population in Draft Regional Plan CAGR S. Unit 71- 81- 91- 01- 11- 21- 31- No. 81 91 01 11 21 31 41 1 Greater Mumbai 3.28 1.87 1.90 0.38 0.27 -0.71 -1.12 2 Thane 6.45 5.41 4.62 3.85 1.96 2.11 1.64 3 Kalyan-Dombivali 5.99 6.63 2.55 1.49 0.97 0.46 0.11 4 Vasai - Virar City 2.57 4.16 6.46 5.80 4.78 3.98 3.37 5 Navi Mumbai 8.42 13.09 8.05 5.32 4.28 3.44 2.96 6 Mira-Bhayander 7.75 10.08 11.48 4.52 4.54 3.11 2.93 7 Bhiwandi-Nizampur 7.13 6.05 4.68 1.71 1.60 0.68 0.56 8 Ulhasnagar 5.00 2.74 2.53 0.66 0.53 -0.54 -0.95 A Municipal Corporations 3.67 2.73 2.75 1.43 1.31 0.77 0.69 9 Ambernath 5.51 2.36 4.94 2.21 2.83 1.87 2.01 10 Kulgaon-Badlapur 5.50 4.75 6.51 5.93 4.78 4.00 3.37 11 Panvel 3.37 4.75 5.84 5.63 4.56 3.89 3.28 12 Khopoli 3.06 6.26 2.68 1.95 0.95 0.60 0.48 13 Pen 2.31 3.87 3.41 2.28 2.10 1.65 1.53 14 Uran 1.86 1.60 2.72 2.73 2.82 2.59 2.43 15 Karjat 1.13 2.27 2.37 1.51 1.50 1.12 1.10 16 Alibag 1.66 1.49 1.81 0.62 1.24 0.67 0.94 17 Matheran -1.24 4.61 0.88 -1.56 -5.89 -0.68 -2.82 B Municipal Councils 3.85 3.49 4.59 3.51 3.39 2.85 2.64 18 Navi Mumbai NT (56) 3.79 3.56 4.33 6.63 4.86 4.38 3.53 19 NAINA & MSRDC (270) 2.49 2.60 3.12 1.05 1.23 0.52 0.54 20 Kalyan 27 Villages (26) 0.00 0.00 0.00 8.76 2.91 0.70 -1.02 21 BSNA (61) 4.52 3.97 4.54 2.84 2.84 2.21 2.11 22 AKBSNA (58) 2.68 1.85 6.85 2.66 3.61 2.32 2.49 23 VVSNA (24) 20.09 18.90 6.03 2.07 1.25 0.99 0.57 24 Khopta (33) 7.74 1.46 1.34 1.30 1.21 1.17 1.10 C SPA Areas 3.64 3.41 5.30 4.03 3.08 2.42 2.06 25 Thane District 2.30 3.84 5.20 2.49 2.79 1.90 1.93 26 Raigad District 0.47 4.38 4.58 2.38 2.44 1.69 1.66 D Census Towns 1.35 4.11 4.89 2.44 2.62 1.80 1.80 Urban MMR 3.67 2.79 2.93 1.66 1.53 1.01 0.94 (A+B+C+D) 27 Thane District Rural 1.95 3.10 1.51 1.47 0.80 0.73 0.36
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28 Raigad District Rural 1.90 1.03 1.62 1.03 1.25 0.97 1.04 E Rural (MMR) 1.93 2.12 1.56 1.28 1.00 0.84 0.67 H Total (MMR) 3.62 2.77 2.90 1.65 1.52 1.01 0.93 (Source: Draft Mumbai Metropolitan Regional Plan 2016-36)
2.1.1 Mode Share and VKT Calculation After evaluating the policy, the mode share and corresponding Vehicle Kilometers Travelled (VKTs) values for the horizon years 2031 and 2050 are given in the Figures 2.1.1 and 2.1.2, respectively.
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Figure 2.1.1: Mode Share values for BAU & Policy 1- Redistribution of population
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Figure 2.1.2: VKTs obtained for BAU & Policy 1- Redistribution of population
2.2 Draft Mumbai Metropolitan Regional Plan 2016-36 scenario (Policy 2) The population and employment trends and forecast for MMR in BAU scenario are based on CTS data (CTS,2008). However, according to the Draft Mumbai Metropolitan Regional Plan: 2016-36 (RP) “in Post- CTS years, it is evident that the population growth is going to be substantially less, SEZs have not materialized and development may not happen in Uran Pen area as envisaged. Some CTS proposals are implemented and while others have gone modification.” Therefore, in this scenario, the planning
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parameters of population and employment are modified and additional transportation network is incorporated as proposed by the Draft Regional Plan. (MMRDA, 2016)
2.2.1 Population Projections The Draft Mumbai Metropolitan Regional Plan (RP) 2016-36 (MMRDA, 2016) projects the population of MMR in future using various methods. Every constituent of MMR i.e. various census units of MMR such as Municipal Corporations, Municipal Councils, Census Towns and Rural areas (Tehsil wise) have different socioeconomic conditions and show different growth trends. The Draft Regional Plan selects the Trend Base method for population projection to supply basic amenities and modify transit network in MMR. Therefore, the planning inputs for this policy scenario are changed according to the population projections from the Draft Regional Plan (MMRDA, 2016). Table 2.2.1 Comparison of population projections for BAU and Policy Scenarios Population Projection (in Scenario millions) HY2021 HY2031 HY2050 BAU and Policy 27.08 34.00 48.61 1 Policy 2 26.01 28.19 33.62
CAGR in MMR for population projection as per Table 2.1.1 is applied over the 2005 base population for each constituent separately as calculated in the Draft Regional Plan. The CAGR for the years between 2031 and 2041 is assumed constant till 2050. (MMRDA, 2016)
2.2.2 Employment Projections The new Growth Centers and Regional Industrial areas are proposed in the Draft Regional Plan to facilitate employment creation and to bring jobs closer to where people live in Vasai- Virar, Bhiwandi, Thane, Kalyan Dombivali and Panvel areas (RoR of MMR). The four growth centers are identified in the Draft Regional Plan at Vasai, Bhiwandi, Kalyan and Panvel Tehsils. Except the Bhiwandi tehsil, others are located in Municipal and SPA areas of RoR of MMR.
Table 2.2.2 Details of Proposed Growth Centres S. No. Location Distric Village Name Area t (km2) 1 Vasai Palghar Nallasopara, Vasai- Virar Municipal Corporation 6.18 2 Kharbav, Thane Kharbav, Malodi, Paye, Paygaon, Nagle, Thane Municipal 13.08 Bhiwandi Corporation 3 Nilje, Kalyan Thane Bhopar , Sandap, Hedutane , Gharivali , Usarghar, 10.83 Katai,Nilje, Kole 4 Shedung, Raigad Ajiwali, Barwai, Bhingar, Bhingarwadi, Borle, Khanavale, 5.77 Panvel Mohope, Poyanje, Sangde, Shedung Total area 35.86 (Source: Draft Mumbai Metropolitan Regional Plan 2016-36)
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Table 2.2.3 Details of Proposed Regional Industrial Areas S. No. Location Distric Village Name Area t (km2) 1 Bhiwandi-1 Thane Angaon, Nivali, Supegaon 4.09 2 Bhiwandi-2 Thane Sape, Vahuli, Borivali Tarf Sonale, Bhoirgaon, Kukase, Amane 5.43 3 Virar Thane Bhatpada, Kopri, Vasai-Virar Municipal Corporation 12.48 4 Taloje Raigad Chindharan, Mahodar, Kherane Kh., Nitalas, Vavanje, 4.12 5 Khopoli Raigad Talawali, Anjrun, Ghodivali, Kandroli Tarf Boreti, Kelavali, 8.39 Khalapur, Mankivali, Navandhe, Wangani 6 Along Raigad Bahiramwatak, Beneghat, Borwe, Khar Borli, Kolave, Masad Kh, 32.90 Amba Masad Bk, Masad Beli, Narvel, Sarebhag, Shirki, Shirki Chawl River No.1, Wadkhal, Wave, Washi, Dherand, Mankule, Narangi, Shahabaj, Shahapur 7 Khopta Raigad Aware, Bandhpada, Dhasakhosi, Govthane, Jui, Kacherpada, 10.85 Koproli, Pale, Pirkone, Sangpalekhar, Talbandkhar, Vindhane Total area 78.26 (Source: Draft Mumbai Metropolitan Regional Plan 2016-36)
The detailed plans for these areas will be prepared after the Draft Regional Plan is approved which will include area allocations for institutional, research and other regional facilities, strategies for development and expanded Development Control Regulations. Currently, the proposed Growth Centres and Regional Industrial Areas are located across various urbanizable zones, industrial zones and green zones in RoR of MMR. Following methodology is used for modification in the planning parameters of employment for this policy: 1. The ratio of total population projection by CTS and total population projection by Draft Regional Plan is calculated and the planning parameters for employment are modified in the same ratio so as to forecast the total employment for all the horizon years. 2. The distribution of total employment is assumed to shift from the area under Municipal Corporation Greater Mumbai (MCGM) to Rest of the Region of MMR. Due to the unpredictability of exact location and nature of these growth centres and industrial corridors, the total employment in Greater Mumbai, as estimated for the horizon years earlier by modifying the CTS data, is reduced by 10% and is distributed in urban areas and rural areas of RoR in 7:3 ratio respectively. Corresponding modifications are done in the number of employment in the office, industry and other jobs.
