ENERGY DEMANDAND EMISSION PATTERN ANALYSIS OF THE TRANSPORT SECTOR OF KARACHI USING LEAP MODEL

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1 ENERGY DEMANDAND EMISSION PATTERN ANALYSIS OF THE TRANSPORT SECTOR OF KARACHI USING LEAP MODEL Sheikh Saeed Ahmad Department of Environmental Sciences Fatima Jinnah Women University ABSTRACT The major aim of this study was to analyze different factors which were driving force behind the pattern of energy study aimed to analyze factors influence energy utilization and resulting level of emissions in Karachi s transport sector by using Long Range Energy Alternatives Planning system (LEAP) from Study was carried out under four alternative scenarios which were CNG scenario, Fuel Efficiency Improvement, Human Power and Modal Shift scenarios along with business-as-usual scenario to evaluate the best possible scenario. Estimation of emission reductions using Fuel Efficiency Improvement scenario gave better understanding. The emission reduction of particulates, NO x, SO x and CO as compared to base scenario till 2030 was 132.6, , and 663 thousand tones respectively. Finally, mitigation measures and strategies are recommended for governmental and non governmental organization as well as policy makers seeking to reduce environmental pollution. KEYWORDS: Energy demand, Emissions, Transport, Karachi, LEAP 1. INTRODUCTION Rapid growth in population resulted in increase in motorization and transport system that is unsustainable for the environment and economy. Similarly, inadequate traffic management strategies have resulted in increase in traffic congestion, longer travel time and higher pollution level that caused adverse impacts on urban environment and sustainable development (1). Karachi is the most populous and largest city of Pakistan, which is situated near the Indus river delta on Arabian Sea. It is the financial and commercial centre of Pakistan. Karachi is located at 24 4 N and E and was the former capital of Pakistan. The area of Karachi is 3,365 sq. km with the population of about more than 14 million (2). During 1990 to 200, the observed growth of vehicles in Karachi was comparatively greater than the population growth. The maximum difference between population and vehicles growth was measured from 2000 to 200. In developing countries the rate of motorization is increasing at a drastic rate. The main reason is insufficient emphasis on the public transport leading to vivid growth in a private transport sector and consequent energy and environmental problems (3). Pakistan is an energy-deficient country. In the year 2000, the gap between demand and supply increased up to 47% (4). Two major energy sources for transport are oil and gas. Pakistan is one of the biggest consumers of oil in transport. Nowadays, there is a rapid increase of the use of gas in the form of CNG in road transport sector and it consumes 4.4% share of the total gas consumption in the country. Government is also promoting and encouraging transport sector to convert in CNG as it will reduce the consumption of gasoline products in Pakistan in the coming years (5). In this research study, the pattern of energy consumption and emissions in Karachi s transport sector was estimated by LEAP model. LEAP is widely accepted as integrated energy planning and climate change mitigation analysis tool and hundreds of different organizations in over 140 countries have been applied this tool (6, 7, ). The aim of this research was to highlight deleterious impacts of air pollution in Karachi generated from transport sector and reduce the air pollution and emission by investigating different options of transport based upon different scenario analysis and to suggest different

2 mitigation measures to reduce the environmental and resources depredation. 2. METHODOLOGY 2.1. The LEAP framework The study was carried out on Karachi transport system. It aimed to analyze the pattern of energy utilization and resulting emissions from passenger transportation along with annual average fuel economy and vehicle travel using LEAP. The Methodological structure of LEAP is illustrated in Fig. (2). The model was divided into four activity levels, with reference to the energy and its associated environmental database. In this study, the selected time period of study was from 2000 to Fig. 2: Methodological structure of LEAP The base level or the first level of aggregation was the Travel demand in passengers (km) estimated up to the year Using the model different scenarios were analyzed for energy consumption and emission factors Calculations For the analysis of different factors following calculations were carried out Eg(t) = ΣVh(t) x VKTh(t) x EFgh(t) x Fh(t) (1) Travel demand (t) = ΣVh(t) x VKTh(t) x vehicle load factor/occupancy rate (t) (2) Where, Eg(t) is the total emission of type g by passenger transportation in tones, Vh(t) was the number of vehicles on the street, VKTh(t) was the average annual distance traveled by a vehicle of type h in year t, EFgh(t) was the emission factor of pollutant type g of vehicle type h in year (t) in g/km, and Fh(t) was the fuel economy of vehicle type h in year (t) in km/l. Energy demand by fuel type was estimated by removing the emission factor in equation (1). The major data used in this study are shown in Table 1 and 2. TABLE 1: ESTIMATION PARAMETERS OF THE EMISSIONS AND ENERGY CONSUMPTION IN 2000 Types Total Number Average Run/day Occupancy Fuel Economy (Km/l) of Vehicles rate Cars Motorcycles/Scooters Rickshaws Taxis Diesel Powered Bus Trucks Tractors Others (Van and Pickup)

