Transport Policy 12 (2005) 245–254 www.elsevier.com/locate/tranpol
Environmentally sustainable urban transportation—comparative analysis of local emission mitigation strategies vis-a`-vis GHG mitigation strategies Sudhakar Yedla*, Ram M. Shrestha, Gabrial Anandarajah Energy Program, School of Environment, Resources and Development, Asian Institute of Technology, Klong Luang, Pathumthani 12120, Thailand Received 13 May 2003; revised 7 January 2005; accepted 10 February 2005 Available online 1 April 2005
Abstract This study presents the comparison between global emission mitigation strategies (GEMS) and local emission mitigation strategies (LEMS) and their potential in controlling the non-target pollutants/emissions in concurrence with their economic performance. Comparative analysis revealed that strategies targeted at the mitigation of local pollution like total suspended particulate matter (TSP) and hydrocarbons (HC) also shows greater potential in reducing carbon dioxide (CO2) emissions (as non-target emission). In GEMS, 20% CO2 reduction resulted in 14.9% reduction in TSP emission. In LEMS with a 20% TSP reduction, CO2 emission reduction was found to be 15.2%. TSP mitigation strategy not only performed well with non-target global emission but also within local emissions with SOx reduction much higher than that of target pollutant (TSP itself). The HC mitigation strategy was found to be under-performing with most of the non-target pollutants lying far below the target pollutant reduction. The total cost of transportation is found to be in a similar and smaller band across all strategies (both GEMS and LEMS). The HC mitigation strategy resulted in the least cost followed by the CO2 and TSP strategies. TSP strategy of emission reduction while planning the transportation system for a longer period was found more effective than GHG mitigation strategy. Therefore, employing local pollutant mitigation strategies in transportation planning would also cater for the needs of GHG mitigation, which is a key factor in attracting international funding organizations to invest in transport infrastructure development in developing countries. It would also provide equal consensus from local policy makers, environmental activist and also global actors. This presents a base for the argument that the transportation projects need to be looked at in pollution mitigation approach rather than the GHG mitigation approach. q 2005 Elsevier Ltd. All rights reserved. Keywords: GHG mitigation; Local emission mitigation; Transportation planning; Total suspended particulates; Hydrocarbons
1. Introduction Transportation planning is predominantly influenced by the financial constraints and the execution depends to a great extent on international funding. In the recent past, due to the development of global environmental issues, research and planning in urban transportation is biased towards the GHG mitigation strategies. This resulted in increased inputs to reduce GHG emissions, putting less emphasis on local and more serious pollution. This phenomenon is gaining * Corresponding author. Permanent Address. Indira Gandhi Institute of Development Research (IGIDR), Vaidya Marg, Goregaon (E), Mumbai 400 065, India. E-mail addresses:
[email protected] (S. Yedla),
[email protected] (R.M. Shrestha).
0967-070X/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2005.02.003
strength particularly with a view that the developed countries would come forward to develop the infrastructure in the developing world under the clean development mechanism (CDM) and joint implementation (JI) projects. The choice of alternatives in transportation changes considerably with the kind of environmental constraints considered. A GHG mitigation approach may ignore some potential alternative options like compressed natural gas (CNG) technology as transport options, as they are not GHG friendly (TERI, 1997). Similarly, a local emission mitigation approach would result in the selection of CNG vehicles as an improved transportation option, which adds to GHG emissions. This would lead to unsustainable transportation development. Only targeting a reduction in global emissions cannot satisfy the local policy makers and users unless the local pollution is also controlled. At the same time, the transport sector is the one, which needs external support for development and GHG mitigation strategies are found to
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be an attraction to involve the developed countries and international financing agencies (Proost and Braden, 1998). Therefore, there is a need for a base for local policy makers to make a trade off between these two strategies and to go for the best bargains in urban transport sector. This study presents a case of analyzing the potential of both GHG mitigation strategies and local pollution (TSP and HC) mitigation strategies in reducing the non-target emissions and also their economic efficiency. Such comparative analysis, which is missing in the literature, would support the decision making process of choosing mitigation strategies to achieve an environmentally sustainable transport systems with the least financial burden. The present study is a timely effort in those lines, which can provide a strong basis for policy makers to put forward the local interest on an international platform without compromising on the interests of their counterparts. The objective of the present study is to carry out a comparative analysis between GHG mitigation strategies and local pollution mitigation strategies in transportation planning for 20 years (1998–2020) and their potential in reducing non-target pollutants and potential savings in total cost of transportation (addition of fleet and maintenance of the existing fleet for the planning period).