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Figure 2.2.1: Proposed Growth Centres and Regional Corridors (Source: Draft Mumbai Metropolitan Regional Plan 2016-36)
2.2.3 Addition of Transport Infrastructure The transportation infrastructure is added to the network as proposed in the Draft Mumbai Metropolitan Regional Plan 2016-36. It is as follows: 1. Suburban Rail - ● Panvel to Karjat ● Panvel to Roha
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2. Metro - ● JVLR-Koparkhairane-Kalyan ● Mira Bhayander- Kharbav-Nashik Road ● Thane-Bhiwandi-Kalyan, and ● Mankhurd-Vashi-Kharghar-Ambernath 3. Tunnels ● Vashi to Kharghar ● Airoli to Katai naka 4. Creek Bridges ● JVLR to Koparkhairane across Thane Creek ● Uran to Rewas across Dharamtar Creek Quantitative changes in the following variables are expected: ● Trip O-D Matrix ● Average Trip Length ● In Vehicle Travel time ● Out of Vehicle Travel Time
2.2.4 Mode Share and VKT Calculation After evaluating the policy, the mode share and corresponding VKTs values for the Horizon years 2031 and 2050 are given in figures 2.2.2 and 2.2.3 respectively.
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Figure 2.2.2: Mode Share values for BAU & Policy 2 Draft Mumbai Metropolitan Regional Plan 2016-36
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Figure 2.2.3: VKTs obtained for BAU & Policy 2 Draft Mumbai Metropolitan Regional Plan 2016-36
2.3 Transport Infrastructure Development (Policy 3) The main objective of this policy scenario is to improve the network conditions and provide quality ridership to public transport users. MMR already has one of the highest Public Transport modal share compared to other Indian cities. However, this seems to be changing. The mode share of public transport in MMR has declined from 88 % in 1992 to 78 % in 2005(MMRDA, 2016). Mumbai has restricted space for new roads infrastructure. Therefore, faster traffic on existing roads becomes a priority. Keeping this in mind, we propose
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay implementation of Bus Rapid Transit System (BRTS) with dedicated/exclusive bus lanes to improve connectivity and to attract more commuters to public transport. According to Experts, BRTS being the comfortable and faster alternative to private mode, BRTS will cater in reduction of number of private vehicle road traffic like cars and two wheelers (Katkurwar, 2015). It will increase capacity, reduce delays and promote more reliability among bus commuters which has been a primary concern for BEST officials in the recent times (Kulkarni, 2016).
Figure 2.3.1 Added BRTS Routes in Network Dedicated Bus corridors for the Horizon Years 2031 and 2050 are suggested on various multi-lane highways like Jogeshwari- Vikhroli Link Road, Western Expressway, Eastern Expressway and Eastern Freeway. This is in line with the recommendations of providing dedicated bus lanes on North- South and East- West arterials in Mumbai under the Strategy for Transportation (Municipal Corporation of Greater Mumbai, 2005). Mumbai, if compared to the capacities achieved by BRTS in Bogota, needs 45000 passengers per hour per direction (pphpd). With a fleet of bi-articulated buses of capacity 270 to 300 plying at a frequency of 21 to 24 seconds, these targets are achievable (Badami, 2005) Following factors of the TDM are influenced by the introduction of the BRTS- ● Mode Share ● PT Routes
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2.3.1 Mode Share and VKT Calculation After evaluating the policy, the mode share and the corresponding VKTs values for the horizon years 2031 and 2050 are given in figures 2.3.2 and 2.3.3 respectively.
Figure 2.3.2: Mode Share values for BAU & Policy 3 Improvement in Transport Infrastructure
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Figure 2.3.3: VKTs obtained for BAU & Policy 3 Improvement in Transport Infrastructure
2.4 Travel Demand Management (Policy 4)
Travel Demand Management (TDM) consists of wide range of policies which directly or indirectly influence the travel behavior of commuters and proactively affect the travel demand within the city boundaries.
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One of the components of TDM can be education, awareness and outreach to make commuters aware of the more sustainable modes of transport. Economic instruments and market mechanisms can be used to discourage the use of private transport which may include additional road user charges such as parking fees, congestion pricing, cordon pricing, registration charges, fuel tax etc. However, one of the biggest challenges of this policy is the inefficiencies and inadequacies in the public transport. Therefore, successful implementation of TDM strategies will require simultaneous improvement in the public transport provisions.
There have been multiple occasions in the past when road user charges in the form of congestion pricing have been advocated as an effective tool for traffic management and sustainable urban transport for Mumbai. In the case of Mumbai Metropolitan Region, Municipal Corporation of Greater Mumbai (MCGM) has introduced the parking policy at the ward level which will charge car owners for off-street parking (public parking lots), on-street parking and residential parking. The differential parking charges will be based on the parking demand, location, time of the day, vehicle type etc. Along with the introduction of parking charges and congestion pricing, increase in conventional fuel prices and registration costs will also affect the travel cost for the private vehicle users in MMR.
Therefore, this policy will be evaluated by changing the travel cost of the `private vehicles’ mode choice in the four-stage travel demand model. The policy is evaluated for two cases with the following assumptions: A. Combined effect of congestion pricing and parking policy resulting in 60% increase in the travel cost of private vehicles in Greater Mumbai. B. Combined effect of increase in fuel pricing and registration cost of vehicles resulting in 10% increase in travel cost of private vehicles in the whole area of MMR.
Following factors of the TDM are influenced by the introduction of the travel demand management policy measures- ● Cost Matrix ● Mode Share
2.4.1 Mode Share and VKT Calculation After evaluating the policies (a) and (b) their mode share and corresponding VKTs values for the Horizon years 2031 and 2050 are given in the figures 2.4.1 to 2.4.4.
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Figure 2.4.1: Mode Share values for BAU & Policy 4 (a) - Travel Demand Management - Congestion Pricing + Parking Policy
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Figure 2.4.2: VKTs obtained for BAU & Policy 4 (a) Travel Demand Management- Congestion Pricing + Parking Policy
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Figure 2.4.3: Mode Share values for BAU & Policy 4 (b) Travel Demand Management – Increase in Registration Cost +Fuel Cost
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
Figure 2.4.4: VKTs obtained for BAU & Policy 4 (b) Travel Demand Management – Increase in Registration Cost +Fuel Cost
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2.5 Improvement in Technology (Policy 5)
The main objective of this scenario is to reduce the vehicular emissions through technology improvement. Technology will play a significant role in climate change mitigation and transforming the transportation sector in the horizon years 2031 and 2050. Some of the measures in the technological transition pathway to achieve the mitigation targets include improved vehicles efficiency, greater use of low-carbon alternative fuels like electric vehicles or biofuels, more efficient information technology systems in transport management etc. For example, The Mumbai Metropolitan Region Development Authority (MMRDA) is currently looking at options to introduce integrated ticketing system for all public transport modes in MMR through a smart card technology which will aid seamless transition for passengers from public transport mode to another.
