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Transportation energy consumption and emissions - a view from city of Indonesia

Transportation energy consumption and emissions - a view from city of Indonesia The purpose of this study is to investigate fuel consumption and road emissions of the transportation sector, thus providing a potential improvement in reducing fuel consumption and emissions. A system dynamics model for road transportation was developed in this study to mimic the fuel consumption and road emission trends of the sector. With the increase of road vehicles population, it is predicted that total fuel consumption and road emission from transportation sector in 2050 will be 62 and 65 times higher than in 2013 level. The increase in the number of private vehicles plays an essential role in escalating road emissions in Padang. The model also predicts that the reduction in the number of private vehicles and integrated public transportation system can result in about 34% reduction of fuel consumption and road emission in 2050. The results provide essential information and can be used by policy makers to meet challenges of decision making to support urban development process. Keywords: Transportation, Fuel consumption, Road emission, System dynamic, Policy mitigation Background reaching 6.45% per year compared to the other sectors In the last few decades, fuel consumption and emission [12]. From the total energy consumption, 30% of the became a serious concern of researchers and policy consumption came from the transportation sector. makers. Global warming, climate change and side effect to Road transport has gradually become an essential part human health are problems that face human kind [1–4]. of the transportation system in cities of Indonesia. As re- Strategic options have to be taken to face this situation. sult, road transport contributes more than 90% to the According to the data published by the International En- total oil consumption and is responsible the increase in ergy Outlook 2011, the consumption of fossil fuels, in the the concentration of Greenhouse gases (GHG) and other global scale, will increase from 354 quadrillion Btu in pollutants. Air pollutants from transportation sources 1990 to 770 quadrillion Btu in 2035 [5]. A significant in- include carbon dioxide (CO ), methane (CH4), nitrous crease is predicted to occur in Non-OECD countries such oxide (N O), carbon monoxide (CO), hydrocarbon (HC) as Malaysia [6, 7], Singapore [8], Brunei [9], and 40 other and particulate matter (PM) [13]. Furthermore, about countries, including Indonesia. Previous studies showed 91% of the total GHG emissions were produced by road that the most significant increase fuel consumption transportation, only about 1% and 8% of the total of and emissions is taking place in cities, where rapidly GHG emission were produced by marine and air trans- increase in urbanization and concentrated economic port, respectively [14]. activities [2, 10, 11]. Hence, fuels (gasoline and diesel fuel) consumed by Furthermore, on a global scale, using the year of 2008 road transportation activities should be put as the prior- as a reference, transportation sector is predicted to have ity action in order to reduce road emissions in the fu- a contribution of 82% to the total increase in the usage ture. Through this study, a system dynamics model is of liquid fuels in 2035 [5]. In the last three years, the developed to estimate and predict the transportation and sector experienced the largest annual growth rate, emissions trends. In light of these study goals, the model was designed consists of two sub models, i.e., transporta- tion model and emission model. Later on, three scenarios * Correspondence: iwan_sukarno81@yahoo.com 1 (a normal growth, a partial effort scenario and an Department of Architecture and Civil Engineering Toyohashi University of Technology, Toyohashi, Japan Department of Industrial Engineering, Andalas University, Padang, Indonesia © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 2 of 11 Fig. 1 Case Study (Padang, Indonesia) integrated transportation scenario) are designed to il- This study is based on the case of Padang city that lo- lustrate the road transportation and emission trends. cated in the West Sumatera, Indonesia, as shown in Fig. 1. Padang is the largest city in the west coast of Methodology Sumatera Island, and the capital city of West Sumatera Over view of this study site province. The city covers an area of 694.96 km and In this study, a transportation model has been developed has a population of 876,678 in 2012 [15]. to estimate the fuel consumption and emission in a city of Indonesia. The system dynamics based on the computer Emissions Fuel simulation model was used to mimic the transportation reduction target consumption New cars and emissions system. Padang, the capital city of West Total road Sumatera was chosen as boundary study. Three categories emissions Average on road of vehicles such as motorcycles, cars, buses, are used in Fuel consumption Total private and this model as representing the common transportation in public vehicles Emission factors the study area, including private vehicles (cars and motor- Average travel cycles) and public vehicles (microbuses, buses, and taxi). distance Total travel However, since this is a local level study, shipping, air and distance long distance fright transportation were omitted in this Fig. 2 Basic causal loop of road transportation model. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 3 of 11 Table 1 Average Fuel Consumption Type of vehicle Average Travel Distance (km/day) Fuel Consumption (km/l) Fuel Consumption (l/vehicles/year) Car 18 12 548 Motorcycle 18 43.85 150 Microbus (petrol) 100 12 3,042 Buses (diesel) 126 10 4,599 Taxi 24 12 730 Similar to other city in developing countries, Padang affects the total of road emissions. The strong link be- faces a rapid transformation, an increase in population, a tween road emission and fuel consumption provides an significant economic growth, and also a rise in the number important insight into the growth of the transportation of vehicles. To support the recovery and development demand. processes, Padang should have a comprehensive study on The model basically consist two sub – models, namely urban energy consumption. This study can be integrated transportation and emission sub-models representing with the long-term urban planning toward sustainable the correlation between road transport and emission in development. cities. Related to the transportation sector, according to the Indonesian Transportation Department, the ratio of the Transportation sub-model population of private vehicles and public vehicles is 98% In this study, the number of private and public vehicles : 2%. During the period of 2000–2013, the growth of pri- is estimated from the historical data of Padang from 1994 vate vehicles reached 12% per year, while the growth of to 2013. The travel distance of public transportation is es- vehicles used in public transportation sector was only timated by multiplying the average distance of the average 2% per year and shows a downward trend. The popula- trip per day. Table 1 shows an assumption that is used in tion of motorcycles had the highest increase. It reached the calculation of fuel consumption. 