2. Overview of Mumbai transport system Mumbai, known as the commercial capital of India, is a rapidly growing urban centre with increased economic and commercial activity. This rapid growth and resulting rural– urban migration coupled with geographical constraints have resulted in many complex issues viz. increased travel demand, decreased travel time, congestion and environmental pollution (WB, 1997). The population of Mumbai has increased from about 3 million in 1951 to about 11.5 million in 2001, a near 4-fold increase. Increase in per capita income from Rs. 4359 ($961) in 1980–5525 ($121.69) in 1989 (BMRDA, 1995) resulted in increased stock of personalized transport with car ownership rising from 15 to 30 cars per 1000 population. This has further resulted in insufficient road availability to cater for the increasing travel demand. Vehicles growth is not in harmony with the road length leading to increased problems in Mumbai road transport system. Between 1984 and 1997, road length has increased from 1431 to 1752 km (by 321 km) where as the number of vehicles per kilometer length of road has increased from 278 to 416. As a consequence, congestion levels have increased substantially (Brandon and Hommann, 1996). Mumbai transport needs are catered for by suburban railway services provided by the Western and Central Railways, buses, taxis, 3-wheelers, and personalized vehicles. 1
USD is equivalent to 45.4 Indian rupees.
Public transport accounts for more than 80% of the journeys or trips with the rail system and buses having almost equal share between them. However, in terms of passenger kilometers, railways carry nearly four times the traffic carried by the buses because of higher average carrying capacity. Suburban rail services are operating along a network of some 300 km of electrified broad gauge provided by two zones of the Indian Railways transporting about 5.2 million suburban passengers per day through some 2000 daily electric motive unit (EMU) services. However, increased per capita income levels in Mumbai resulted in increased personalized transport also and their increasing trends are alarming. 2.1. Pollution Between 1980 and 1998, the number of registered vehicles has increased from 281,687 to 785,352 and the personal vehicles are expected to grow by five times in the next 20 years time (IGIDR, 2001). This has resulted in increased pollution generation in Mumbai. Air pollution measurement programs over the last decade showed a definite increase in average suspended particle matter (SPM by 24%) and oxides of nitrogen (NOx by 20%) concentrations (WB, 1997) and traffic emissions contribute the major fraction of the overall air pollution. SPM concentrations (annual average, and maximum of 24 h) are much higher than WHO air quality guidelines of 140 mg/m3 at many measuring sites in Mumbai viz. Chembur, Andheri. At certain times, the WHO air quality guideline for SO2 (80 mg/m3) is also exceeded. The emission of various pollutants from urban transportation is expected to grow 3–5 times in the next 20 years (IGIDR, 2001). Hence, it is essential to plan the transport sector expansion for the next 20 years and develop policies accordingly. Such planning should be aiming at energy saving, emission mitigation and improved transportation networks. However, it involves a huge financial burden and involvement of international funding agencies will be unavoidable. GHG mitigation is one such dimension to the urban transportation system, which could catalyze the process of international involvement but that should not over ride the demand for local emission mitigation. In this context, the present study provides valuable input on which strategy to follow for uncompromised achievement of both goals of development and GHG mitigation with the involvement of international agencies and also targeting the mitigation of environmental pollution.
3. Methodology Transportation planning needs the projection of travel demand for the planning period and subsequent projection of vehicular stock to cater for the needs of travel demand. Vehicles are added to the existing fleet so as to meet the projected travel demand. Appropriate technology penetration levels for different technologies are assumed to
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find the distribution of different modes of vehicles to be added. This is done while satisfying a set of constraints and minimizing the costs involved. In a least-cost approach, alternative transportation options are assessed by their unit cost to achieve certain objectives under various constraints. The principle of a least-cost approach is used here to minimize the total personal vehicle cost for urban passenger transportation. The personal vehicle cost includes capital cost and operational and maintenance cost of the vehicles that should be added during the planning horizon and the operational and maintenance cost of the existing vehicles for the passenger transportation. All the costs are expressed as a total net present value to the base year (detailed formulation of objective function is given in Appendix A). The model includes following constraints: Travel demand constraint. The total travel service provided by existing and new vehicles in any year should be greater than or equal to the forecasted demand. Vehicle capacity constraint. The total vehicle-km service provided by any type of vehicle should not exceed its maximum vehicle-km capacity of the total stock of that type of vehicle (i.e. existing and new units added). Vehicle stock constraint. For candidate vehicles, total number of vehicles added to the transport system should not exceed the maximum limit on the number of vehicles that could be added during the planning horizon (which depends on maximum feasible penetration rate). Emission constraint. Annual emission constraints: total emissions of the particular pollutant by all types of vehicles in a year should not exceed the target level of emission of that year; Overall emission constraints: total carbon dioxide emissions by all types of vehicles during the planning horizon should not exceed the target level, depends on overall emission reduction. Various researchers have adopted optimization technique to assess different alternative options in urban transport and energy planning and also to find the optimized cost of transportation or CO2 emission mitigation. Iniyan and Sumathy (2003) have applied an optimal renewable energy mathematical (OREM) model to find the substitution for the renewable energy sources in India over a planning period of 2010–2011 to 2020–2021. This linear programming model allocates optimal renewable energy sources for different end-uses with an objective of minimizing cost/efficiency ratio based on a set of constraints viz. social acceptance, reliability, demand and potential. Optimization models have been applied to find cost-effective solutions with a set of constraints and optimization mode of the RAINS model is one such effort by Cofala et al. (2004). It is a powerful tool that can assist in the search for cost-effective solutions to combat the negative effects of air pollution and it was applied to explore the cost-effective emission control strategies for SO2 emissions in Asia. Set of determinants like people’s exposure to dangerous SO2 levels and total SO2 emissions determine the set of strategies and the optimal cost of abatement.