However, it is not possible to directly incorporate all technology changes in the four stage Travel Demand model. Therefore, we propose to study the impact of uptake of electric vehicles in the Indian market under BAU and other policy scenarios for the horizon years 2031 and 2050. This policy will
calculate the reduction in CO2 and other emissions due to greater penetration of electric vehicles in the Indian market alongside the usage of electric locomotives in trains/ metro/monorail. Assuming that the cost of ownership of electric vehicles will be competitive to that of conventional fuel vehicles, the VKTs and modal share will not see any change.
Electric vehicles penetration in Indian markets is studied in the report titled `Electric Vehicle Scenarios and a Roadmap for India’ under the `Promoting Low Carbon Transport in India’ project by UNEP. The report studies the recent development in the technological research, markets and policy landscape of electric vehicles in India. The report presents three future scenarios namely for passenger transport; BAU, National EV policies (EV) scenario and EV Low Carbon scenario (EV + 20 C) spanning till 2030 with various degrees of assumptions regarding the role of electric vehicles. The most ambitious (EV + 0 2 C) scenario assumes decreased CO2 content of electricity which can largely affect the emissions from transport sector (Shukla et al., 2014).
The report undertakes following assumptions under the (EV + 20 C Scenario):
1. Increased governmental support for electric vehicles going beyond the mandates of The National Electric Mobility Mission Plan 2020 in the form of duty waivers, sales tax reduction, and creation of smart grids and charging facilities, greater incentives for R&D etc. There is also a world-wide drive for technological innovation in EVs which towards reducing costs through economies of scale. 2. Increased carbon tax on conventional fuels along the lines of global 20 C stabilization target. The report shows that under the (EV+2º C Scenario), electric 4- wheeler share would be 40% in 2035 and for electric 2-wheelers, 80% penetration by 2030 which will increase to 100% by 2035.
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
Figure 2.5.1 Share of Electric, Hybrid and Fuel Cell 4-Wheelers in India (Source: Shukla et al. 2014)
Figure 2.5.2 Share of Electric and Hybrid 2-Wheelers in India (Source: Shukla et al. 2014)
In this policy, we assume that the share of 4-wheelers will be 40% in 2031 and will further rise to 100% by 2050. Similar assumption is considered for the share of electric three wheelers, taxis and buses as well. For the two wheelers, we assumed the share of 80% in 2031 and it will further rise to 100% in 2050.
The emissions from electric vehicles are completely indirect or the electricity emissions. These emissions depend upon the source of electricity generation. So, to consider different projections of the electricity grid mix for India in the horizon years 2031 and 2050, four scenarios are assumed:
i. Scenario 1: New Policies Scenario (IEA, 2015)
ii. Scenario 2: Electricity from non-renewable Sources (100%)
iii. Scenario 3: Half electricity from renewable and another half from non- renewable sources (50%-50%)
iv. Scenario 4: Electricity from Renewable Sources (100%)
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
In Scenario 1, the electricity grid mix for future, horizon years 2031 and 2050, is taken from IEA (2015).
Because of these scenarios, Policy 5 has been evaluated in the four subparts i.e. Policy 5 S1, Policy 5 S2, Policy 5 S3 and Policy 5 S4.
The emission factors for electric vehicles in all these four scenarios have already been calculated in BAU Report and these emission factors have been used to estimate emission inventory for this policy.These emission factors are attached in Appendix – A.
2.6 Evaluation of Policy Packages After successful evaluation of individual policy scenarios operating in silos focusing on separate objectives like improvement of transport infrastructure or travel demand management, we evaluate the mitigation potential of policy bundles to analyze the integrated impact of these strategies on reduction
of CO2 and other emissions.
Two Policy bundles are modeled to analyze the combined mitigation effect of the policy scenarios recognizing the impact of promoting the public transport and discouraging the use of private vehicles simultaneously. The bundles are run for the horizon years 2031 and 2050.
1) Policy Bundle 1- BAU + Policy 3+ Policy 4
2) Policy Bundle 2- Policy 2 + Policy 3+ Policy 4
2.6.1 Mode Share and VKT Calculation After evaluating the policy bundles 1 and 2, their mode share and corresponding VKTs values for the Horizon years 2031 and 2050 are given in the figures 2.6.1 to 2.6.4.
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Figure 2.6.1: Mode Share values for BAU & Policy Bundle 1- BAU+ Policy 3+ Policy 4
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Figure 2.6.2: VKTs obtained for BAU & Policy Bundle 1- BAU+ Policy 3+ Policy 4
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
Figure 2.6.3: Mode Share values for BAU & Policy Bundle 2- Policy 2+ Policy 3+ Policy 4
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Figure 2.6.4: VKTs obtained for BAU & Policy Bundle 2- Policy 2+ Policy 3+ Policy 4
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3. Comparison between BAU and Policies
3.1 Mode Share A comparison of mode share obtained after evaluation of polices with BAU scenario is given in the figure 3.1 below:
Figure 3.1 Mode Share comparison of BAU and Polices and Policy Bundles
3.2 Vehicle Kilometers Travelled A comparison of VKTs obtained after evaluation of polices with BAU scenarios is given in the figure 3.2 below:
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
Figure 3.2 Comparison of VKTs of BAU and Evaluated Polices
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
A comparison of VKTs obtained after evaluation of policy bundles with BAU scenarios is given in the figure 3.3 below:
Figure 3.3 Comparison of VKTs of BAU and Policy Bundles
3.3 Emissions The obtained VKTs in evaluated policies are further used to calculate emissions. As the emission factors have already been calculated in the base year and BAU calculations for mainly five pollutants (CO, HC, NOx, CO2 and PM), inventory for a particular pollutant has been generated using the following equation:
�� = (���)� × ��
Here Ei represents emission in gm for the pollutant i, (veh) j represents km travelled by vehicle j and ej is the corresponding calculated emission factor. The calculated emission factors for both electrical and conventional vehicles are attached in Appendix A.
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The estimated emissions (with their categorization as Direct and Indirect) in BAU and policy scenarios in the horizon years 2031 and 2050 are shown in the Tables 3.1 and 3.2. This section also deals with the comparison of BAU Scenario and evaluated policies in terms of the estimated emissions for all the Horizon years assuming the continued usage of the conventional vehicles. Figures 3.5 to 3.9 show the comparison of emission values for BAU and policies except Policy 5. Policy 5 estimates the emissions with the assumption of the uptake of electric vehicles in the BAU scenario. Therefore, emissions under the Policy 5 are compared to the emissions under BAU in the section 3.3.1.