3 The registered vehicles data which was obtained from Federal Bureau of Statistics was divided into light duty (cars, jeeps, motorcycles, rickshaws, taxis), heavy-duty (diesel powered buses, trucks and tractors) and others including vans and pickups (9). TABLE 2: EMISSION FACTORS OF VEHICLES FOR DIFFERENT POLLUTANTS IN g/km. Types PM 10 SOx NOx CO Cars/Jeeps Motorcycles/ Scooters Rickshaws Taxi Buses Trucks Tractors Others construction Construction is an important part of LEAP model. BAU scenario is usually considered as baseline scenario and others known as alternative scenarios are compared with it. This study consisted of five scenarios which determined the effect of multiple urban transport strategies that would lessen total energy utilization and emissions in Karachi s transport sector. 1: Base Base year in the research study was year 2000 and 2030 as end year. Business-as-usual/Base scenario generally gave estimations based upon current values and projected values up to : CNG In this scenario it was assumed that diesel operated buses and tractors would be converted to CNG buses and tractors. The increase in conversion of CNG buses and tractors would be annually. This could be achieved by 3% substitution of diesel operated buses and tractors by natural gas. As a result of which energy consumption and emissions would be reduced proportionately. 3: Fuel Efficiency Improvement The fuel efficiency scenario included improvement in fleet structure and operational efficiency by considering inclusion of new vehicles and assuming % improvement in fuel efficiency through gradual fleet renewal of new vehicles and technological solution such as single wide tires and inflated tires etc. As a result of this vehicles would cover more distance in less amount of fuel and there would be a reduction in total emissions. 4: Human power scenario This scenario was based upon man own efforts to promote the use of bicycle as a mode of transport by saying a big no to motorized transport. This could be achieved by 5% decrease in average annual growth rate of cars, motorcycles, rickshaws, taxis and buses. 5: Modal Shift In the base year passenger transport included 2% private vehicles and 1% public transport. In this scenario the assumption was based upon that the share of personal/private transport reduced to 76% by 2030 and public transport increased to 24%. Such modal shift helped to investigate the implication for energy, environment and social factors. 3. RESULTS The main focus of study was passenger modes of transport in road transportation of Karachi and did not include the freight modes. This study included various important factors i.e. total registered vehicles, travel demand, energy demand and level of emissions, whereas total emissions included Particulates, Nitrogen oxides, Sulphur dioxide and Carbon monoxide. The prime objective of this study was to estimate the possible changes in the near future by taking the above mentioned factors into account Base year Base year in the research study was year 2000 and the total time period was extended up to 2030 to predict the potential increase in number of registered vehicle, fuel economy and in total air pollutant emissions such as CO, SO 2, NO x andpm 10 in future. In Karachi total registered vehicle number was thousand in year Fig. 3 depicts that total travel demand in Karachi in year 2000 was billion passenger/km.

4 Trvel dem and (Passenger-km ) R eg io n ; K a rac h i Cars Motorcycle Rickshaws Taxi Buses Trucks Others Billion Passenger-kms Y e a rs Fig. 3: Travel Demand in Karachi in Million Passenger-kms developed by LEAP Fig. 4 showed the total vehicular energy consumption in Karachi in year 2000 i.e thousand liters. For the environmental impact, NOx, SO 2, PM 10 and CO emissions were taken into account. In base year 2000 the Energy Consumption: (Liters) Year: 2000 total emission values for PM 10, SO 2, NO x and CO ranged from 22.7, 110, and 91.3 thousand tonnes respectively (Fig 5). 1,100 1, Cars Motorcycle Rickshaws Taxi Buses Trucks Tractors Others Thousand Liters BAU Karachi Fig. 4: Energy consumption in Karachi in Liters developed by LEAP Fig. 5: Total Particulate, SO 2, NOx and Carbon monoxide emissions in Karachi in 2000 developed by LEAP