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To find the least cost combinations of power supply technologies that meet projected power demand in Brazil, Schaeffer and Szklo (2001) have applied a linear programming model that simulated scenarios through changes in emission fees and caps, costs for technologies and demand side efficiency. In an effort to find optimal mix of vehicles in transportation for the control of NOx emissions in Beijing, Shrestha et al. (2005) have used a least-cost vehicular mix optimization model to determine the share of different vehicular modes to meet the projected travel demand and the minimized cost of transportation against various control strategies of NOx by 10–50%. The cost was optimized against a set of constraints like travel requirement constraints, capacity constraints and emission constraints to find the optimal solution. This least cost vehicular mix model was based on supply-side planning network. A similar approach is adopted for in the present study to assess both GHG and local emission mitigation strategies. The comparative analysis of GHG mitigation strategies and local emission mitigation strategies was carried out by running the optimization model for both cases by considering GHG emission constraints (CO2) and local pollutant emission constraints (TSP and HC separately), respectively. The potential of each strategy in controlling the non-target pollutants—CO2 and TSP and HC for local and global strategies, respectively—is assessed by running the optimization model for different levels of mitigation targets. Marginal abatement cost. For all the strategies of emission mitigation the total cost of transportation was determined by running the optimization model. Total cost of transportation was also determined without any emission constraints. Total emission of various pollutants was determined under each strategy. Per unit of pollution abatement cost is represented by the additional cost, which would be incurred for introducing an emission mitigation constraint, i.e. which would result in reduction of emission from the transport sector. This is derived in terms of $/ton of pollutant reduced and is given by the following equation: MAC Z
TC1 K TC0 E0 K E1
where MAC Marginal abatement cost of pollution TC0 Total cost of transportation under base scenario (no constraints) TC1 Total cost of transportation under alternative scenario (constraints) E0 Total emission under base scenario (no constraints) E1 Total emission under alternative scenario (constraints) MAC tends to get negative values if the alternative strategy is cheaper than the base strategy without any emission constraint. It attains a positive value if
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the alternative strategy is costlier than the base case and also less polluting. However, it is possible to get MAC negative if the alternative strategy is costly as well as more polluting. This is possible with some strategies, which result in increase of certain non-target pollutants. In such case the MAC attains a positive value when the strategy is cheaper but is more polluting. 3.1. Potential candidates for energy efficiency and pollution mitigation In a broad sense, the alternative transportation options can be classified in to two categories. One is alternative fuels and the other is alternative and advanced technologies and management practices. The following are the different individual candidates in each of those categories. 3.1.1. Alternative fuels † Compressed natural gas (CNG) for cars/buses/3-wheelers/2-wheelers. † Electric and battery for three wheelers/mini buses/cars. † Electricity for mass rapid transit system (MRTS)/trolley buses, etc. † Duel fueled (hybrid). † Fuel cells for cars. † Ethanol/methanol for cars. † Biofuel (bio-diesel). † LPG for cars/buses. † Fuel quality improvements (unleaded/low sulphur). 3.1.2. Technology and other options † Four stroke-2 wheelers in place of 2-stroke 2 wheelers. † Control devices like magnetizers, catalytic converters, etc. † Inspection and maintenance. † Increased share of public transport. † Efficient vehicles (as in developed countries). † CVID (computer variable ignition device for cars). In the present planning exercise all modes of transport (bus, car, 3-wheelers and 2-wheelers) are selected and the alternative options in the respective modes are chosen as candidate options for the optimal transport planning for Mumbai for the next 20 years. Selection of these alternatives is based on their energy saving potential (ESP), emission reduction potential (ERP) and economic performance (IGIDR, 2002). Based on a major research study by IGIDR (2002) the following alternative options are chosen for the case of Mumbai2. 2
Details on the energy saving, emission mitigation potential and economic performance of these selected alternatives vis-a`-vis other alternatives can be obtained from IGIDR (2002).