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Table 3.1 Estimated Vehicle Emissions in BAU and Policy Scenarios for the horizon year 2031
2031 Type of Emissions BAU S1 BAU S2 BAU S3 BAU S4 Bundle I Bundle II Policy 1 Policy 2 P CO (Tonne/year) Total emissions 39545 39899 39170 38441 37440 34017 45444 36248 3 Indirect emissions 1344 1698 969 240 1344 1483 1344 1483
Percentage indirect emissions 3.40% 4.25% 2.47% 0.62% 3.59% 4.36% 2.96% 4.09% 3 Direct emissions 38201 38201 38201 38201 36097 32534 44100 34765 3 HC (Tonne/year) Total emissions 6821 6821 6821 6821 6505 5483 8067 5915 Indirect emissions 0 0.32 0.19 0.05 0 0 0 0
Percentage indirect emissions 0.00% 0.00% 0.00% 0.00% 0.00% 0.01% 0.00% 0.00% 0 Direct emissions 6821 6821 6821 6821 6505 5483 8067 5914 NOX (Tonne/year) Total emissions 13730 15011 12375 9738 13612 13664 14291 13843 1 Indirect emissions 5165 6445 3809 1173 5165 5700 5165 5700
Percentage indirect emissions 37.61% 42.94% 30.78% 12.04% 37.94% 41.71% 36.14% 41.17% 3 Direct emissions 8566 8566 8566 8566 8448 7965 9127 8143 CO2 (Tonne/year) Total emissions 8455108 9391793 7463604 5535415 8299658 8204535 9000783 8395421 83 Indirect emissions 2919693 3856377 1928189 0 2919693 3222132 2919693 3222132 29
Percentage indirect emissions 34.53% 41.06% 25.83% 0.00% 35.18% 39.27% 32.44% 38.38% 3 Direct emissions 5535415 5535415 5535415 5535415 5379964 4982403 6081089 5173289 54 PM2.5 (Tonne/year) Total emissions 746 839 648 456 732 719 806 739 Indirect emissions 367 460 268 76 367 404 367 404
Percentage indirect emissions 49.12% 54.77% 41.38% 16.72% 50.09% 56.28% 45.45% 54.72% 4 Direct emissions 380 380 380 380 365 314 440 335
Table 3.2 Estimated Vehicle Emissions in BAU and Policy Scenarios for the horizon year 2050
2050 Type of Emissions BAU S1 BAU S2 BAU S3 BAU S4 Bundle I Bundle II Policy 1 Policy 2 P CO (tonne/year) Total emissions 76335 76913 75813 74712 72647 68171 95254 71124 Indirect emissions 1989 2567 1467 366 1985 2109 1989 2112
Percentage indirect emissions 2.61% 3.34% 1.93% 0.49% 2.73% 3.09% 2.09% 2.97% Direct emissions 74346 74346 74346 74346 70662 66063 93265 69012 HC (tonne/year) Total emissions 10269 10270 10269 10269 9786 8859 12947 9269 Indirect emissions 0 0.49 0.28 0.07 0 0 0 0
Percentage indirect emissions 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Direct emissions 10269 10269 10269 10269 9785 8859 12946 9268 NOX (Tonne/year) Total emissions 15383 16888 14023 11158 15230 15235 16359 15362 Indirect emissions 5623 7127 4262 1397 5612 5960 5623 5971 Percentage indirect emissions 36.55% 42.21% 30.40% 12.52% 36.85% 39.12% 34.37% 38.87%
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Direct emissions 9760 9760 9760 9760 9618 9275 10736 9390 CO2 (Tonne/year) Total emissions 17356622 18861182 15995833 13130484 16954373 16609913 19524073 16936854 1 Indirect emissions 4226137 5730698 2865349 0 4218095 4479682 4226137 4487724 4
Percentage indirect emissions 24.35% 30.38% 17.91% 0.00% 24.88% 26.97% 21.65% 26.50% Direct emissions 13130484 13130484 13130484 13130484 12736278 12130230 15297935 12449129 1 PM2.5 (Tonne/year) Total emissions 845 959 742 524 830 818 934 831 Indirect emissions 412 526 309 91 411 437 412 437
Percentage indirect emissions 48.76% 54.86% 41.62% 17.41% 49.56% 53.39% 44.09% 52.62% Direct emissions 433 433 433 433 418 381 522 394
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Figure 3.4 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios
Figure 3.5 Total CO emissions (tonne/year) in BAU and Policy scenarios
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Figure 3.6 Total HC emissions (tonne/year) in BAU and Policy scenarios
Figure 3.7 Total NOX emissions (tonne/year) in BAU and Policy scenarios
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Figure 3.8 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios
3.3.1 Uptake of EVs
The estimated emissions (with their categorization as Direct and Indirect) in BAU and policy scenarios with Electric Vehicles’ (EV) Uptake considering all the scenarios of the electricity grid mix (S1, S2, S3 and S4) in the horizon years 2031 and 2050 are shown in the tables 3.3 to 3.10. The comparison of emissions between BAU and policy scenarios with the uptake of EVs under all the four scenarios of the electricity grid mix in horizon years 2031 and 2050 for all the pollutants is also presented in the figures 3.9 to 3.28.
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Table 3.3 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 1 for the horizon year 2031 2031 ype of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b Po BAU S1 S1 S1 S1 S1 S1 S1 S1 CO (tonne /year) Total emissions 39545 23120 21985 25315 22583 23285 23439 23535 ndirect emissions 1344 1864 1947 1981 1979 1874 1885 1889 ercentage indirect 3.40% 8.06% 8.86% 7.83% 8.76% 8.05% 8.04% 8.02% emissions Direct emissions 38201 21298 20076 23386 20645 21454 21599 21691 HC (tonne /year) Total emissions 6821 3248 2832 3601 2925 3259 3227 3265 ndirect emissions 0.26 0.36 0.37 0.38 0.38 0.36 0.36 0.36 ercentage indirect 0.00% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% emissions Direct emissions 6821 3248 2832 3601 2925 3259 3227 3264 NOX (tonne /year) Total emissions 13730 13582 13684 14236 13855 13628 13653 13687 ndirect emissions 5165 7188 7504 7642 7626 7226 7267 7283 ercentage indirect 37.61% 52.92% 54.84% 53.68% 55.04% 53.02% 53.23% 53.21% 5 emissions Direct emissions 8566 6580 6346 6822 6406 6592 6578 6599 CO2 (tonne /year) 845510 Total emissions 7959600 7952913 8489137 8095060 7999461 8033585 8057099 80 8 291969 ndirect emissions 3958458 4148396 4191564 4211232 3977812 3999256 4007061 40 3 ercentage indirect 34.53% 49.73% 52.16% 49.38% 52.02% 49.73% 49.78% 49.73% 4 emissions 553541 Direct emissions 4001142 3804516 4297573 3883828 4021648 4034329 4050038 40 5 PM2.5(tonne /year) Total emissions 746 691 695 733 706 694 694 697 ndirect emissions 367 510 533 543 542 513 516 517 ercentage indirect 49.12% 73.88% 76.67% 74.05% 76.69% 74.00% 74.41% 74.26% 7 emissions Direct emissions 380 194 174 207 178 194 192 194
Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 1 for the horizon year 2050 2050 pe of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b P BAU S1 S1 S1 S1 S1 S1 S1 S1 CO (tonne /year) Total emissions 76335 39134 39104 39786 39180 39164 39201 39188 direct emissions 1989 3804 3774 4457 3850 3834 3871 3858
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay rcentage indirect 2.61% 9.72% 9.65% 11.20% 9.83% 9.79% 9.88% 9.85% emissions Direct emissions 74346 35329 35329 35329 35329 35329 35329 35329 HC (tonne /year) Total emissions 10269 4079 4079 4079 4079 4079 4079 4079 direct emissions 0.38 0.73 0.72 0.85 0.74 0.73 0.74 0.74 rcentage indirect 0.00% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% 0.02% emissions Direct emissions 10269 4078 4078 4078 4078 4078 4078 4078 NOX (tonne /year) Total emissions 15383 17178 17093 19023 17308 17262 17367 17330 direct emissions 5623 10754 10670 12599 10884 10839 10943 10907 rcentage indirect 36.55% 62.61% 62.42% 66.23% 62.89% 62.79% 63.01% 62.94% emissions Direct emissions 9760 6423 6423 6423 6423 6423 6423 6423 CO2 (tonne /year) 1735662 Total emissions 15366383 15302784 16753045 15464158 15429913 15508423 15481166 15 2 direct emissions 4226137 8082592 8018993 9469254 8180367 8146122 8224632 8197375 8 rcentage indirect 24.35% 52.60% 52.40% 56.52% 52.90% 52.79% 53.03% 52.95% emissions 1313048 Direct emissions 7283791 7283791 7283791 7283791 7283791 7283791 7283791 7 4 PM2.5(tonne /year) Total emissions 845 984 978 1120 994 991 998 996 direct emissions 412 788 782 923 797 794 802 799 rcentage indirect 48.76% 80.03% 79.91% 82.44% 80.23% 80.16% 80.31% 80.26% emissions Direct emissions 433 197 197 197 197 197 197 197
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Figure 3.9 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake
Figure 3.10 Total CO emissions (tonne/year) in BAU and Policy scenarios with the with the Scenario 1 of the Electric Vehicles’ Uptake
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Figure 3.11 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake
Figure 3.12 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake
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Figure 3.13 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 1 of the Electric Vehicles’ Uptake