5 3.2. Comparison of alternatives and base scenario Base scenario is usually called as reference scenario to which the alternative scenarios are compared. Alternative Cam parison of D ifferent s: (Total registered Vehicles) R egion : Karachi scenarios were built to estimate the amount of reduction in energy consumption and total emissions and to check which one scenario would be more effective Million Vehicles BA U CNG Sce nario Fuel Efficiency Im pro vem ent Hum an Powe r Sce nario M oda l Shift s Fig. 6: Total Registered Vehicles in different scenarios developed by LEAP The Fig. 6 clearly showed that among all these scenarios, the Fuel Efficiency Improvement scenario resulted in maximum reduction in total number of registered vehicles. The total number of registered vehicles in base scenario was 37.3 million while in CNG scenario, fuel efficiency improvement, Human power and modal shift scenario were 33.3 million, 29.4 million, 34.4 million and 33 million respectively. C o m p a r is o n o f D if f e r e n t s c e n a r io s ( E n e r g y C o n s u m p t io n ) R e g i o n : K a r a c h i Million Liters B A U C N G S c e n a r i o F u e l E f fi c ie n c y I m p r o v e m e n t H u m a n P o w e r S c e n a r io M o d a l S h i ft S c e n a r i o s Fig. 7: Different scenarios comparison in terms of energy consumption developed by LEAP The alternative scenarios in Fig. 7 showed decrease in energy consumption. In BAU the value of energy consumption was million liters. However in CNG scenario, fuel efficiency improvement, Human power and modal shift scenario the values were 195.5, 1.3, and million liters respectively highlighting a decreasing trend.

6 Fig. : Comparison of particulates, SO 2, NOx and Carbon monoxide emissions by different scenarios developed by LEAP As far as particulates emissions were concerned Fig explained that CNG and Human Power scenario showed little change as compared to BAU while fuel efficiency improvement and modal shift scenario showed significant reduction. The values of Particulates emissions in BAU was thousand tones while in CNG scenario, fuel efficiency improvement, Human power and modal shift scenario the values were 571.4, 459, 53.9 and thousand tonnes respectively. Like particulates emissions, Sulphur dioxide emissions also indicated declining trend in alternative scenarios and showed a significant decrease in fuel efficiency improvement and modal shift scenario (Fig. ). In base scenario its estimated value was thousand tonnes whereas in CNG and Human power scenario the values were and 1660 thousand tonnes. Modal shift and fuel efficiency improvement scenarios values were 133 and thousand tonnes respectively which were the maximum reduction in emissions. Fig. clearly depicted that fuel efficiency improvement scenario showed maximum reduction in Nitrogen oxides emissions, while the other scenarios showed a slight change. The value of Nitrogen oxides emission in CNG, fuel efficiency improvement, Human power and modal shift scenarios were 11.9, 9.5, 11. and 11.2 million tonnes respectively while in BAU it was 12.3 million tonnes. In the Fig. there was no major difference in case of CO emissions in three scenarios i.e. in CNG, Human power and modal shift scenario, but still the amount was reduced from base scenario. In base scenario, its value was 2912 thousand tonnes whereas in CNG, fuel efficiency improvement, Human power and modal shift scenarios, amount of emissions was , , and thousand tonnes respectively. 4. DISCUSSION Urban transportation is becoming utmost concern in larger cities of developing world. There is a great increase in demand for urban transportation facilities due to the rapid increase in population and spatial expansion (10 and 11). In Asian countries the urban transport sector is growing at an exponential rate. Transport sector is also a detrimental factor for urban air quality leading to serious health problem among mass in these urban centers and also contribution towards the GHG emissions (12). The transport system of Pakistan is based upon road transport to a great extent. The Motorway network and National Highway is 9,574 km long, comprising 3.65% of the entire road network, which bears 0% of Pakistan s total traffic load. In last 10 years, road traffic has grown at a dramatic rate superseding the economy. In Pakistan the rate of population growth is also increasing very faster with the urban population increasing from 2.3% 32.5% from 191 to199 respectively (13). One of the major contributing factors in increasing vehicle ownership is population growth. In Pakistan the number of motor vehicle was 5.5 million in 2005, increased from 2.7 million in 1990 with an increase of over 100%. All over the world, a number of studies have been conducted in order to predict the future requirements of energy and level of emissions and assessment of the capability of alternative scenarios to cut down energy requirements and total level of emissions. In Nepal the LEAP model was used to model the transport sector in

