Greenhouse gases reduction effect through infrastructure export: Verification on modal shift

Nội dung text: Greenhouse gases reduction effect through infrastructure export: Verification on modal shift

1. GREENHOUSE GASES REDUCTION EFFECT THROUGH INFRASTRUCTURE EXPORT: VERIFICATION ON MODAL SHIFT Assoc. Prof. Dr. Tomoyo Toyota The University of Shimane, Japan 1. Introduction Infrastructure export is cited as one of Japanese growth strategy. Urbanization and economic growth are developed rapidly centering on developing countries, and the importance of the business strategy which made an overseas market the subject is rising. Japan is the target which promotes infrastructure export to 30 trillion yen (about 300 billion US$) by 2020. Infrastructure export is also expected of the effect which contributes to global environment problem solving and disaster prevention as well as improvement of lifestyle in a partner country. One of contribution to the environment field by the infrastructure export is reduction in greenhouse gases (GHGs). A generating electricity section is biggest greenhouse gas emission, and next is transport sector. That the electric power and transportation demand provide the developing country which rises with expensive technological infrastructure can contribute to mitigation of global warming. To mitigate climate change, it is necessary for all countries to be involved. Currently, China is the world’s largest emitter of CO2, and without the cooperation of China and other developing countries, it will be impossible to solve the climate change problem, as the amount of GHGs emission from these countries, where population and economic growth are rising rapidly, is expected to double between 2010 and 2030. This study estimates reduction effects of GHGs emission through 14 rail projects (subway, monorail and diesel train project) in 5 countries (India, Indonesia, Thailand, China and Philippine). All of the projects were funded by Yen loans(ODA) because I could obtain detailed information regarding both establishment and operation of the projects. 144
2. 2. Energy and GHGs emission in transportation sector The transportation sector is the second-largest GHGs emission sector after electricity, and it accounts for 24% of the whole sector (see Figure 1). The amount of GHGs emission in the transportation sector is increasing each year, and the GHGs emission from transportation more than doubled between 1971 and 2008 (IEA, 2017). World CO2 Emissions from Fuel Combustion (total CO2 emissino is 32,294.2 Mt in 2015) Transport: of others 6% Transport: of road 18% Electricity and heat generation 42% Other sectors 10% Manufacturing Other energy industries and industry own use construction 19% 5% 㻌 Figure 1. Global CO2 emission by sector in 2015 㻔㻿㼛㼡㼞㼏㼑㻦㻌㻵㻱㻭㻔㻞㻜㻝㻣㻕㻕㻌 㻌 The GHGs emissions from the transportation sector increases with the rise in the income level. Figure 2 shows the population size and GHGs emissions from the transportation sector in Organization for Economic Co-operation and Development (OECD) and non-OECD countries. The population size of non-OECD countries is five times that of OECD countries, but the CO2 emission from the transportation sector is only 60% of that of OECD countries. Per capita annual CO2 emission from the transportation sector is 2.8 t-CO2 in OECD and 0.4 t-CO2 in non-OECD countries. If the amount of per capita emission of the transportation sector in non-OECD countries reaches the same level as that of OECD countries, then the amount of GHGs emission in the world will increase by about 40%. 145
3. 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 OECD countries non-OECD countries Population (million) CO2 emission from transportation sector (1,000 ton of CO2) Figure 2. Amount of CO2 emission from transportation sector (in 2015) Source: 㻔㻿㼛㼡㼞㼏㼑㻦㻌㻵㻱㻭㻔㻞㻜㻝㻣㻕㻕 What, then, is the GHGs emission structure in the transportation sector? Figure 1 also shows that travel on roads produces the largest emission in transportation sector.㻌Because the needs of transportation increase with an increase in income, the mitigation of emission from the transportation and road traffic sector is one of the most important problems that needs to be addressed. Emission from transportation can be calculated as Emission = Carbon content (CO2/MJ) × Energy intensity (MJ/pkm) × Transportation demand (pkm). When reducing the amount of emission from road traffic, it is necessary to cut down the amount of emission of each factor. Thus, reducing carbon content means switching to fuels with low carbon content, e.g., converting from fossil fuel to biofuel (Haroon, 2000). To lower energy intensity, it is effective to improve engine efficiency, make a modal shift from light-duty vehicles to a mass transportation system, and increase the load factor. Further, as a way of reducing transportation demand, short roads and improvements can lower the distance that vehicles are driven. (Source: IEA, 2009; OECD/ITF, 2000; David, 2007; Cefic and ECTA) The International Energy Agency (IEA) anticipates that the four most important modes that will contribute to CO2 in the baseline scenario in 2050 are LDVs at 43%, trucks at 21%, aviation at 30%, and shipping at 8%. Additionally, they predict that buses and rail lines will increase significantly, and that CO2 reduction via efficiency and alternative fuels in these modes will become increasingly 146