Alternative option 1: buses run on compressed natural gas (CNG); Alternative option 2: cars run on Compressed natural gas (CNG); Alternative option 3: replacement of 2-stroke 2-wheelers by 4-stroke 2-wheelers (motorbikes); Alternative option 4: three wheelers running on compressed natural gas (CNG); Alternative option 5: battery operated (BOV) 3-wheelers. Adoption of cleaner fuels is a dominant trend in urban transportation among the cities in Asia and CNG leads the list of cleaner fuels. CNG is particularly good alternative to control emissions from the in-use vehicles. Though other alternative fuels like LPG, application of ethanol and methanol does appear they could not be as successful as CNG (Yedla, 2005). As the Mumbai traffic is dominated by cars and buses, it would be logical to consider buses and cars running on cleaner fuels. Shifting from 2-stroke two wheelers to 4-stroke two wheelers is a recent trend observed in Indian cities. With strong metro system in place, 3-wheelers are the prominent feeder service in Mumbai (Ramanathan, 1999; TERI, 1997). With these circumstantial facts supporting the above mentioned criteria of energy saving, emission reduction and economic viability, the above listed five alternative options have been considered for the case of Mumbai to improve the transportation and control pollution. 3.2. Transportation planning for Mumbai Transportation planning for Mumbai is done for the period of 1998–2020. Output of the model presents the vehicles added to the base stock each year to cater for the needs of the rising transportation demands with a given set of constraints. Selection of different alternative transportation options depends on the emission constraints declared in the model. Emission mitigation targets influence the cost of the total transportation system. This section presents the case of transportation planning for Mumbai without any emission mitigation targets. 3.3. Comparative analysis of emission mitigation strategies The comparative analysis to reveal the strategic approach to the local emission mitigation (LEMS) against the GHG mitigation strategies (GEMS) is done in three steps. In the first step, transportation planning for the Mumbai transport system was done with restrictions on CO2 emissions. Levels of CO2 mitigation targets tried are 5, 10, 15, and 20% of the overall CO2 emissions over the period of 20 years. Under each level of CO2 mitigation target the vehicular mix and the emission of other pollutants is monitored. In the next step, the transportation planning was done with restrictions on total suspended particulate matter with different levels of emission mitigation at 5, 10, 15, and 20% of the overall
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TSP. And the third step presents a similar analysis with emission restrictions on hydrocarbons (HC). In each step, while constraint is declared for one pollutant the other two pollutants under consideration are not given any constraints. Other parameters analyzed in all these three steps apart from the target pollutant include CO2, sulphur dioxide (SOx), NOx, TSP and HC. Percentage change in each of these pollutants with varying levels of mitigation efforts is measured in all the three cases of emission mitigation.
4. Results and discussion 4.1. Transportation planning for Mumbai without efforts for emission mitigation Among the list of alternative options listed in Section 3, the optimization model chose CNG cars over CNG 3 wheelers and CNG buses. It is indeed an interesting observation to note that gasoline cars continue to occupy a major share of vehicular stock. Battery operated vehicles were selected in the beginning of the planning period (2005). Diesel cars were replaced by CNG cars and gasoline cars. However, the share of diesel buses increases ignoring the CNG options completely, which could be due to the huge difference in capital cost of CNG and Diesel buses. There is a clear increase in 4 stroke 2 wheelers over the 2 stroke 2 wheelers even without emission constraints. Vehicular mix for the next 20 years for different modes is given in Table 1. Total emissions of various pollutants and GHG for this vehicular mix are given in Table 2. Over the planning horizon, NOx emission increased by 5.5 times where as CO2 and SOx emissions increased by 4.4 and 4 times, respectively. TSP and HC increased by 3.3 times. This trend of emissions could be attributed to the increased share of CNG vehicles. Increased CNG usage would marginally increase the CO2 emissions. However, this results in decreased TSP and HC emissions. The persistence of diesel buses contributed to the higher growth of SOx.