7 2006. Base year in study was 2005 and time period extended i.e. Particulates, NO x, SOx and CO was done. In baseline to The forecast was about the energy consumption, scenario i.e. BAU scenario, the total number of registered total registered vehicles and emissions level. The proposed vehicle increased from thousands to millions. Similar results alternative scenarios in this study resulted in 15% to 20% were obtained for travel demand, energy demand and level reduction in emissions in contrast to BAU scenario. of emissions. Four alternative scenarios were proposed for its solution i.e. CNG scenario, Fuel efficiency improvement, Similarly, in this research of transport sector energy and Human Power and Modal shift scenario. Different emission analysis, 2000 was considered as base year with alternative scenarios of emission analysis of urban transport forecasting up to year The analysis of four pollutants sector of Karachi are given in table (3 and 4). TABLE 3: COMPARISON OF PROPOSED SCENARIOS FOR PARTICULATE AND SULPHUR DIOXIDE EMISSIONS Units: 000Tonnes Analysis Results (Particulate Emissions) Analysis Results (Sulphur dioxide emissions) % Decrease 0.0% 16.6% 14.72% 0.0% 32.% 27.% Table (3) presented the comparison of alternative scenarios for Particulates emissions. There was no considerable change among the four scenarios from 2000 to Table 4.1 clearly depicted that all the alternative scenarios showed The comparison in table (3) also showed that percentage of emission reduction of Sulphur dioxide in 2030 was 7.2, 22.1, 13.3 and 27. percent for CNG, Fuel Efficiency improvement, Human power and Modal shift scenario some degree of reduction in emissions and the maximum respectively. However Modal shift scenario showed reduction was shown in Fuel Efficiency Improvement maximum decrease., which was figured to be 22.4% up to year TABLE 4: COMPARISON OF PROPOSED SCENARIOS FOR NITROGEN OXIDE EMISSIONS AND CARBON MONOOXIDE Units: 000 Tonnes s Analysis Results (Nitrogen oxide emissions) Analysis Results (Carbon monoxide Emissions) Years Years s Years Years Business-as Usual CNG Decreased % Decrease 0.0% 0.6% 3.41% 0.0% 1.17% 7.2% Fuel Efficiency Improvement Decreased % Decrease 0.0% 15.7% 22.4% 0.0% 15.9% 22.1% Human Power Decreased % Decrease 0.0%.2%.9% 0.0% 14% 13.3% Modal shift Decreased Business-as- Usual CNG Decreased % Decrease 0.0% 0.475% 3.06% 0.0% 3.73% 4.67% Fuel Improvement Efficiency

8 Decreased % Decrease 0.0% 15.6% 22.34% 0.0% 15.6% 22.7% Human Power Decreased % Decrease 0.0% 4.37% 4.33% 0.0% 5.47% 3.5% Modal shift Decreased % Decrease 0.0% 10.12%.73% 0.0% 6.73% 6.0% Table (4) explained the assessment of alternative scenarios for NO x. The highest decrease was noted for fuel efficiency improvement scenarios i.e.22.34% and for CNG, Human power and Modal shift scenario, the reduction was 3.06, 4.33 and.73% respectively. The comparison in table (4) also showed that percentage of emission reduction of Carbon monoxide in 2030 were 4.67, 22.7, 3.5 and 6.0% for CNG, Fuel Efficiency improvement, Human power and Modal shift scenario respectively. Fuel efficiency improvement scenario showed maximum decrease. According to the present study, Fuel efficiency improvement scenario proved to be better among the all. Fuel efficiency improvement is an important component of environmental sustainability, because by maintenance and management of automobiles, it results in reduction in energy demand and air pollutant emissions and greenhouse gases. Traffic flow and transport management / planning are necessary steps in the urban areas in order to reduce air pollution but unfortunately these steps have not been given due importance in Pakistan. Technology change like fuel substitution and conversion to less polluted fuels and use of management tools are utmost requirement of the day for effective and efficient implementation of laws for emission control. Similarly, transport planning with maintenance and inspection of vehicle inspection are important tools for emissions reduction. The collaboration of education with transportation laws implementation would encourage the use of Public transport and thus helpful in improving air quality. 1. CONCLUSION This paper presented the energy demand and emission factor in Karachi s transport sector by using LEAP model. The increment in energy requirement and level of pollutants could be limited through measures encouraging fuel substitution to more environmental friendly fuels, proper maintenance of the vehicles and managing the rapidly increasing number of personal vehicles through area licensing schemes and carbon pricing. This will restrict the excessive use of cars which would help to mitigate congestion and pollution problems from transportation.