250 motorcycles per 1000 people respectively. Emission model sub-model Concept of system dynamics With respect to the emissions model, data related to emis- The model is a simplified representation of the real - sions and other urban pollutants produced by fuel com- world phenomenon to make it easier to understand. In bustion were calculated based on fuel consumption and the urban context, system dynamic modeling can help the distance travelled by different transportation modes. the policy maker to meet challenges of decision making Emission factors to trace GHG emissions and other air to support the urban development process [16]. Therefore, pollutants of various types of vehicle are estimated to develop the transportation and emission models, a according to the EURO emissions standard, as shown commercial simulation program called STELLA is used. in Table 2. In order to understand the relationship among various variables in the transportation sector and road emission, a Model development causal loop diagram is developed (Fig. 2). A system dynamic model for transportation and emis- The two main drivers of fuel consumption are the total sions is developed based on a causal loop diagram kilometers travelled by the vehicles and vehicle popula- (Fig. 2). Total vehicles from road transportation are tion. Increasing fuel consumption, in turn, positively quantified based on the number of vehicles and average Table 2 Emission Factors (g/km) Pollutant/vehicle Car Motorcycle Auto Rickshaw Microbus Bus Taxi CO 223.6 26.6 26.6ª 515.2 515.2 208.3 CO 2.2ª 2.2ª 5.5ª 4ª 3.6 0.9 NOx 0.2 0.19 0.3ª 12 12 0.5 CH 0.17 0.18 0.18 0.09 0.09 0.01 SO 0.053 0.013 0.029 1.42 1.42 10.3 PM 0.03 0.05 0.2 0.56 0.56 0.07 HC 0.25 1.42 1ª 0.87 0.87 0.13 Source: Ramachandra and Shetmala, 2009 [10] Ministry of environment regulation, No.04/2009 [20] Sukarno et al. Future Cities and Environment (2016) 2:6 Page 4 of 11 Table 3 Detail of simulation scenarios Variables Scenario 1 Scenario 2 Scenario 3 Normal Growth (reference scenario) Partial effort Integrated transportation Private vehicle growth Continue to increase: 12% p.a Continue to increase: 12% p.a Starting in 2020, the level gradually decreases by 8% Public transport growth Gradually decrease by 1% p.a From 2020, gradually increase From 2020, it gradually increase by 3% (bus type) and gradually increase by 1% (Bus rapid transit) p.a (integrated transportation (Bus rapid by 2% p.a (microbus type) transit, rail transport) Emissions standard Euro II Euro III Euro III Split mode between private 53:47 (based on 2010 condition) Starting in 2020, the level of From 2020, gradually increase to 70% and public transportation public transportation gradual increases to 50% The above assumptions of future trends are adapted from Transport Master Plan of Padang 2030, and the authors’ own rationales Emission standard is adopted from the Ministry of Environment Regulation growth rate of vehicles in a year per different vehicle X TFC ¼ ðÞ V  AD FE ð2Þ i i i;km type, which is given by: TV ðÞ t ¼ TVðÞ t−dt þðÞ V  GRV dt ð1Þ Where, TFC = fuel consumption; V = vehicles per i i i i type (i); AD = average distance of vehicle per type (i); Where, TV = total vehicles per type (i); V = vehicles FC = fuel economy from vehicle type (i) per driven i i i,km per type (i); GRV = growth rate of vehicles per type (i). kilometer. Fuel consumption is calculated based on the multipli- Furthermore, emission from road transportation is es- cation of vehicle mileage and fuel economy of each type timated based on the number of vehicles and total travel of vehicle, and is given by: distance per different vehicle type, and is given by: Fig. 3 The stock - flow diagram of transportation and emissions model Sukarno et al. Future Cities and Environment (2016) 2:6 Page 5 of 11 Table 4 Energy consumption in the transportation sector (Thousand BOE) 2006 2007 2008 2009 2010 2011 2012 2013 Gas 42 49 124 191 195 181 154 185 Fuel types Avgas 19 12 11 9 12 13 14 16 Avtur 14,303 14,845 15,526 16,262 20,779 20,983 22,967 24,499 premium 92,901 98,847 111,377 121,226 130,486 144,330 160,910 166,800 Bio premium 9 326 257 617 0 0 0 0 Pertamax 2,947 2,752 1,736 3,478 3,985 3,643 3,884 4,934 Bio pertamax 0 58 95 118 0 0 0 0 Pertamax plus 748 921 669 829 971 1,717 870 931 Bio solar 1,408 5,692 6,041 15,558 28,503 46,583 60,132 70,932 Kerosene 22 22 18 11 6 4 3 3 ADO 57,268 55,241 60,812 67,328 70,655 59,672 61,092 54,940 IDO 105 57 34 29 35 26 20 15 FO 314 269 194 163 244 158 215 124 Total Fuel 170,044 179,042 196,770 225,628 255,676 277,129 310,107 323,194 Electricity 41 52 50 68 54 54 66 79 Source : [21] ADO automotive diesel oil, IDO Industrial Diesel Oil, FO fuel oil, BOE barrel oil equivalent increased from 170 thousand BOE to 323 thousand BOE E ¼ V  TD  EF ð3Þ i j j i;j; per year, equivalent to an annual growth rate of 8.1%. It Where, E = emission (i); V = vehicles per type (j); EF = is also can be seen that premium and ADO had the i j i,j emission factor of emission (i) from vehicle type (j). highest growth rate. This was due to the high intensity In this transportation and emissions model, it is as- in motor vehicles usage. The Indonesian National Police sumed that future fuel consumption and air emission reported that from over 86 million vehicles in 2013, 80% trends are mainly affected by vehicle growth rate, emission were motorcycles, 11% were passenger cars, 6% were standard, and split mode between private and public uses trucks, and 3% were buses [17]. Over the past 5 years, as shown in Table 3. Three test scenarios are carried out the Indonesia’s vehicles market was grew over 20% and using this model in order to investigate and predict future continue increase [18]. fuel consumption and air emissions trends. Scenario 1 is called a reference scenario whereby it is Reference scenario assumed that the simulation runs based on the existing In the reference scenario, the current growth rate of trend of vehicle growth rate and transportation split various type of vehicles (private and public) will con- mode. In scenarios 2 and 3, it is assumed that, starting tinue to happen until the year of 2050 without any major from year of 2020 it has major change in several condi- tions. Prior to 2020, the trends are assumed to be the same as scenario 1, and based on the implementation of Transport Master Plan of Padang 2030 some major im- provements on public transportation system will be taking place on/after the year of 2020. Detailed overview of the test scenarios and the stock - flow diagram of the transportation and emissions produced are shown in Table 3 and Fig. 3. Result and discussion Current status Table 4 shows the historical fuel consumption level of road vehicles in Indonesia from 2006 to 2013. From the Fig. 4 Trend of transportation modes table, it can be inferred that the fuel consumption level Sukarno et al. Future Cities and Environment (2016) 2:6 Page 6 of 11 Fig. 5 Scenario 1- vehicle population and fuel consumption interruption from the present policy. As a part of the In scenarios 2 and 3, it was assumed that major public transportation, the growth rate of city buses grad- changes will be made by the government in the year of ually decreases by 1% per year, while microbuses con- 2020. As a consequence, the model predicted that the tinuously increase by 2 % per year. The decrease in the population of the vehicles for each scenario will be equal growth rate of city buses was assumed due to the shift, from the year of 2013 to 2020, as shown in the figure