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4.2. Optimal vehicular mix for CO2 control strategies The emphasis of many development projects on urban transportation research is on the green house gas mitigation as a strategy to attract the participation of developed countries. Inclusion of transport sector projects under the clean development mechanism (CDM) has been in discussions for long time. As a result of this increased focus substantial research inputs have been derived in assessing the potential of various technological and management options of transportation for the mitigation of GHG emissions. A major research project carried out by Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India is one of such efforts in identifying the potential of various alternatives in urban transportation in controlling GHG and other harmful emissions. This section presents the planning of the Mumbai transport system for various target levels of CO2 emission mitigation and the corresponding changes in vehicular mix and total emission levels of various local pollutants. It is observed that increasing CO2 mitigation targets resulted in more penetration of alternative and cleaner transportation options (CNG, BOV, 4-stroke vehicles). The presence of battery operated 3-wheelers (BOV) as an alternative option resulted in complete rejection of CNG 3-wheeler option at all levels of CO2 mitigation targets. Battery operated 3-wheeler has dominated the addition of 3-wheelers for the whole planning period. The number of added gasoline cars was high in the early periods (2005) and remained unchanged in the later time period (2010–2020). Diesel cars did not grow much over the period. The stock of CNG cars did not change much with a certain level of technology penetration assumed at the beginning of the planning period. The stock of diesel buses was found reducing over time with an increasing share of CNG buses. The induction of CNG technology in buses took place in the later part of the planning period (2005 on wards). Greenhouse gas mitigation efforts lead to a mixed mandate for alternative/cleaner fuels. In a study to find the potential options for greenhouse gas mitigation from transport sector it was found that renewable derived alternative fuels can reduce greenhouse gas emissions per
Table 1 Vehicular mix for Mumbai for the next 20 years (1998–2020) Vehicles
Base year
2005
2010
2015
2020
Gasoline 3 wheeler CNG 3 wheeler BOV 3 wheeler Gasoline car Diesel car CNG car Diesel bus CNG bus 2 Stroke 2 wheeler 4 Stroke 2 wheeler
54,757 0 5849 145,461 36,365 101,698 6966 0 233,530 20,866
101,010 0 0 254,895 166,243 14,649 14,647 0 268,320 225,508
114,478 0 0 340,069 189,195 37,785 19,432 0 265,014 468,245
163,300 0 1 655,613 18,619 76,582 24,184 0 109,263 1,087,894
232,323 0 0 833,383 14,895 133,556 33,461 0 156,775 1,534,209
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Table 2 Emissions of various pollutants over 20 years (’000 t) Year
CO2
TSP
SOx
NOx
HC
Base year 2005 2010 2015 2020
1165.97 2298.74 2883.47 3498.26 5170.25
3.64 6.32 8.01 7.92 13.1
1.65 3.14 3.98 5.01 6.63
3.765 8.91 11.69 15.89 20.69
28.09 51.51 60.49 76.33 93.04
unit of energy used by as much as 80%, but supplies are insufficient to meet current demand except at very high prices (Michaelis and Davidson, 1996). Pereira et al. (1997) found that transport sector in Venezuela is more effective in controlling CO2 emissions and switching to larger capacity vehicles and conversion of gasoline vehicles to natural gas vehicles are considered more effective. It was also noticed in the literature that even when the natural gas or other alternative fuels are considered for CO2 mitigation the options were chosen at a later time. Azar et al. (2003), in their attempt to assess fuel choices in urban transportation sector in Sweden under stringent global carbon constraints by using a global energy systems model (GET 1.0) they found that despite the stringent CO2 constraints oil-based fuels remain dominant in the transportation sector over the next 50 years. Once the transition towards alternative fuels takes place, the preferred choice of fuels is hydrogen, even if it is assumed that hydrogen fuel cell vehicles are substantially more costly than methanol fuel cell vehicles. This substantiates the results of the present study. Table 3 presents the total emission of various pollutants under different CO2 mitigation targets. As it can be observed from the table, targeting at different levels of CO2 mitigation resulted in considerable reduction in local pollution as well (20% of TSP and HC, 13% of NOx and 47% of SOx). Fig. 1 presents the trend of variations in nontarget local pollutant emissions under the CO2 mitigation targets. The change in emissions is shown as percentage change. SOx showed a greater response to the CO2 mitigation strategy than any other emission. Higher level of CO2 mitigation targets result in more efficient SOx reduction. Trends of TSP and HC are similar with 20% reduction each under the 30% CO2 reduction target. This could be due to the fact that both these pollutants are emitted mostly by gasoline vehicles. NOx showed comparatively less reduction. Though some variations are noticed in
Percentage Change in Emission
250
50 CO2
40
TSP SOx
30
NOx HC
20 10 0
5%
10%
15%
20%
25%
30%
–10
CO2 mitigation target Fig. 1. Percentage change in pollutants under consideration at different levels of CO2 mitigation targets under GEMS.