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Table 3.5 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 2 for the horizon year 2031 2031 ype of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b Po BAU S2 S2 S2 S2 S2 S2 S2 S2 CO (tonne /year) Total emissions 39899 23600 22488 25823 23093 23767 23924 24021 ndirect emissions 1698 2218 2338 2335 2369 2228 2239 2243 ercentage indirect 4.25% 9.40% 10.40% 9.04% 10.26% 9.37% 9.36% 9.34% emissions Direct emissions 38201 21298 20076 23386 20645 21454 21599 21691 HC (tonne /year) Total emissions 6821 3248 2832 3601 2926 3259 3227 3265 ndirect emissions 0.32 0.42 0.45 0.45 0.45 0.43 0.43 0.43 ercentage indirect 0.00% 0.01% 0.02% 0.01% 0.02% 0.01% 0.01% 0.01% emissions Direct emissions 6821 3248 2832 3601 2925 3259 3227 3264 NOX (tonne /year) Total emissions 15011 15318 15504 16074 15702 15373 15407 15444 ndirect emissions 6445 8469 8917 8923 9039 8506 8548 8563 ercentage indirect 42.94% 55.28% 57.52% 55.51% 57.57% 55.33% 55.48% 55.45% 5 emissions Direct emissions 8566 6580 6346 6822 6406 6592 6578 6599 CO2 (tonne /year) 939179 Total emissions 9229536 9283784 9833858 9446090 9275606 9316610 9342628 93 3 385637 ndirect emissions 4895142 5182108 5128248 5244943 4914496 4935940 4943745 49 7 ercentage indirect 41.06% 53.04% 55.82% 52.15% 55.53% 52.98% 52.98% 52.92% 5 emissions 553541 Direct emissions 4001142 3804516 4297573 3883828 4021648 4034329 4050038 40 5 PM2.5(tonne /year) Total emissions 839 817 827 867 841 820 821 824 ndirect emissions 460 604 636 636 644 606 609 610 ercentage indirect 54.77% 73.86% 76.83% 73.37% 76.66% 73.91% 74.19% 74.04% 7 emissions Direct emissions 380 194 174 207 178 194 192 194
Table 3.6 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 2 for the horizon year 2050 2050 pe of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b P BAU S2 S2 S2 S2 S2 S2 S2 S2 CO (tonne /year) Total emissions 76913 40239 40200 41081 40298 40278 40325 40309 direct emissions 2567 4910 4871 5752 4969 4948 4996 4979 rcentage indirect 3.34% 12.20% 12.12% 14.00% 12.33% 12.29% 12.39% 12.35% emissions
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Direct emissions 74346 35329 35329 35329 35329 35329 35329 35329 HC (tonne /year) Total emissions 10270 4079 4079 4079 4079 4079 4079 4079 direct emissions 0.49 0.94 0.93 1.10 0.95 0.95 0.95 0.95 rcentage indirect 0.00% 0.02% 0.02% 0.03% 0.02% 0.02% 0.02% 0.02% emissions Direct emissions 10269 4078 4078 4078 4078 4078 4078 4078 NOX (tonne /year) Total emissions 16888 20055 19948 22393 20220 20162 20294 20248 direct emissions 7127 13631 13524 15970 13796 13739 13871 13825 rcentage indirect 42.21% 67.97% 67.80% 71.32% 68.23% 68.14% 68.35% 68.28% emissions Direct emissions 9760 6423 6423 6423 6423 6423 6423 6423 CO2 (tonne /year) 1886118 Total emissions 18243891 18157651 20124222 18376475 18330038 18436499 18399539 18 2 direct emissions 5730698 10960100 10873860 12840432 11092685 11046247 11152708 11115748 11 rcentage indirect 30.38% 60.08% 59.89% 63.81% 60.36% 60.26% 60.49% 60.41% emissions 1313048 Direct emissions 7283791 7283791 7283791 7283791 7283791 7283791 7283791 7 4 PM2.5(tonne /year) Total emissions 959 1203 1195 1375 1215 1211 1220 1217 direct emissions 526 1006 998 1179 1018 1014 1024 1021 rcentage indirect 54.86% 83.66% 83.55% 85.71% 83.82% 83.77% 83.90% 83.85% emissions Direct emissions 433 197 197 197 197 197 197 197
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Figure 3.14 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake
Figure 3.15 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake
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Figure 3.16 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake
Figure 3.17 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake
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Figure 3.18 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 2 of the Electric Vehicles’ Uptake
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Table 3.7 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 3 for the horizon year 2031 2031 ype of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b Po BAU S3 S3 S3 S3 S3 S3 S3 S3 CO (tonne /year) Total emissions 39170 22611 21452 24777 22042 22774 22926 23021 ndirect emissions 969 1490 1533 1606 1565 1499 1510 1514 ercentage indirect 2.47% 6.59% 7.15% 6.48% 7.10% 6.58% 6.59% 6.58% emissions Direct emissions 38201 21298 20076 23386 20645 21454 21599 21691 HC (tonne /year) Total emissions 6821 3248 2832 3601 2925 3259 3227 3265 ndirect emissions 0.19 0.28 0.29 0.31 0.30 0.29 0.29 0.29 ercentage indirect 0.00% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% 0.01% emissions Direct emissions 6821 3248 2832 3601 2925 3259 3227 3264 NOX (tonne /year) Total emissions 12375 11744 11758 12290 11900 11781 11796 11826 ndirect emissions 3809 5832 6008 6287 6130 5870 5912 5927 ercentage indirect 30.78% 49.66% 51.10% 51.15% 51.51% 49.83% 50.12% 50.12% 5 emissions Direct emissions 8566 6580 6346 6822 6406 6592 6578 6599 CO2 (tonne /year) 746360 Total emissions 6615339 6544150 7065716 6664959 6648627 6675470 6696333 67 4 192818 ndirect emissions 2966953 3054186 3200059 3117022 2986308 3007751 3015556 30 9 ercentage indirect 25.83% 44.85% 46.67% 45.29% 46.77% 44.92% 45.06% 45.03% 4 emissions 553541 Direct emissions 4001142 3804516 4297573 3883828 4021648 4034329 4050038 40 5 PM2.5(tonne /year) Total emissions 648 557 555 592 564 559 559 561 ndirect emissions 268 412 424 444 433 415 418 419 ercentage indirect 41.38% 73.91% 76.41% 75.10% 76.74% 74.14% 74.75% 74.60% 7 emissions Direct emissions 380 194 174 207 178 194 192 194
Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 3 for the horizon year 2050 2050 pe of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b P BAU S3 S3 S3 S3 S3 S3 S3 S3 CO (tonne /year) Total emissions 75813 38134 38112 38615 38168 38156 38183 38174 direct emissions 1467 2805 2783 3286 2839 2827 2854 2845 rcentage indirect 1.93% 7.36% 7.30% 8.51% 7.44% 7.41% 7.47% 7.45% emissions
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Direct emissions 74346 35329 35329 35329 35329 35329 35329 35329 HC (tonne /year) Total emissions 10269 4079 4079 4079 4079 4079 4079 4079 direct emissions 0.28 0.54 0.53 0.63 0.54 0.54 0.55 0.54 rcentage indirect 0.00% 0.01% 0.01% 0.02% 0.01% 0.01% 0.01% 0.01% emissions Direct emissions 10269 4078 4078 4078 4078 4078 4078 4078 NOX (tonne /year) Total emissions 14023 14575 14511 15974 14674 14640 14719 14691 direct emissions 4262 8152 8088 9551 8251 8216 8295 8268 rcentage indirect 30.40% 55.93% 55.74% 59.79% 56.23% 56.12% 56.36% 56.28% emissions Direct emissions 9760 6423 6423 6423 6423 6423 6423 6423 CO2 (tonne /year) 1599583 Total emissions 12763841 12720721 13704007 12830133 12806915 12860145 12841665 12 3 direct emissions 2865349 5480050 5436930 6420216 5546342 5523124 5576354 5557874 5 rcentage indirect 17.91% 42.93% 42.74% 46.85% 43.23% 43.13% 43.36% 43.28% emissions 1313048 Direct emissions 7283791 7283791 7283791 7283791 7283791 7283791 7283791 7 4 PM2.5(tonne /year) Total emissions 742 787 782 888 794 792 797 795 direct emissions 309 590 586 692 598 595 601 599 rcentage indirect 41.62% 75.02% 74.88% 77.87% 75.25% 75.17% 75.35% 75.29% emissions Direct emissions 433 197 197 197 197 197 197 197
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Figure 3.19 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake
Figure 3.20 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake
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Figure 3.21 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake
Figure 3.22 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake
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Figure 3.23 Total PM2.5 (tonne/year) emissions in BAU and Policy scenarios with the Scenario 3 of the Electric Vehicles’ Uptake
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Table 3.9 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 4 for the horizon year 2031 2031 ype of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b Po BAU S4 S4 S4 S4 S4 S4 S4 S4 CO (tonne /year) Total emissions 38441 21623 20417 23730 20991 21781 21928 22021 ndirect emissions 240 761 729 878 761 770 781 785 ercentage indirect 0.62% 3.52% 3.57% 3.70% 3.62% 3.54% 3.56% 3.57% emissions Direct emissions 38201 21298 20076 23386 20645 21454 21599 21691 HC (tonne /year) Total emissions 6821 3248 2832 3601 2925 3259 3227 3264 ndirect emissions 0.05 0.15 0.14 0.17 0.15 0.15 0.15 0.15 ercentage indirect 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% emissions Direct emissions 6821 3248 2832 3601 2925 3259 3227 3264 NOX (tonne /year) Total emissions 9738 8170 8013 8505 8098 8190 8185 8209 ndirect emissions 1173 3196 3099 3650 3221 3234 3276 3291 ercentage indirect 12.04% 39.12% 38.67% 42.92% 39.78% 39.49% 40.02% 40.09% 4 emissions Direct emissions 8566 6580 6346 6822 6406 6592 6578 6599 CO2 (tonne /year) 553541 Total emissions 4001142 3804516 4297573 3883828 4021648 4034329 4050038 40 5 ndirect emissions 0 1038765 926264 1271871 989100 1058119 1079563 1087368 10 ercentage indirect 0.00% 25.96% 24.35% 29.60% 25.47% 26.31% 26.76% 26.85% 2 emissions 553541 Direct emissions 4001142 3804516 4297573 3883828 4021648 4034329 4050038 40 5 PM2.5 (tonne /year) Total emissions 456 297 283 316 287 298 296 298 ndirect emissions 76 220 213 253 221 223 226 227 ercentage indirect 16.72% 74.04% 75.20% 79.83% 76.95% 74.79% 76.29% 76.13% 7 emissions Direct emissions 380 194 174 207 178 194 192 194