from city buses to other vehicle types (microbuses or above. Due to the various changes starting from the year motorcycles), made by passengers. This phenomenon 2020 such as vehicle growth rate, implementation of rail can be seen from the General Plan for Road Transport transport, and split mode transportation, several results Network (RUJTJ) of Padang for the years of 2004–2013 of scenarios were seen after the year of 2020. Under the (Fig. 4). When scenario 1 was applied to the model and reference scenario (S1), both of vehicle populations (pri- setting the split mode ratio between private and public vate and public) experience a steady increase until year vehicles equals to 53:47, the model predicted that popu- 2050. Regarding private vehicles, the model predicted lation of vehicles and the amount of fuel consumed will that the number of motorcycles in 2050 will be 66 times increase, as depicted in Fig. 5. higher than the number in 2013 and the population of Moreover, from Table 5, it also can be seen that the passenger cars in 2050 will be 24 times larger than their predicted emission level in 2050 is 65 times higher than population in 2013. Even though with partial effort by the emission level in the year of 2013. Emission analysis implementation of bus rapid transit (BRT), the number based on vehicle type reveals that motorcycles contrib- of private vehicles was predicted to be same as their ute higher CO and other pollutant compounds in the population in the reference scenario. In this scenario, a year of 2050 (CO : 53.8%, CH : 91.4%, CO: 90.8%, HC: slight increase in the number of vehicles used as a means 2 4 98.7%, NOx: 85%, PM: 93.3%, SO : 65%) compared to of public transportation was due to the increase in the other road vehicles such as buses, microbuses, taxi and population of city buses. According to the master plan of cars. Padang transportation 2010–2030, the implementation of BRT is one of the alternative solutions to decrease the Overall simulation usage of privately owned vehicles. When scenario 3 was Figures 6 and 7 present the predicted population of pri- applied to the model, it was predicted that, with the im- vate and public vehicles in Padang. plementation of an integrated transportation system Table 5 Scenario 1- Total emissions from road transport (Kg/km) Vehicles 2013 2050 type CO CH CO HC Nox PM SO CO CH CO HC Nox PM SO 2 4 2 2 4 2 Citybus 4,424 0.8 30.9 7.5 103 4.8 12.2 3,050 0.5 21.3 5.2 71 3.3 8.4 Microbus 50,850 8.9 395 85.9 1,184 55.3 140.2 105,803 18.5 821 178.7 2,464 115.0 291.6 Taxi 1,115 0.8 11.0 1.2 1.0 0.1 0.3 1,611 1.2 15.9 1.8 1.4 0.2 0.4 Car 170,652 130 1,679 191 153 22.9 40.4 4,138,954 3,147 40,723 4,628 3,702 555.3 981.1 Motorcycle 74,860 507 6,191 5,403 535 141 37 4,958,141 33,551 410,072 357,881 35,415 9,320 2,423 Sukarno et al. Future Cities and Environment (2016) 2:6 Page 7 of 11 Fig. 6 Private vehicle population consisting of BRT and rail transport, the total number the partial effort applied to both scenarios. A better re- of vehicles used as a means of public transportation will sult is shown in scenario 3. The total fuel consumption be decreased. This is due to the decrease in the number in 2050 is successfully suppressed to 36 times lower of microbuses (seven passengers). Furthermore, the model than that of the consumption in 2013. The reason for also predicted that the implementation of an integrated this is the implementation of an integrated public mass transportation system will lead to the reduction in transportation system. the number of microbuses and private vehicles. Under With the implementation of an integrated public trans- scenario 3, the population of microbuses will be reduced portation system, which was set to happen in 2020 in the to 30%. simulation, the decrease in road emissions level produced In terms of fuel consumption, the simulation result is by road vehicles can be observed in Table 6. It was found shown in Fig. 8. For scenarios 1 and 2, the total fuel con- that the emission level of scenario 3 was 34% lower than sumption almost has the same pattern. This is due to the emission level of scenario 1, and 17% lower than the Fig. 7 Public vehicle population Sukarno et al. Future Cities and Environment (2016) 2:6 Page 8 of 11 Fig. 8 Public vehicles population emission level of scenario 2. The important point pre- Decree No. 61 in 2011 regarding the National Action sented from these results is the emissions produced by Plan for Reducing Emissions of Greenhouse Gases motorcycles. From the simulation, it was found that the (RAN-GRK) [19]. It is a working document containing reduction in the population of private vehicles and the im- measures to reduce greenhouse gas emissions in plementation of an integrated public transportation sys- Indonesia. Hence, the Ministry of Transportation pro- tem played a significant role in decreasing emission from posed nine main strategies for energy conservation in road transport. Presently, about 70% of the total amount the transportation sector [14], as listed in Table 7. of emission is caused by motorcycles. Based on the RAN-GRK, there are seven action plans Thus, from the overall results it was found that having proposed in Padang, Indonesia, including (1) the refor- a split mode of 70:30 between private and public trans- mation of a Bus Rapid Transit (BRT) system; (2) the re- portation provided a good result in terms of fuel con- newal of public transportation vehicles; (3) socialization sumption and emissions. However, to implement this and training of smart driving; (4) non-motorized transport scenario in the real world is not easy. Hence, this elem- development; (5) intelligent transport system development; ent must be a priority for urban energy studies in the fu- (6) the implementation of Traffic Impact Control (TIC); ture, and should be integrated with the long-term urban and (7) parking Management Application. The plan adopts planning toward sustainable development. a new paradigm of enhancing sustainable transportation development to reduce energy consumption and GHG Policy mitigation and opportunities to reduce road emissions from the transportation sector, called the avoid- transport emissions shift-improve approach [3]. “Avoid” or “reduce” can be In order to follow up the Bali Action Plan at the Confer- achieved by reducing the need of travel through infrastruc- ences of Parties (COP 13) to the United Nations Frame- ture planning and trip management. The “shift” means works Convention on Climate Change (UNFCCC) and switching from private vehicles to the environmentally obtain the same results as that from COP-15 in friendly public transport. “Improve” means increasing the Copenhagen and COP-16 in Cancun, the Government of energy efficiency of vehicle technology. Indonesia commits to reduce greenhouse gas emissions by Furthermore, the Indonesian government has realized 26% by its own efforts, and reach 41% if it receives inter- the importance of reducing the GHG emissions. Recently, national assistance in 2020 from the condition without an the Indonesian government encourages the concept of a action plan. To comply with this commitment, the Presi- low carbon society. There are three main strategies for re- dent of the Republic of Indonesia issued Presidential ducing the GHG emissions, which are listed and described in Table 8. Generally, a low carbon society can be achieved Table 6 Total emissions of scenario 3- year 2050 (kg/day) through the integration of public transport. As shown in Vehicles type CO CH CO HC Nox PM SO 2 4 2 the simulation results, the growth of GHG emissions can City bus 6,140 1.1 43 10 143 7 17 be reduced by reducing private vehicle ownership. One of the options is the integration of transportation systems Microbus 50,750 9 394 86 1,182 55 140 such as train and bus modes. Furthermore, a light-rail Taxi 1,028 0.8 10 1.1 0.9 0.1 0.2 transit system, Bus Rapid Transit, and non-motorized Car 4,223,145 3,211 37,774 4,722 3,777 567 1,001 transport should be integrated with land-use and urban Motorcycle 3,883,347 26,278 291,981 43,797 21,899 7,300 1,898 planning. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 9 of 11 Table 7 National Action Plan for Reducing Emissions of Greenhouse Gases (RAN-GRK) No Action Plans Key point (s) Location 1 Reformation of Bus Rapid Transit - Implementation of mass transit 12 cities in Indonesia: (BRT) system - Road based using buses which uses a special line. Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 2 Renew of public transportation vehicles - Evaluation of public transportation vehicles 12 cities in Indonesia: - Emission inspection Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, - Vehicle life expectancy Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin - Change with new vehicles 3 Installation of “converter kits” for public - Installation of “converter kits” for public vehicles to nine cities in Indonesia: vehicles replace oil fuel use with natural gas. Medan, Palembang, Jabodetabek, Cilegon, Cirebon, Surabaya, Denpasar, - Reduce CO2 emission to 20% Balikpapan and Sengkang 4 Socialization and training of smart driving Teaching and training the environmentally friendly 12 cities in Indonesia: driving to save fuel and to reduce air pollution. Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 5 Non motorized transport development - Increasing pedestrian and bicycle paths 12 cities in Indonesia: - Integrated with public transport planning and air Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, quality planning Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 6 Intelligent transport system development � Improving the communication and information 13 cities in Indonesia: system in the public transport Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, ➢ Travel routes Surabaya, Denpasar, Makasar, Balikpapan, Banjarmasin and Jabodetabek ➢ Cut travel time (Jakarta, Bogor, Depok, Tanggerang and Bekasi) � Decrease GHG emissions 7 Implementation of Traffic Impact � Land use and transport planning 12 cities in Indonesia: Control (TIC) � Travel demand management Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, � Integrated public transport Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 8 Parking Management Application � Anti idling regulation 12 cities in Indonesia: � Transport demand management (TDM) Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 9 Implementation of Congestion Charging � Decrease the private vehicles on the road two cities in Indonesia: and Road Pricing � Integrate with mass transportation Jakarta and Surabaya Sukarno et al. Future Cities and Environment (2016) 2:6 Page 10 of 11 Table 8 Strategies to achieve a low carbon society Strategy Measure Opportunity Stakeholders Reducing per kilometer emissions Improvement of emissions standards for new - Adopted Euro 3 emissions standard - National Government agencies: vehicle technology - Can be promoted by using cleaner fuel MEMR, MoE, MoI, MoT such as natural gas, CNG, and biofuel - Automotive industry - Combined with Bus Rapid Transit Improvement of land use and transport planning - Integration of transportation planning - Local government and air quality - Urban planners - A significant political will is necessary Reducing per unit transport vehicle Enforcement of routine inspection of vehicle - Requires a regulation mechanism - Local government and policy emissions emissions - Enforcement to prevent corruption makers - Traffic agencies - Vehicle owners Enforcement of mass transport (buses or trains) - Integration of transportation planning - National Government; MEMR, and private sectors MoE, MoI, and MoT - Non-motorized transport development - Local government - Private sector Reducing travel distance - Travel demand management - Changing the behavior of society - Urban planner - Non-motorized transport - Significant regulation necessary - National Government; MoE, MoI, - Land use and transportation planning - Integration of public transportation and and MoT urban planning - Local government - Policy makers Sources : [14] MEMR Ministry of Energy and Mineral Resource, MoE Ministry of Environment, MoI Ministry of Industry, MoT Ministry of Transportation Sukarno et al. Future Cities and Environment (2016) 2:6 Page 11 of 11 Conclusion Received: 27 January 2016 Accepted: 3 June 2016 In order to project fuel consumption and emission from the road sector in Padang from 2013 to 2050, an inte- References grated system dynamics model was developed under 1. Colvile RN, Hutchinson EJ, Mindell JS, Warren RF (2001) The Transport sector as a source of air pollution. Atmos Environ 35(9):1537–1565 three different scenarios. Although it is a basic model 2. Fong WK (2008) A study on the prediction and control of urban energy with various limitations as mentioned above, it provides consumption and carbon dioxide emissions. Doctoral thesis of Toyohashi to capture energy consumption and emission trends. University of technology, Japan 3. Ratanavaraha V, Jomnonkwao S (2015) Trends in Thailand CO2 emissions in The results show that Padang, in the small scope, will be the transportation sector and Policy Mitigation. 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Accessed on May emissions of greenhouses gases; SO , sulfure dioxide; TIC, traffic impact 2015. (in Indonesia version). control; UNFCCC, United Nations Framework Convention on Climate Change 21. CDIEMR (2014) Handbook of Energy and Economic Statistic of Indonesia 2014. Center for data and information on energy and mineral resources. Acknowledgements Ministry of Energy and Mineral Resources. Jakarta. The author would like to thank the Padang City Government, Padang Statistical Bureau, the Ministry of Transportation, the Ministry of Environment and the Ministry of Energy and Mineral Resource of Republic of Indonesia for the data support. Authors’ contributions IS, HM and LS contribute to data collection and verification. IS and HM participated in design of the case report and drafting the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Future Cities and Environment Springer Journals

Transportation energy consumption and emissions - a view from city of Indonesia

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Springer Journals
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Copyright © 2016 by The Author(s)
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Energy; Energy Efficiency (incl. Buildings); Renewable and Green Energy; Energy Technology; Landscape/Regional and Urban Planning
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2363-9075
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10.1186/s40984-016-0019-x
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Abstract