individual cases, the overall total emissions remained in a particular trend. And most of the emissions showed improvement. It is interesting to see that there is a considerable improvement in local pollution levels even with GEMS (GHG mitigation strategies). 4.3. Optimal vehicular mix for TSP control strategies This section examines the response of GHG emissions to the TSP emission mitigation strategy (TEMS). TSP has been selected as a candidate due to the fact that it is the most critical pollutant in urban transportation exceeding the allowable limits at most of the monitoring stations. The optimization model was run with TSP emission constraints. Unlike the previous case of GEMS, TSP mitigation resulted in more shift towards the CNG technology and battery operated vehicles. The vehicular mix also suggests that diesel cars and buses are least preferred with no addition of them to the existing fleet of vehicles for the entire planning period. Unlike in the case of CO2 emission mitigation strategies, CNG 3-wheelers are selected in TEMS though it is at the higher level of mitigation targets (20% mitigation target). A similar trend is observed with CNG bus with increased share towards the later part of the time period. This is in spite of the higher capital investments required for CNG buses. The emission levels of various pollutants under different TSP mitigation targets are presented in Table 4. There is a similar reduction in the emission levels of all non-target
Table 3 Total emissions of various pollutants over 20 years (’000 t) under different CO2 mitigation targets
Table 4 Total emissions of various pollutants over 20 years (’000 t) under different TSP mitigation targets
CO2 mitigation target
CO2
TSP
SOx
NOx
HC
TSP mitigation target
TSP
CO2
SOx
NOx
HC
Base 5% 10% 15% 20%
15,040.7 14,162.6 13,550.0 12,799.2 12,048.3
40.20 39.34 37.47 35.53 34.22
20.43 21.04 20.04 18.62 16.17
61.01 60.50 60.65 59.41 57.35
312.09 309.74 289.72 266.04 258.48
Base 5% 10% 15% 20%
40.0 38.01 36.06 40.00 32.16
14,373.79 13,978.90 13,398.64 14,373.79 12,191.79
21.47 20.48 19.43 21.47 16.51
60.37 60.88 60.26 60.37 57.45
315.03 306.86 287.28 315.03 256.42
25
251
20 CO2
20
TSP SOx
15
NOx HC
10 5 0 5%
10%
15%
20%
–5
Percentage Change in Emission
Percentage Change in Emission
S. Yedla et al. / Transport Policy 12 (2005) 245–254
CO2
15
TSP SOx
10
NOx HC
5 0
5%
10%
15%
20%
–5 –10
TSP Mitigation Target
HC Mitigation Target
Fig. 2. Percentage change in pollutants under consideration at different levels of SPM mitigation targets under TEMS.
Fig. 3. Percentage change in pollutants under consideration at different levels of HC mitigation targets under HEMS.
pollutants vis-a`-vis TSP mitigation targets. SOx, a nontarget pollutant showed better reduction levels than that of TSP, the target pollutant. Twenty percent TSP reduction target could reduce SOx by 23%. This could be due to the fact that more diesel vehicles are replaced by gasoline/CNG vehicles resulting in a reduction of SOx emission. HC, with just about 5% reduction is the least affected pollutant under TEMS. CO2 emissions showed a considerable reduction in the similar range of TSP mitigation targets. At 20% CO2 mitigation target, 15% TSP reduction was noticed where as at 20% TSP mitigation target, 15.18% CO2 reduction could be achieved. However, in the latter case the vehicular mix differs from the former. SOx reduction was slightly higher under CO2 mitigation strategies where as the other pollutants followed similar trends both under GEMS and TEMS. Fig. 2 presents the trends of variation in non-target local pollutant emissions under the TSP mitigation targets. The change in emissions is shown as percentage change.
Under this strategy the 4-stroke 2-wheeler population had increased considerably. CNG vehicles and batteryoperated vehicles had a share in the vehicle stock but this happened towards the later part of the planning period (2010–2020). HEMS presented a different case of emissions reduction compared to that of GEMS and TEMS. Table 5 presents the emission levels of various pollutants under different HC mitigation targets. This strategy of reducing HC emissions could not control CO2 emissions substantially with only 9.7% reduction of CO2 against 20% HC reduction target. This is much less than that of CO2 reduction under 20% TSP reduction strategies (15.3%). Hence, for the control of CO2 as a nontarget pollutant, TEMS performed better compared to HEMS. HEMS was found to be doing well in controlling NOx. It showed poor performance in reducing TSP emission with just 8.6% reduction in TSP emission against the 15% reduction under GEMS. Fig. 3 presents the percentage change in different pollutants with HC mitigation targets. One distinct observation from this figure is that reduction of all non-target emissions (CO2, TSP, NOx, and SOx) is less than the target pollutant (HC). At lower level of mitigation targets, the reduction of non-target emissions is almost negligible except in the case of SOx, which showed an increasing trend. Table 6 presents the total cost of transportation under all conditions of mitigation targets and different mitigation strategies. Total cost of transportation under all three