Table 3.4 Estimated Vehicle Emissions in BAU and Policy Scenarios with Electric Vehicles’ Uptake with Electricity Grid Mix under Scenario 4 for the horizon year 2050 2050 pe of Emissions Bundle I Bundle II Policy 1 Policy 2 Policy 3 Policy 4a Policy 4b P BAU S4 S4 S4 S4 S4 S4 S4 S4 CO (tonne /year) Total emissions 74712 36029 36024 36150 36038 36035 36042 36039 direct emissions 366 700 695 820 709 706 712 710 rcentage indirect 0.49% 1.94% 1.93% 2.27% 1.97% 1.96% 1.98% 1.97% emissions Direct emissions 74346 35329 35329 35329 35329 35329 35329 35329
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HC (tonne /year) Total emissions 10269 4078 4078 4078 4078 4078 4078 4078 direct emissions 0.07 0.13 0.13 0.16 0.14 0.13 0.14 0.14 rcentage indirect 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% emissions Direct emissions 10269 4078 4078 4078 4078 4078 4078 4078 NOX (tonne /year) Total emissions 11158 9096 9075 9555 9128 9117 9143 9134 direct emissions 1397 2673 2652 3131 2705 2694 2720 2711 rcentage indirect 12.52% 29.38% 29.22% 32.77% 29.63% 29.55% 29.75% 29.68% emissions Direct emissions 9760 6423 6423 6423 6423 6423 6423 6423 CO2 (tonne /year) 1313048 Total emissions 7283791 7283791 7283791 7283791 7283791 7283791 7283791 7 4 direct emissions 0 0 0 0 0 0 0 0 rcentage indirect 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% emissions 1313048 Direct emissions 7283791 7283791 7283791 7283791 7283791 7283791 7283791 7 4 PM2.5(tonne /year) Total emissions 524 371 370 401 373 372 374 374 direct emissions 91 175 173 204 177 176 178 177 rcentage indirect 17.41% 47.03% 46.83% 50.99% 47.33% 47.23% 47.46% 47.38% emissions Direct emissions 433 197 197 197 197 197 197 197
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Figure 3.24 Total CO2 emissions (Million tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake
Figure 3.25 Total CO emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake
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Figure 3.26 Total HC emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake
Figure 3.27 Total NOX emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake
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Figure 3.28 Total PM2.5 emissions (tonne/year) in BAU and Policy scenarios with the Scenario 4 of the Electric Vehicles’ Uptake
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3.3.2 As per vehicles classes This section deals with the comparison of emissions due to different classes of vehicles in BAU and all policy scenarios. The vehicles of MMR have been divided into Internal and External. All the polices have been applied to Internal Vehicles except Policy 5 i.e. electrification of Vehicles. The electrification has also been applied to the external vehicles. So, for external vehicles, the variation in the emissions is only in the policy 5.
The Comparison of emissions in BAU and all other policy scenarios for the horizon years 2031 and 2050 due to the different vehicle classes of internal and external vehicles is shown in the figures 3.14 to 3.23.
Figure 3.29 Comparison of CO emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR
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Figure 3.30 Comparison of CO emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR
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Figure 3.31 Comparison of HC emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR
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Figure 3.32 Comparison of HC emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR
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Figure 3.33 Comparison of CO2 emissions (Million tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR
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Figure 3.34 Comparison of CO2 emissions (Million tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR
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Figure 3.35 Comparison of NOX emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR
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Figure 3.36 Comparison of NOX emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR
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Figure 3.37 Comparison of PM2.5 emissions (tonne/year) in BAU and Policy Scenarios due to Internal vehicles of MMR
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Figure 3.38 Comparison of PM2.5 emissions (tonne/year) in BAU and Policy Scenarios due to External vehicles of MMR
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3.3.3 Comparison of the total emissions in Rapid Assessment, BAU, Policy Packages This section deals with the comparison of the total emissions in Rapid Assessment, BAU, Policy Packages and the Policy packages with the uptake of Electric vehicles under all the four scenarios of electricity grid mix in the years 2031 (figures 3.39 to 3.43) and 2050 (figures 3.44 to 3.48).
Figure 3.39 Total CO2 emissions (Million tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031
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Figure 3.40 Total CO emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031
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Figure 3.41 Total HC emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031
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Figure 3.42 Total NOx emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031
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Figure 3.43 Total PM2.5 emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2031
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Figure 3.44 Total CO2 emissions (Million tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050
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Figure 3.45 Total CO emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050
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Figure 3.46 Total HC emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050
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Figure 3.47 Total NOx emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050
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Figure 3.48 Total PM2.5 emissions (tonne/year) in RA, BAU, Policy scenarios and Policy scenarios with all the four Scenarios of the Electric Vehicles’ Uptake in year 2050
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4. Conclusions
This report has demonstrated the modeling results and comparisons of VKTs, modal share and emissions as a result of BAU scenario and various policy packages. Policy 1 is the policy in which differential growth rates at municipal corporations’ level are incorporated in the planning parameters. The distribution of population across the constituents of MMR is changed while keeping the total population same as the BAU scenario. This policy scenario exhibits higher VKTs and higher emissions than the BAU scenario in horizon years. Private vehicle trips are increased as there is not enough provision of public transport in the areas with higher growth. VKTs are increasing due to less accessibility to public transport, less employment generation than required and therefore,
increase in the average trip length. Increase in VKTs leads to subsequent increase in the CO2 and other emissions.
Maximum reduction in VKTs and subsequently CO2 emissions for all horizons is observed due to the modelling of Policy Scenario 2 (Draft Mumbai Metropolitan Regional Plan: 2016-36 policies) as
compared to all other policy scenarios. BAU scenario predicts 8.45 million tonne of CO2 emissions in
2031 and 17.35 million tonne of CO2 in 2050. After considering the recent policies proposed by Draft
Regional Plan, CO2 emissions reduce to 8.39 million tonne and 16.94 million tonne, respectively. Modification in the planning parameters of population and employment as per the current policies proposed in the Draft Regional Plan and providing additional transportation network in the RoR of MMR has maximum impact on reduction of emissions in MMR in future. Consequently, Policy bundle 2 comprising of the integration of Policy Scenario 2 with other policy recommendations under the Travel Demand Management and Transport Infrastructure Development show maximum reduction in
CO2 (3-4%) and other emissions among all policy scenarios and bundles. Between the popular mitigation strategies of Travel Demand Management (Policy 4) and Transport Infrastructure Development (Policy 3), Transport Infrastructure Development shows slightly higher mitigation potential with higher reduction in CO2 and other emissions compared to TDM policies.