The purpose of this study is to investigate fuel consumption and road emissions of the transportation sector, thus providing a potential improvement in reducing fuel consumption and emissions. A system dynamics model for road transportation was developed in this study to mimic the fuel consumption and road emission trends of the sector. With the increase of road vehicles population, it is predicted that total fuel consumption and road emission from transportation sector in 2050 will be 62 and 65 times higher than in 2013 level. The increase in the number of private vehicles plays an essential role in escalating road emissions in Padang. The model also predicts that the reduction in the number of private vehicles and integrated public transportation system can result in about 34% reduction of fuel consumption and road emission in 2050. The results provide essential information and can be used by policy makers to meet challenges of decision making to support urban development process. Keywords: Transportation, Fuel consumption, Road emission, System dynamic, Policy mitigation Background reaching 6.45% per year compared to the other sectors In the last few decades, fuel consumption and emission [12]. From the total energy consumption, 30% of the became a serious concern of researchers and policy consumption came from the transportation sector. makers. Global warming, climate change and side effect to Road transport has gradually become an essential part human health are problems that face human kind [1–4]. of the transportation system in cities of Indonesia. As re- Strategic options have to be taken to face this situation. sult, road transport contributes more than 90% to the According to the data published by the International En- total oil consumption and is responsible the increase in ergy Outlook 2011, the consumption of fossil fuels, in the the concentration of Greenhouse gases (GHG) and other global scale, will increase from 354 quadrillion Btu in pollutants. Air pollutants from transportation sources 1990 to 770 quadrillion Btu in 2035 [5]. A significant in- include carbon dioxide (CO ), methane (CH4), nitrous crease is predicted to occur in Non-OECD countries such oxide (N O), carbon monoxide (CO), hydrocarbon (HC) as Malaysia [6, 7], Singapore [8], Brunei [9], and 40 other and particulate matter (PM) [13]. Furthermore, about countries, including Indonesia. Previous studies showed 91% of the total GHG emissions were produced by road that the most significant increase fuel consumption transportation, only about 1% and 8% of the total of and emissions is taking place in cities, where rapidly GHG emission were produced by marine and air trans- increase in urbanization and concentrated economic port, respectively [14]. activities [2, 10, 11]. Hence, fuels (gasoline and diesel fuel) consumed by Furthermore, on a global scale, using the year of 2008 road transportation activities should be put as the prior- as a reference, transportation sector is predicted to have ity action in order to reduce road emissions in the fu- a contribution of 82% to the total increase in the usage ture. Through this study, a system dynamics model is of liquid fuels in 2035 [5]. In the last three years, the developed to estimate and predict the transportation and sector experienced the largest annual growth rate, emissions trends. In light of these study goals, the model was designed consists of two sub models, i.e., transporta- tion model and emission model. Later on, three scenarios * Correspondence: iwan_sukarno81@yahoo.com 1 (a normal growth, a partial effort scenario and an Department of Architecture and Civil Engineering Toyohashi University of Technology, Toyohashi, Japan Department of Industrial Engineering, Andalas University, Padang, Indonesia © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 2 of 11 Fig. 1 Case Study (Padang, Indonesia) integrated transportation scenario) are designed to il- This study is based on the case of Padang city that lo- lustrate the road transportation and emission trends. cated in the West Sumatera, Indonesia, as shown in Fig. 1. Padang is the largest city in the west coast of Methodology Sumatera Island, and the capital city of West Sumatera Over view of this study site province. The city covers an area of 694.96 km and In this study, a transportation model has been developed has a population of 876,678 in 2012 [15]. to estimate the fuel consumption and emission in a city of Indonesia. The system dynamics based on the computer Emissions Fuel simulation model was used to mimic the transportation reduction target consumption New cars and emissions system. Padang, the capital city of West Total road Sumatera was chosen as boundary study. Three categories emissions Average on road of vehicles such as motorcycles, cars, buses, are used in Fuel consumption Total private and this model as representing the common transportation in public vehicles Emission factors the study area, including private vehicles (cars and motor- Average travel cycles) and public vehicles (microbuses, buses, and taxi). distance Total travel However, since this is a local level study, shipping, air and distance long distance fright transportation were omitted in this Fig. 2 Basic causal loop of road transportation model. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 3 of 11 Table 1 Average Fuel Consumption Type of vehicle Average Travel Distance (km/day) Fuel Consumption (km/l) Fuel Consumption (l/vehicles/year) Car 18 12 548 Motorcycle 18 43.85 150 Microbus (petrol) 100 12 3,042 Buses (diesel) 126 10 4,599 Taxi 24 12 730 Similar to other city in developing countries, Padang affects the total of road emissions. The strong link be- faces a rapid transformation, an increase in population, a tween road emission and fuel consumption provides an significant economic growth, and also a rise in the number important insight into the growth of the transportation of vehicles. To support the recovery and development demand. processes, Padang should have a comprehensive study on The model basically consist two sub – models, namely urban energy consumption. This study can be integrated transportation and emission sub-models representing with the long-term urban planning toward sustainable the correlation between road transport and emission in development. cities. Related to the transportation sector, according to the Indonesian Transportation Department, the ratio of the Transportation sub-model population of private vehicles and public vehicles is 98% In this study, the number of private and public vehicles : 2%. During the period of 2000–2013, the growth of pri- is estimated from the historical data of Padang from 1994 vate vehicles reached 12% per year, while the growth of to 2013. The travel distance of public transportation is es- vehicles used in public transportation sector was only timated by multiplying the average distance of the average 2% per year and shows a downward trend. The popula- trip per day. Table 1 shows an assumption that is used in tion of motorcycles had the highest increase. It reached the calculation of fuel consumption. 