4.4. Optimal vehicular mix for HC emission mitigation strategies (HEMS) This section presents a case where hydrocarbon mitigation targets are set. This has been selected based on the fact that TSP mitigation targets mostly the diesel vehicles and HC mitigation would target the replacement of gasoline vehicles. This would present a perfect comparison for efforts in different directions and the results of those efforts. Table 5 Total emissions of various pollutants over 20 years (’000 t) under different HC mitigation targets
Table 6 Total cost of transportation under different emission reduction targets and different mitigation strategies
HC mitigation target
HC
CO2
TSP
SOx
NOx
Emission reduction target
Base 5% 10% 15% 20%
302.27 292.39 280.88 265.27 249.67
15,002.53 15,039.22 15,017.27 13,907.07 13,538.25
41.3 41.28 41.05 38.94 37.76
24.24 24.88 25.40 22.06 21.38
58.69 58.13 57.60 54.78 54.18
0 5 10 15 20
Total cost of transportation in billion USD CO2 mitigation strategy
TSP mitigation strategy
HC mitigation strategy
4.854 4.862 4.876 4.891 4.908
4.869 4.882 4.897 4.920 5.027
4.854 4.859 4.866 4.876 4.907
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Table 7 Marginal abatement costs of CO2 and TSP under GEMS and TEMS Mitigation target
5% 10% 15% 20%
MAC of CO2 (USD/ton)
MAC of TSP (USD/ton)
CO2 strategy
TSP strategy
HC strategy
CO2 strategy
TSP strategy
HC strategy
9.68 14.55 16.55 18.24
31.65 28.41 31.81 72.09
K160.81 K807.32 19.99 35.99
9.53!103 7.86! 7.90!103 9.09!103
6.42!103 7.11!103 8.59!103 20.2!103
10.4!104 4.13!104 9.13!104 1.47!104
strategies (GEMS, TEMS and HEMS) is very close to each other. Among the three strategies of emission mitigation, cost of transportation appears to be taking least value for HEMS closely followed by GEMS and TEMS. This essentially means that, irrespective of the mitigation strategy adopted the cost of transportation remains least affected. HEMS, which showed less potential in reducing non-target emissions, showed better performance in economic terms. TEMS maintained the balance in both mitigation potential and economic performance. The dynamics of pollutant reduction is well explained by the cost per reduction of every ton of pollutant. Minimized total cost of transportation is used to calculate the marginal abatement cost under each strategy as explained in Section 4.3. 4.5. Marginal abatement cost
80 70 60
CO2 strat TSP strat
50 40 30 20 10 0 0%
5%
5. Conclusions This study examines various strategies to be followed in long term urban transportation planning and designing policies. It was found that GHG mitigation strategies (GEMS) result in the reduction of local pollutants as well. However, in a comparative analysis it was found that strategies targeted at the mitigation of local pollutants like TSP and HC also shows greater potential in reducing CO2 emissions (as non-target emission). TSP mitigation strategy (TEMS) performed well by reducing GHG (non-target emission) at a similar magnitude as that of the target pollutant. In TEMS, SOx reduction (other non-target Cost per ton of TSP reduction (USD)
Cost per ton of CO2 reduction (USD)
Cost per ton of CO2 mitigation revealed a different story. Marginal abatement cost (MAC) of CO2 under different strategies is presented in Table 7. With a CO2 mitigation strategy in urban transportation planning, cost per every ton of CO2 reduction is in the range of 9.68–18 US$. MAC was found to be higher at higher mitigation targets. TSP reduction strategy showed higher MAC for CO2 mitigation (as non-target pollutant) with 31–72 US$ for mitigation targets of 5–20%. However, at a moderate level of mitigation targets, MAC for CO2 under TSP and CO2 mitigation strategies was found to be close. The difference in MAC is increasing at higher mitigation targets as shown in Fig. 4. As it can be seen from the figure, the difference between MAC values under different strategies is less between the mitigation target levels of 5–15%.
Marginal abatement cost for the TSP mitigation is on higher side as the quantity of TSP mitigated is less compared to that of CO2. As it can be observed from Table 7 and Fig. 5, the difference between the marginal abatement costs (MAC) of TSP under GEMS and TEMS is very less. This demonstrates the potential of local pollution mitigation strategies (LEMS) in handling the global emission mitigation. Therefore, employing local pollutant mitigation strategy in transportation planning would also cater the needs of the GHG mitigation, which is a key factor in attracting international funding agencies to invest in transport infrastructure development in developing countries. By employing the local pollutant emission mitigation strategies (LEMS) in urban transportation planning it would be possible to handle both local and global pollutants with equal consensus from local policy makers and environmental activist and global actors.
10%
15%
20%
25%
Percentage reduction Fig. 4. Marginal abatement cost of CO2 under GEMS and LEMS.
2.50E+04
CO2 strat 2.00E+04
TSP strat
1.50E+04 1.00E+04 5.00E+03 0.00E+00 0%
5%
10%
15%
20%
25%
Percentage reduction
Fig. 5. Marginal abatement cost of TSP under GEMS and LEMS.