Congestion pricing and parking policy in combination will reduce CO2 emissions in 2031 to 8.4 million tonnes while increasing the fuel prices and registration cost of vehicles will reduce CO2 emissions to 8.43 million tonnes from 8.45 million tonnes in BAU scenario in the horizon year 2031. The modal share of buses in BAU scenario is 9% and 7.05% in 2031 and 2050, respectively which increases to 16.63% and 13.43% in 2031 and 2050 due to the introduction of BRTS on four multi-lane highways under the Policy 3 scenario of Transport Infrastructure Development. The modal share of Public Transport (metro, mono, train and buses) is 77.27% in the BAU scenario which increases maximum to 80. 67% under the Policy Bundle 1 where the cumulative impact of BAU with TDM and BRTS is assessed.
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Electric Vehicles policy has significant impact on the reduction of CO2 and other emissions for all horizon years. The uptake of electric vehicles under scenario 1 of the electricity grid mix in the BAU scenario itself will reduce the CO2 emissions from 8.45 million tonne to 8.07 million tonne in 2031 which amounts to 5% reduction. Similarly, 10% reduction is observed in 2050 in BAU scenario only due to the electric vehicles Scenario 1 policy. The range of percentage change in emissions under all policy scenarios and bundles after the uptake of electric vehicles under all the scenarios of electricity grid mix and keeping other assumptions constant is in table 4.1 below: Table 4.1 Percentage reduction in emissions across all policy scenarios and bundles after the uptake of electric vehicles Change in Emissions across policy scenarios and bundles due to the uptake of EVs (%) Pollutant (↑): Increase & (↓): Decrease Scenario 1 Scenario 2 Scenario 3 Scenario 4
2031 2050 2031 2050 2031 2050 2031 2050 CO 35-44 (↓) 43-58 (↓) 34-43 (↓) 41-57 (↓) 37-45 (↓) 44-59 (↓) 40-48 (↓) 47-62 (↓) HC 48-55 (↓) 54-68 (↓) 48-55 (↓) 54-68 (↓) 48-55 (↓) 54-68 (↓) 48-55 (↓) 54-69 (↓)
NOx 0.14-0.38 (↑) 12-16 (↑) 12-13 (↑) 31-37 (↑) 13.7-14 (↓) 2-5 (↓) 40-42 (↓) 40-42 (↓)
CO2 3-6 (↓) 8-14 (↓) 9-13 (↑) 3-9 (↑) 20-21 (↓) 23-30 (↓) 52-54 (↓) 56-63 (↓) PM2.5 3-9 (↓) 19-20 (↑) 7-15 (↑) 45-47 (↑) 23-27 (↓) 4-5 (↓) 59-61 (↓) 55-57 (↓)
As a result of the uptake of electric vehicles CO emissions are set to decrease by 35-48 % and 43-68 % in 2031 and 2050, respectively in all the scenarios of the electricity grid mix. Since electricity have negligible HC emissions, these emissions are set to reduce by 48-55% and 54-69% in 2031 and 2050. However, NOx emissions are more from the Non-renewable sources and lesser from the Renewable sources, therefore these emissions are increasing in the scenarios 1 & 2. With increase in the share of renewable sources, NOx emissions are set to reduce by 40 percent in the future years (Scenario 4).
Similarly, CO2 emissions also decrease with increase in the share of electricity from the Renewable
sources as CO2 emissions are reducing by 3-61% and 19-57% in 2031 and 2050 through all the four scenarios. Lastly, PM emissions are also reducing with the increase in the share of electricity from renewable sources and are set to reduce by 60 and 56 % in 2031 and 2050 in the scenario 4. Among all the four scenarios of Electricity grid mix, Scenario 1 (New Policies Scenario) and 3 (50%NRE - 50% RE) are the most plausible ones because of the large dependency of India on Coal. Also, Scenario 2 (100% NRE) seems out of the equation due to the emerging scope of renewable energy in India. However, this scope does not necessarily support the scenario 4 (100% RE) again because of the present grid mix of India. However, there is an increase in the PM and NOx emissions in the scenario 1 due to electrification of vehicles but since these emissions occur outside the cities (Coal fired or Gas- fired or oil fired power plants), these emissions will not affect the health of the residents of the MMR.
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The BAU scenario demonstrates significant effort to incorporate the development of public transportation in Mumbai Metropolitan Region. The recent developments in metro rail, monorail and multi-modal corridors are a part of BAU scenario. The future projections for population and employment growth in MMR in the BAU scenario also follow integrated land use and transportation planning principles. Therefore, policy scenarios like Travel Demand Management or Transport
Infrastructure Development do not exhibit significant reductions in CO2 or other emissions as compared to BAU scenario. As a result, we can safely infer that only the penetration of electric vehicles in the two, three and four wheelers markets in Mumbai Metropolitan Region will lead to significant reduction
in absolute CO2 emissions from transport sector.
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References
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David Newbery ,2013, EPRG and Imperial College London, E&E Seminar, Green Emotion - GEM The Economics of Electric Vehicles Cambridge, Retrieved from URL http://www.eprg.group.cam.ac.uk/wpcontent/uploads/2013/01/EEJan13_EconomicsEVs.pdf
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GHG Inventory 2010. cBalance Solutions Pvt. Ltd – Electricity GHG Inventory. Retrieved from URL http://cbalance.in/wp-content/uploads/2013/01/cbalance_white-paper_Electricity-emission- factors_28Dec2012_revised_V2.pdf.
Global Green Growth Institute and Center for Study of Science, Technology and Policy. (2015). Electric Buses in India: Technology, Policy and Benefits. Seoul, Republic of Korea.
Katkurwar, S. (2015), Mumbai’s first BRTS corridor may house metro in future, Hindustan Times, May 23, 2015. Retrieved from URL: https://www.hindustantimes.com/mumbai/mumbai-s- first-brts-corridor-may-house-metro-in-future/story-SacVWgoD4pOdrrREw4YuNO.html
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Mittal, Moti L., Chhemendra Sharma, and Richa Singh. 2012, "Estimates of emissions from coal fired thermal power plants in India." 2012 International Emission Inventory Conference.
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Municipal Corporation of Greater Mumbai. (2005). Mumbai City Development Plan 2005-2025. Mumbai.
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Saxena, S., Gopal, A., & Phadke, A. (2014). Electrical consumption of two-, three-and four-wheel light-duty electric vehicles in India. Applied Energy, 115, 582-590.