250 motorcycles per 1000 people respectively. Emission model sub-model Concept of system dynamics With respect to the emissions model, data related to emis- The model is a simplified representation of the real - sions and other urban pollutants produced by fuel com- world phenomenon to make it easier to understand. In bustion were calculated based on fuel consumption and the urban context, system dynamic modeling can help the distance travelled by different transportation modes. the policy maker to meet challenges of decision making Emission factors to trace GHG emissions and other air to support the urban development process [16]. Therefore, pollutants of various types of vehicle are estimated to develop the transportation and emission models, a according to the EURO emissions standard, as shown commercial simulation program called STELLA is used. in Table 2. In order to understand the relationship among various variables in the transportation sector and road emission, a Model development causal loop diagram is developed (Fig. 2). A system dynamic model for transportation and emis- The two main drivers of fuel consumption are the total sions is developed based on a causal loop diagram kilometers travelled by the vehicles and vehicle popula- (Fig. 2). Total vehicles from road transportation are tion. Increasing fuel consumption, in turn, positively quantified based on the number of vehicles and average Table 2 Emission Factors (g/km) Pollutant/vehicle Car Motorcycle Auto Rickshaw Microbus Bus Taxi CO 223.6 26.6 26.6ª 515.2 515.2 208.3 CO 2.2ª 2.2ª 5.5ª 4ª 3.6 0.9 NOx 0.2 0.19 0.3ª 12 12 0.5 CH 0.17 0.18 0.18 0.09 0.09 0.01 SO 0.053 0.013 0.029 1.42 1.42 10.3 PM 0.03 0.05 0.2 0.56 0.56 0.07 HC 0.25 1.42 1ª 0.87 0.87 0.13 Source: Ramachandra and Shetmala, 2009 [10] Ministry of environment regulation, No.04/2009 [20] Sukarno et al. Future Cities and Environment (2016) 2:6 Page 4 of 11 Table 3 Detail of simulation scenarios Variables Scenario 1 Scenario 2 Scenario 3 Normal Growth (reference scenario) Partial effort Integrated transportation Private vehicle growth Continue to increase: 12% p.a Continue to increase: 12% p.a Starting in 2020, the level gradually decreases by 8% Public transport growth Gradually decrease by 1% p.a From 2020, gradually increase From 2020, it gradually increase by 3% (bus type) and gradually increase by 1% (Bus rapid transit) p.a (integrated transportation (Bus rapid by 2% p.a (microbus type) transit, rail transport) Emissions standard Euro II Euro III Euro III Split mode between private 53:47 (based on 2010 condition) Starting in 2020, the level of From 2020, gradually increase to 70% and public transportation public transportation gradual increases to 50% The above assumptions of future trends are adapted from Transport Master Plan of Padang 2030, and the authors’ own rationales Emission standard is adopted from the Ministry of Environment Regulation growth rate of vehicles in a year per different vehicle X TFC ¼ ðÞ V  AD FE ð2Þ i i i;km type, which is given by: TV ðÞ t ¼ TVðÞ t−dt þðÞ V  GRV dt ð1Þ Where, TFC = fuel consumption; V = vehicles per i i i i type (i); AD = average distance of vehicle per type (i); Where, TV = total vehicles per type (i); V = vehicles FC = fuel economy from vehicle type (i) per driven i i i,km per type (i); GRV = growth rate of vehicles per type (i). kilometer. Fuel consumption is calculated based on the multipli- Furthermore, emission from road transportation is es- cation of vehicle mileage and fuel economy of each type timated based on the number of vehicles and total travel of vehicle, and is given by: distance per different vehicle type, and is given by: Fig. 3 The stock - flow diagram of transportation and emissions model Sukarno et al. Future Cities and Environment (2016) 2:6 Page 5 of 11 Table 4 Energy consumption in the transportation sector (Thousand BOE) 2006 2007 2008 2009 2010 2011 2012 2013 Gas 42 49 124 191 195 181 154 185 Fuel types Avgas 19 12 11 9 12 13 14 16 Avtur 14,303 14,845 15,526 16,262 20,779 20,983 22,967 24,499 premium 92,901 98,847 111,377 121,226 130,486 144,330 160,910 166,800 Bio premium 9 326 257 617 0 0 0 0 Pertamax 2,947 2,752 1,736 3,478 3,985 3,643 3,884 4,934 Bio pertamax 0 58 95 118 0 0 0 0 Pertamax plus 748 921 669 829 971 1,717 870 931 Bio solar 1,408 5,692 6,041 15,558 28,503 46,583 60,132 70,932 Kerosene 22 22 18 11 6 4 3 3 ADO 57,268 55,241 60,812 67,328 70,655 59,672 61,092 54,940 IDO 105 57 34 29 35 26 20 15 FO 314 269 194 163 244 158 215 124 Total Fuel 170,044 179,042 196,770 225,628 255,676 277,129 310,107 323,194 Electricity 41 52 50 68 54 54 66 79 Source : [21] ADO automotive diesel oil, IDO Industrial Diesel Oil, FO fuel oil, BOE barrel oil equivalent increased from 170 thousand BOE to 323 thousand BOE E ¼ V  TD  EF ð3Þ i j j i;j; per year, equivalent to an annual growth rate of 8.1%. It Where, E = emission (i); V = vehicles per type (j); EF = is also can be seen that premium and ADO had the i j i,j emission factor of emission (i) from vehicle type (j). highest growth rate. This was due to the high intensity In this transportation and emissions model, it is as- in motor vehicles usage. The Indonesian National Police sumed that future fuel consumption and air emission reported that from over 86 million vehicles in 2013, 80% trends are mainly affected by vehicle growth rate, emission were motorcycles, 11% were passenger cars, 6% were standard, and split mode between private and public uses trucks, and 3% were buses [17]. Over the past 5 years, as shown in Table 3. Three test scenarios are carried out the Indonesia’s vehicles market was grew over 20% and using this model in order to investigate and predict future continue increase [18]. fuel consumption and air emissions trends. Scenario 1 is called a reference scenario whereby it is Reference scenario assumed that the simulation runs based on the existing In the reference scenario, the current growth rate of trend of vehicle growth rate and transportation split various type of vehicles (private and public) will con- mode. In scenarios 2 and 3, it is assumed that, starting tinue to happen until the year of 2050 without any major from year of 2020 it has major change in several condi- tions. Prior to 2020, the trends are assumed to be the same as scenario 1, and based on the implementation of Transport Master Plan of Padang 2030 some major im- provements on public transportation system will be taking place on/after the year of 2020. Detailed overview of the test scenarios and the stock - flow diagram of the transportation and emissions produced are shown in Table 3 and Fig. 3. Result and discussion Current status Table 4 shows the historical fuel consumption level of road vehicles in Indonesia from 2006 to 2013. From the Fig. 4 Trend of transportation modes table, it can be inferred that the fuel consumption level Sukarno et al. Future Cities and Environment (2016) 2:6 Page 6 of 11 Fig. 5 Scenario 1- vehicle population and fuel consumption interruption from the present policy. As a part of the In scenarios 2 and 3, it was assumed that major public transportation, the growth rate of city buses grad- changes will be made by the government in the year of ually decreases by 1% per year, while microbuses con- 2020. As a consequence, the model predicted that the tinuously increase by 2 % per year. The decrease in the population of the vehicles for each scenario will be equal growth rate of city buses was assumed due to the shift, from the year of 2013 to 2020, as shown in the figure from city buses to other vehicle types (microbuses or above. Due to the various changes starting from the year motorcycles), made by passengers. This phenomenon 2020 such as vehicle growth rate, implementation of rail can be seen from the General Plan for Road Transport transport, and split mode transportation, several results Network (RUJTJ) of Padang for the years