S. Yedla et al. / Transport Policy 12 (2005) 245–254
but local pollutant) was much higher than that of target pollutant. This makes TEMS a more effective strategy. This presents a basis for the argument that the transportation projects can continue to look at local pollution mitigation approach and still derive effective GHG mitigation credits. HC strategy was found to be under-performing with most of the non-target pollutants lying far below the target pollutant reduction level. It showed more potential for co-local pollutant but very poor performance in reducing GHGs. The total cost of transportation was found to be in similar range for all cases. HC approach resulted in least cost followed by CO2 and TSP strategy. With more reduction of non-target pollutants (both local and global), competitive economic performance and preference from the local policy makers and civil societies, TEMS seems to be more effective than GEMS for long term transportation planning. Therefore, it would be better if the development projects in urban transportation planning and management consider the TSP mitigation strategy rather than the CO2 or GHG mitigation strategy to achieve the same level of effect both locally and globally.
Acknowledgements
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Vidvt km traveled by vehicle mode i, device d, vintage v, year t. A.3. Parameters Cidv discounted capital cost of a vehicle mode i, device d, vintage v; Sidv discounted salvage value of a vehicle mode i, device d, vintage v; Oidvt operating cost of vehicle mode i, device d, vintage v, year t. A.4. Constraints A.4.1. Travel demand constraint † Transport services supply in p-km in year t must be greater than the forecasted demand in the year t. p-km is calculated by multiplying the v-km by occupancy rate. ! I X D X t X Vidvt !OCidt R TDt ct iZ1 dZ1 vZKv
Authors are grateful to Swedish International Development Cooperation Agency (Sida) for supporting this research study, which was undertaken at Asian Institute of Technology (AIT), Thailand. Valuable comments and suggestions from the reviewers and editors of Transport Policy are gratefully acknowledged.
† Total travel services in p-km by each mode of transport services in year t must be greater than that of the minimum level in year t. D X t X
! Vidvt !OCidt R TDmin ci;t it
dZ1 vZKv
Appendix A. Details of the optimization model used
A.4.2. Vehicle capacity constraint
The total cost includes capital cost and operational and maintenance cost of the vehicles that should be added during the planning horizon and the operational and maintenance cost of the existing vehicles for the passenger transportation. All costs are expressed as a total net present value to the base year.
† The total vehicle-km service provided by any type of vehicle should not exceed its maximum vehicle-km capacity of the total stock of that type of vehicle (i.e. existing and new units added). The maximum vehicle-km that can be traveled by a vehicle could be the average vehicle utilization rate.
A.1. Objective function
t X
To minimize total costs (capital, operational and maintenance cost) of new vehicles and operating and maintenance costs of existing as well as new vehicles. I X D X V X
Xidv ðCidv K Sidv Þ C
iZ1 dZ1 vZ1
I X D X
t X
T X
Vidvt !Oidvt
iZ1 dZ1 vZKv tZ1
A.2. Variable Xidv number of vehicle, mode i device d to be commissioned in year v;
! Vidvt %
vZKv
t X
Xidv K
vZKv
tKL Xidv
! max Xidv !Vidt ci;d
vZKv
A.4.3. Vehicle stock constraint † For candidate vehicles, total number of vehicles added to the transport system should not exceed the maximum limit on the number of vehicles that could be added during the planning horizon (which depends on maximum feasible penetration rate). max Xidvt % Xidt
ci;d;t ðFor some selected new technologyÞ
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A.4.4. Emission constraint † Annual emission constraints: total emissions of the particular pollutant by all types of vehicles in a year should not exceed the target level of emission of that year I X D X t X
Vidvt !EFidt % Etmax ct
iZ1 dZ1 vZKv
† Overall emission constraints: total emissions (of the particular pollutant) by all types of vehicles during the planning horizon should not exceed the target level, depends on overall emission reduction. I X D X t X T X
Vidvt !EFidt % Emax
iZ1 dZ1 vZKv tZ1
Where,OCidt—Occupancy rate of a vehicle, mode i, device d, year t; Lidv—life period of a vehicle, mode i device d, vintage v; TDt—Total travel demand in p-km in year t; TDmax it —maximum level of total travel services by transport mode i in year t; TDmin it —minimum levels of total travel max services by transport mode i in year t; Vidt —maximum km max traveled by a vehicle, mode i, device d, year t; Xidt — maximum number of candidate vehicle, mode i, device d, year t; EFidt —Emission-factor of vehicle, mode i device d, year t; Etmax —target level of emission of the particular pollutant in year t from all vehicles; Emax —target level of overall emission of the particular pollutant during the planning period. Further details on the model and various constraints considered can be found in IGIDR (2002) and Ram et al. (2005).
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