Shukla, P., Dhar, S., Pathak, M., & Bhaskar, K. (2014). Electric Vehicles Scenarios and a Roadmap for India. UNEP DTU Partnership. http://orbit.dtu.dk/files/104752085/Electric_Vehicle_Scenarios_and_a_Roadmap_for_India_u pload.pdf
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Appendix – A Table A-1 Calculated Emission factors for Conventional Vehicles gm/km Vehicle Year CO HC NOx CO2 PM
200 2.444 0.267 0.344 133.58 0.0276 5
202 0.604 0.139 0.178 144.13 0.004 1 Conventional Car/Taxi 203 0.528 0.133 0.130 144.29 0.004 1
205 0.528 0.133 0.020 147.12 0.0038 0
200 0.02291 1.153 0.790 0.112 31.07 5 7
202 0.02288 0.628 0.416 0.122 36.33 1 2 Conventional Two-Wheeler 203 0.933 0.201 0.057 42.71 0.011 1
205 0.994 0.099 0.06 43.61 0.004 0
200 4.468 1.889 0.558 77.17 0.203 5
202 1.550 0.650 0.385 85.51 0.025 Conventional Three- 1 Wheeler 203 0.487 0.316 0.147 81.74 0.017 1
205 0.302 0.206 0.084 67.53 0.011 0
200 7.997 1.601 11.96 789.18 1.38 5 Conventional Bus 202 3.026 0.271 4.641 611.48 0.0851 1
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203 2.877 0.202 1.319 611.15 0.0225 1
205 0.77 2.877 0.191 611.15 0.0178 0 4
200 3.25 1.73 2.59 353.96 0.741 5 6 6 4 5
202 2.67 1.00 1.49 401.25 0.133 1 8 4 6 Conventional LCV 203 0.91 0.94 0.30 401.25 0.015 1 5 6 2
205 0.62 0.94 0.10 401.25 0.005 0 3 6 3
200 14.7 1.85 12.2 811.43 1.715 5 2 2 8 5
202 4.58 0.26 6.11 762.39 0.123 1 3 4 4 Conventional HCV 203 4.34 0.10 1.54 762.39 0.03 1 5 1 9
205 4.34 0.07 0.79 762.39 0.024 0 5 4 5
Table A-2 Calculated Electric Vehicles’ Emission factors (Scenario 1: Average Energy Consumption) gm/km Vehicle Year CO HC NOx CO2 PM
200 0.05 1.10E- 0.06 0.390 129.55 5 8 05 4
202 0.05 1.02E- 0.01 0.243 120.63 1 3 05 7 Electric Car/Taxi 203 0.04 8.97E- 0.01 0.180 101.95 1 7 06 3
205 0.04 7.93E- 0.00 0.117 88.18 0 2 06 9
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200 0.01 2.14E- 0.01 0.076 25.17 5 1 06 2
202 0.01 1.98E- 0.00 0.047 23.44 1 0 06 3 Electric Two-Wheeler 203 0.00 1.74E- 0.00 0.035 19.81 1 9 06 2
205 0.00 1.54E- 0.00 0.023 17.13 0 8 06 2
200 0.02 3.95E- 0.02 0.140 46.52 5 1 06 3
202 0.01 3.65E- 0.00 0.087 43.32 Electric Three- 1 9 06 6 Wheeler 203 0.01 3.22E- 0.00 0.065 36.61 1 7 06 5
205 0.01 2.85E- 0.00 0.042 31.66 0 5 06 3
200 0.50 9.71E- 0.56 3.442 1144.03 5 8 05 8
202 0.47 8.98E- 0.15 2.143 1065.24 1 0 05 3 Electric Bus 203 0.41 7.92E- 0.11 1.592 900.23 1 4 05 3
205 0.36 7.00E- 0.07 1.036 778.63 0 6 05 6
200 2.49 4.77E- 16.91 2.79 5621.01 5 7 04 3 0
202 2.30 4.41E- 10.52 0.75 5233.86 1 8 04 8 0 Suburban Rail/Train 203 2.03 3.89E- 0.55 7.824 4423.12 1 5 04 5
205 1.80 3.44E- 0.37 5.090 3825.67 0 1 04 3
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200 7.95 1.52E- 53.83 17892.6 8.88 5 0 03 7 6 3
202 7.34 1.40E- 33.51 16660.2 2.38 1 8 03 2 8 8
Metro / Mono Rail 203 6.47 1.24E- 24.90 14079.5 1.76 1 9 03 5 7 7
205 5.73 1.10E- 16.20 12177.7 1.18 0 2 03 3 8 7
Table A-3 Calculated Electric Vehicles’ Emission factors (Scenario 2: Average Energy Consumption) gm/km Vehicle Year CO HC NOx CO2 PM
200 0.05 1.10E- 0.06 0.390 129.555 5 8 05 4
203 0.05 1.13E- 0.01 Electric Car/Taxi 0.225 134.65 1 9 05 6
205 0.05 1.02E- 0.01 0.149 119.57 0 4 05 1
200 0.01 2.14E- 0.01 0.076 25.17 5 1 06 2
203 0.01 2.20E- 0.00 Electric Two-Wheeler 0.044 26.16 1 2 06 3
205 0.01 1.99E- 0.00 0.029 23.23 0 0 06 2
200 0.02 3.95E- 0.02 0.140 46.52 5 1 06 3
Electric Three- 203 0.02 4.07E- 0.00 0.081 48.35 Wheeler 1 1 06 6
205 0.01 3.68E- 0.00 0.053 42.94 0 9 06 4
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200 0.50 9.71E- 0.56 3.442 1144.03 5 8 05 8
203 0.52 1.00E- 0.14 Electric Bus 1.987 1189.04 1 3 04 2
205 0.47 9.04E- 0.09 1.313 1055.83 0 3 05 7
200 2.49 4.77E- 16.91 2.79 5621.01 5 7 04 3 0
203 2.57 4.91E- 0.69 Suburban Rail/Train 9.764 5842.13 1 2 04 6
205 2.32 4.44E- 0.47 6.452 5187.66 0 4 04 6
200 7.95 1.52E- 53.83 17892.6 8.88 5 0 03 7 6 3
203 8.18 1.56E- 31.08 18596.5 2.21 1 6 03 0 2 7 Metro / Mono Rail 205 7.39 1.41E- 20.53 16513.2 1.51 0 7 03 8 3 6
Table A-4 Calculated Electric Vehicles’ Emission factors (Scenario 3: Average Energy Consumption) gm/km Vehicle Year CO HC NOx CO2 PM
200 0.05 1.10E- 0.06 0.390 129.555 5 8 05 4
203 0.03 6.46E- 0.00 Electric Car/Taxi 0.133 67.33 1 4 06 9
205 0.03 5.85E- 0.00 0.089 59.78 0 1 06 6
200 0.01 2.14E- 0.01 0.076 25.17 5 1 06 2 Electric Two-Wheeler 203 0.00 1.26E- 0.00 0.026 13.08 1 7 06 2
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
205 0.00 1.14E- 0.00 0.017 11.61 0 6 06 1
200 0.02 3.95E- 0.02 0.140 46.52 5 1 06 3
Electric Three- 203 0.01 2.32E- 0.00 0.048 24.18 Wheeler 1 2 06 3
205 0.01 2.10E- 0.00 0.032 21.47 0 1 06 2
200 0.50 9.71E- 0.56 3.442 1144.03 5 8 05 8
203 0.29 5.71E- 0.08 Electric Bus 1.174 594.52 1 9 05 3
205 0.27 5.16E- 0.05 0.785 527.92 0 0 05 7
200 2.49 4.77E- 16.91 2.79 5621.01 5 7 04 3 0
203 1.46 2.80E- 0.40 Suburban Rail/Train 5.770 2921.06 1 8 04 6
205 1.32 2.54E- 0.27 3.859 2593.83 0 8 04 9
200 7.95 1.52E- 53.83 17892.6 8.88 5 0 03 7 6 3
203 4.67 8.93E- 18.36 1.29 9298.26 1 2 04 8 2 Metro / Mono Rail 205 4.22 8.08E- 12.28 0.88 8256.62 0 6 04 2 9
Table A-5 Calculated Electric Vehicles’ Emission factors (Scenario 4: Average Energy Consumption)
gm/km Vehicle Year CO HC NOx CO2 PM
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200 0.05 1.10E- 0.06 0.390 129.555 5 8 05 4
203 0.00 1.60E- 0.00 Electric Car/Taxi 0.041 0.00 1 8 06 3
205 0.00 1.46E- 0.00 0.029 0.00 0 8 06 2
200 0.01 2.14E- 0.01 0.076 25.17 5 1 06 2
203 0.00 3.11E- 0.00 Electric Two-Wheeler 0.008 0.00 1 2 07 1
205 0.00 2.84E- 0.00 0.006 0.00 0 1 07 0
200 0.02 3.95E- 0.02 0.140 46.52 5 1 06 3
Electric Three- 203 0.00 5.75E- 0.00 0.015 0.00 Wheeler 1 3 07 1
205 0.00 5.24E- 0.00 0.010 0.00 0 3 07 1
200 0.50 9.71E- 0.56 3.442 1144.03 5 8 05 8
203 0.07 1.41E- 0.02 Electric Bus 0.362 0.00 1 4 05 4
205 0.06 1.29E- 0.01 0.257 0.00 0 7 05 7
200 2.49 4.77E- 16.91 2.79 5621.01 5 7 04 3 0
203 0.36 6.95E- 0.11 Suburban Rail/Train 1.777 0.00 1 4 05 5
205 0.33 6.33E- 0.08 1.265 0.00 0 1 05 3
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Evaluation of Mitigation Policy Packages – Mumbai Metropolitan Region | IIT Bombay
200 7.95 1.52E- 53.83 17892.6 8.88 5 0 03 7 6 3
203 1.15 2.21E- 0.36 5.656 0.00 1 7 04 8 Metro / Mono Rail 205 1.05 2.02E- 0.26 4.027 0.00 0 5 04 3
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