of 2004–2013 of scenarios were seen after the year of 2020. Under the (Fig. 4). When scenario 1 was applied to the model and reference scenario (S1), both of vehicle populations (pri- setting the split mode ratio between private and public vate and public) experience a steady increase until year vehicles equals to 53:47, the model predicted that popu- 2050. Regarding private vehicles, the model predicted lation of vehicles and the amount of fuel consumed will that the number of motorcycles in 2050 will be 66 times increase, as depicted in Fig. 5. higher than the number in 2013 and the population of Moreover, from Table 5, it also can be seen that the passenger cars in 2050 will be 24 times larger than their predicted emission level in 2050 is 65 times higher than population in 2013. Even though with partial effort by the emission level in the year of 2013. Emission analysis implementation of bus rapid transit (BRT), the number based on vehicle type reveals that motorcycles contrib- of private vehicles was predicted to be same as their ute higher CO and other pollutant compounds in the population in the reference scenario. In this scenario, a year of 2050 (CO : 53.8%, CH : 91.4%, CO: 90.8%, HC: slight increase in the number of vehicles used as a means 2 4 98.7%, NOx: 85%, PM: 93.3%, SO : 65%) compared to of public transportation was due to the increase in the other road vehicles such as buses, microbuses, taxi and population of city buses. According to the master plan of cars. Padang transportation 2010–2030, the implementation of BRT is one of the alternative solutions to decrease the Overall simulation usage of privately owned vehicles. When scenario 3 was Figures 6 and 7 present the predicted population of pri- applied to the model, it was predicted that, with the im- vate and public vehicles in Padang. plementation of an integrated transportation system Table 5 Scenario 1- Total emissions from road transport (Kg/km) Vehicles 2013 2050 type CO CH CO HC Nox PM SO CO CH CO HC Nox PM SO 2 4 2 2 4 2 Citybus 4,424 0.8 30.9 7.5 103 4.8 12.2 3,050 0.5 21.3 5.2 71 3.3 8.4 Microbus 50,850 8.9 395 85.9 1,184 55.3 140.2 105,803 18.5 821 178.7 2,464 115.0 291.6 Taxi 1,115 0.8 11.0 1.2 1.0 0.1 0.3 1,611 1.2 15.9 1.8 1.4 0.2 0.4 Car 170,652 130 1,679 191 153 22.9 40.4 4,138,954 3,147 40,723 4,628 3,702 555.3 981.1 Motorcycle 74,860 507 6,191 5,403 535 141 37 4,958,141 33,551 410,072 357,881 35,415 9,320 2,423 Sukarno et al. Future Cities and Environment (2016) 2:6 Page 7 of 11 Fig. 6 Private vehicle population consisting of BRT and rail transport, the total number the partial effort applied to both scenarios. A better re- of vehicles used as a means of public transportation will sult is shown in scenario 3. The total fuel consumption be decreased. This is due to the decrease in the number in 2050 is successfully suppressed to 36 times lower of microbuses (seven passengers). Furthermore, the model than that of the consumption in 2013. The reason for also predicted that the implementation of an integrated this is the implementation of an integrated public mass transportation system will lead to the reduction in transportation system. the number of microbuses and private vehicles. Under With the implementation of an integrated public trans- scenario 3, the population of microbuses will be reduced portation system, which was set to happen in 2020 in the to 30%. simulation, the decrease in road emissions level produced In terms of fuel consumption, the simulation result is by road vehicles can be observed in Table 6. It was found shown in Fig. 8. For scenarios 1 and 2, the total fuel con- that the emission level of scenario 3 was 34% lower than sumption almost has the same pattern. This is due to the emission level of scenario 1, and 17% lower than the Fig. 7 Public vehicle population Sukarno et al. Future Cities and Environment (2016) 2:6 Page 8 of 11 Fig. 8 Public vehicles population emission level of scenario 2. The important point pre- Decree No. 61 in 2011 regarding the National Action sented from these results is the emissions produced by Plan for Reducing Emissions of Greenhouse Gases motorcycles. From the simulation, it was found that the (RAN-GRK) [19]. It is a working document containing reduction in the population of private vehicles and the im- measures to reduce greenhouse gas emissions in plementation of an integrated public transportation sys- Indonesia. Hence, the Ministry of Transportation pro- tem played a significant role in decreasing emission from posed nine main strategies for energy conservation in road transport. Presently, about 70% of the total amount the transportation sector [14], as listed in Table 7. of emission is caused by motorcycles. Based on the RAN-GRK, there are seven action plans Thus, from the overall results it was found that having proposed in Padang, Indonesia, including (1) the refor- a split mode of 70:30 between private and public trans- mation of a Bus Rapid Transit (BRT) system; (2) the re- portation provided a good result in terms of fuel con- newal of public transportation vehicles; (3) socialization sumption and emissions. However, to implement this and training of smart driving; (4) non-motorized transport scenario in the real world is not easy. Hence, this elem- development; (5) intelligent transport system development; ent must be a priority for urban energy studies in the fu- (6) the implementation of Traffic Impact Control (TIC); ture, and should be integrated with the long-term urban and (7) parking Management Application. The plan adopts planning toward sustainable development. a new paradigm of enhancing sustainable transportation development to reduce energy consumption and GHG Policy mitigation and opportunities to reduce road emissions from the transportation sector, called the avoid- transport emissions shift-improve approach [3]. “Avoid” or “reduce” can be In order to follow up the Bali Action Plan at the Confer- achieved by reducing the need of travel through infrastruc- ences of Parties (COP 13) to the United Nations Frame- ture planning and trip management. The “shift” means works Convention on Climate Change (UNFCCC) and switching from private vehicles to the environmentally obtain the same results as that from COP-15 in friendly public transport. “Improve” means increasing the Copenhagen and COP-16 in Cancun, the Government of energy efficiency of vehicle technology. Indonesia commits to reduce greenhouse gas emissions by Furthermore, the Indonesian government has realized 26% by its own efforts, and reach 41% if it receives inter- the importance of reducing the GHG emissions. Recently, national assistance in 2020 from the condition without an the Indonesian government encourages the concept of a action plan. To comply with this commitment, the Presi- low carbon society. There are three main strategies for re- dent of the Republic of Indonesia issued Presidential ducing the GHG emissions, which are listed and described in Table 8. Generally, a low carbon society can be achieved Table 6 Total emissions of scenario 3- year 2050 (kg/day) through the integration of public transport. As shown in Vehicles type CO CH CO HC Nox PM SO 2 4 2 the simulation results, the growth of GHG emissions can City bus 6,140 1.1 43 10 143 7 17 be reduced by reducing private vehicle ownership. One of the options is the integration of transportation systems Microbus 50,750 9 394 86 1,182 55 140 such as train and bus modes. Furthermore, a light-rail Taxi 1,028 0.8 10 1.1 0.9 0.1 0.2 transit system, Bus Rapid Transit, and non-motorized Car 4,223,145 3,211 37,774 4,722 3,777 567 1,001 transport should be integrated with land-use and urban Motorcycle 3,883,347 26,278 291,981 43,797 21,899 7,300 1,898 planning. Sukarno et al. Future Cities and Environment (2016) 2:6 Page 9 of 11 Table 7 National Action Plan for Reducing Emissions of Greenhouse Gases (RAN-GRK) No Action Plans Key point (s) Location 1 Reformation of Bus Rapid Transit - Implementation of mass transit 12 cities in Indonesia: (BRT) system - Road based using buses which uses a special line. Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 2 Renew of public transportation vehicles - Evaluation of public transportation vehicles 12 cities in Indonesia: - Emission inspection Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, - Vehicle life expectancy Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin - Change with new vehicles 3 Installation of “converter kits” for public - Installation of “converter kits” for public vehicles to nine cities in Indonesia: vehicles replace oil fuel use with natural gas. Medan, Palembang, Jabodetabek, Cilegon, Cirebon, Surabaya, Denpasar, - Reduce CO2 emission to 20% Balikpapan and Sengkang 4 Socialization and training of smart driving Teaching and training the environmentally friendly 12 cities in Indonesia: driving to save fuel and to reduce air pollution. Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 5 Non motorized transport development - Increasing pedestrian and bicycle paths 12 cities in Indonesia: - Integrated with public transport planning and air Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, quality planning Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 6 Intelligent transport system development � Improving the communication and information 13 cities in Indonesia: system in the public transport Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, ➢ Travel routes Surabaya, Denpasar, Makasar, Balikpapan, Banjarmasin and Jabodetabek ➢ Cut travel time (Jakarta, Bogor, Depok, Tanggerang and Bekasi) � Decrease GHG emissions 7 Implementation of Traffic Impact � Land use and transport planning 12 cities in Indonesia: Control (TIC) � Travel demand management Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, � Integrated public transport Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 8 Parking Management Application � Anti idling regulation 12 cities in Indonesia: � Transport demand management (TDM) Medan, Padang, Pekanbaru, Palembang, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Makasar, Balikpapan and Banjarmasin 9 Implementation of Congestion Charging � Decrease the private vehicles on the road two cities in Indonesia: and Road Pricing � Integrate with mass transportation Jakarta and Surabaya Sukarno et al. Future Cities and Environment (2016) 2:6 Page 10 of 11 Table 8 Strategies to achieve a low carbon society Strategy Measure Opportunity Stakeholders Reducing per kilometer emissions Improvement of emissions standards for new - Adopted Euro 3 emissions standard - National Government agencies: vehicle technology - Can be promoted by using cleaner fuel MEMR, MoE, MoI, MoT such as natural gas, CNG, and biofuel - Automotive industry - Combined with Bus Rapid Transit Improvement of land use and transport planning - Integration of transportation planning - Local government and air quality - Urban planners - A significant political will is necessary Reducing per unit transport vehicle Enforcement of routine inspection of vehicle - Requires a regulation mechanism - Local government and policy emissions emissions - Enforcement to prevent corruption makers - Traffic agencies - Vehicle owners Enforcement of mass transport (buses or trains) - Integration of transportation planning - National Government; MEMR, and private sectors MoE, MoI, and MoT - Non-motorized transport development - Local government - Private sector Reducing travel distance - Travel demand management - Changing the behavior of society - Urban planner - Non-motorized transport - Significant regulation necessary - National Government; MoE, MoI, - Land use and transportation planning - Integration of public transportation and and MoT urban planning - Local government - Policy makers Sources : [14] MEMR Ministry of Energy and Mineral Resource, MoE Ministry of Environment, MoI Ministry of Industry, MoT Ministry of Transportation Sukarno et al. 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Reformation of Bus Rapid Transit (BRT) system, Architecture 2(4):149–165 Installation of “converter kits” for public vehicles and 14. ESDM (2012) Ministry of Mineral Resources. Transport Sector, Assessment of Improvement of emissions standards for new vehicle Greenhouse Emissions, 2012 (in Indonesia version) 15. Padang Statistical Yearbook (2013) Padang dalam angka 2013. BPS Kota technology are the policy option that can be adopted in Padang. 2013. (in Indonesia version) Padang. Nevertheless, a policy evaluation should be 16. Sanjaykumar S (2008) Urban sprawl a system dynamic approach, 44th performed to identify whether such policies provide a ISOCARP Congress 17. BPS (2013) Perkembangan Jumlah Kendaraan Bermotor Menurut Jenis significant impact to decreasing fuel consumption and tahun 1987–2013. Available at: http://www.bps.go.id/linkTabelStatis/view/id/ emission level. 1413. Accessed on May 2015. (in Indonesia version). 18. Bandivandekar A (2012) Top vehicle market 2012. Retrieved from http:// Abbreviations www.theicct.org/blogs/staff/top-vehicle-markets-2012. ADO, automotive diesel oil; BOE, barrel of oil equivalent; BRT, bus rapid 19. President decree no.61/2011. National action plant for reducing of transit; CH , nitrous oxide; CO, carbon oxide; CO , carbon diocide; FO, fuel oil; 4 2 Greenhouse gases (GHG). http://sipuu.setkab.go.id/PUUdoc/17288/ GHG, greenhouse gases; HC, hydrocarboon; IDO, Industrial Diesel Oil; MEMR, PERPRES%20612011.pdf. Accessed on May 2015. Ministry of Energy and Mineral Resource; MoE, Ministry of Environment; MoT, 20. MeR (2009) Ministry of environment Regulation, No.04/2009 concerning Ministry of Transportation; PM, particulate matter; RAN-GRK, Rencana aksi threshold limit of vehicles exhaust emissions. Available at: http://prokum. nasional-Gas rumah kaca (In Indonesia) or National action plan for reduction esdm.go.id/permen/2009/permen-esdm-04-2009.pdf. Accessed on May emissions of greenhouses gases; SO , sulfure dioxide; TIC, traffic impact 2015. (in Indonesia version). control; UNFCCC, United Nations Framework Convention on Climate Change 21. CDIEMR (2014) Handbook of Energy and Economic Statistic of Indonesia 2014. Center for data and information on energy and mineral resources. Acknowledgements Ministry of Energy and Mineral Resources. Jakarta. The author would like to thank the Padang City Government, Padang Statistical Bureau, the Ministry of Transportation, the Ministry of Environment and the Ministry of Energy and Mineral Resource of Republic of Indonesia for the data support. Authors’ contributions IS, HM and LS contribute to data collection and verification. IS and HM participated in design of the case report and drafting the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests.

Journal

Future Cities and Environment – Springer Journals

Published: Jun 24, 2016

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