4. important. This study examines how a modal shift from road to mass transportation through railroad has an impact on GHGs emissions. The main objectives of railway projects conducted through the Japanese ODA are to enhance regional economic development or to reduce air pollution through constructing subways or railways, and not to reduce the amount of GHGs emissions. Therefore, the GHGs reduction effect because of the modal shift is estimated as a co-benefit effect. 3. Research subject and methodology 3.1 Object project This study aims to estimate the GHGs emission reduction effect through transportation by railway project. The GHGs emission reduction effect is calculated as the emission gap between the baseline and railways built through the projects. This study estimates the GHGs emission reduction effects of 14 railway projects in these five countries: India, Indonesia, Thailand, China, and the Philippines as indicated in Table 1. There are two main objectives of these projects: one is to promote regional economic development and improve the urban environment through the alleviation of traffic congestion and the reduction of pollution caused by motor vehicles, and the other is to contribute to the promotion of trade, both passenger and freight, and to the vitalization of regional economies. Table 1. Project Summary No. Country Project type Scale(km) Scale(Mpkm) Scale(Mtkm) 1 China Electric, P 4 9,216 2 China Electric, F 154 151 3 China Electric, P 8 194 739 4 China Electric, P and F 42 50 450 5 China Electric, P and F 57 252 1,559 6 China Electric, P and F 139 6,267 7 China Electric, F 88 246 8 China Diesel, F 40 12,581 9 China Diesel, F 191 247 10 India Electric, P 35 2,494 11 Indonesia Diesel, P and F 59 244 23 12 Indonesia Diesel, P and F 51 367 -24 13 Philippine Electric, P 8 247 14 Thailand Electric, P 10 315 Train total 886 4,561 31,057 note)P=Passsenger, F=Freight, Mpkm=Million passenger-km, Mtkm=Million ton 147
5. Source: JICA Website 3.2 Methodology of GHGs reduction effect The GHGs emission reduction effect is calculated as the emission gap between the baseline and the transportation projects. The amount of GHGs reduction effect refers to the appraisal technique of the CDM, which is approved by the United Nations. The GHGs reduction effect in the transportation sector refers to the ACM0016: “Mass Rapid Transit Projects” and AM0090: “Modal shift in transportation of cargo from road transportation to water or rail transportation,” or JICA Climate-FIT (Mitigation) (JICA Climate Finance Impact Tool/Mitigation) Draft Ver. 1.0, June 2011). However, because detailed data and monitoring information for every project are necessary to apply the evaluation methodology of the CDM, it is difficult to estimate the reduction effect of all projects using the methodology of the CDM. For simplification, while referring to the CDM methodology, the reduction effect is estimated with the following procedures. The project intends to reduce GHGs emissions by realizing a “modal shift” from existing passenger transport systems, i.e., conventional buses, passenger cars, taxis, and bikes, to passenger railway systems, such as a new railway, a double-track railway, or a quadruple-track railway. In addition, the “electrification” of passenger railway systems will reduce GHGs emission. The GHGs emission reduction amount is calculated as the difference between the project emission (PE) and the baseline emission (BE) amounts: ERi,y = BEi,y – PEi,y, (1) where ERi,y is GHGs emission reduction due to i project activity in year y (t-CO2/y), BEi,y is baseline emissions: GHGs emissions with existing transport systems in year y (t-CO2/y), PEi,y is project emissions: GHGs emissions after the success of modal shift to the passenger/freight railway from the existing transport systems in year y (t-CO2/y). Project emission by the CDM methodology is the sum of the 1) emission based on the fuel and/or electricity consumed by the project railway (direct project 148
6. emission) plus 2) emission caused by project passengers from their trip origin to the entry station of the project, and from the exit station of the project to their destination (indirect project emission). However, as we cannot collect the indirect project emission by the available data, project emission means only direct project emission in this study. In contrast, the baseline emission (for passenger transportation) by the CDM methodology is defined as ௉೔ǡ೤ ܤܧ௜Ǥ௬ ൌ σ௣൫ܤܧ௣ǡ௜ǡ௬ ൈܨܧܺ௣ǡ௜ǡ௬൯, (2) ௉ೄುಶೃ where BEy is baseline emission in year y (t-CO2), BEp,y is baseline emission per surveyed passenger p surveyed in year y (each surveyed passenger has a different expansion factor), Py is the total number of passengers in year y, PSPER is number of passengers in the time period of the survey (1 week), p is the surveyed passenger (each individual), y is year of the crediting period, and i is each project i. Surveys are required to estimate the baseline and project emissions, but those surveys were not conducted. Hence, for example, we created the basic unit to collect macro-level data by the following estimation methods: 1) Estimation process and data of the reduction effect (Case of passenger travel) From formula (1), the GHGs reduction effect by a passenger transport railway project is calculated by detecting the amount of project emissions from the amount of the baseline emissions. Also, the GHGs reduction effect by a passenger transport railway project㻌deducts and calculates the amount of project emissions from the amount of the baseline emissions. Figures 3 and 4 show the estimation flow of the baseline and project emissions. 149
7. (5) Power consumption (7) CO2 Electric train (1) Amount of for passenger emission passenger transportation (GWh) (t-CO2) transportation (pkm) (4) Unit (6) Emission conversion: factor: caloric unit to power (3) calorific value for (8) Emission electric unit generation passenger transportation factor: fuel (1 GWh = 3.6 MJ) (t-CO /GWh) (MJ) from tank to 2 wheel (t-CO2/MJ) (2) Average rail (9) CO2 emission energy intensity for (t-CO2) passenger (MJ/pkm) Non-electric train Data source (1) JICA website (2) IEA/SPM (2004) (6) IEA(2011a, 2011b, 2011c) (8) IPCC (1996) Figure 3. Estimation flow of the project emission (Passenger transportation) For mid- and long- distance transportation (1) Amount of (12) Calorific value for (13) CO2 emission passenger passenger (t-CO2) transportation (pkm) transportation (MJ) (11) Average bus (8) Emission factor: fuel energy intensity from tank to wheel (MJ/pkm) (t-CO2/MJ) For urban transportation (1) Amount of (16) CO2 emission passenger (t-CO2) transportation (pkm) (14) Baseline of (15) Energy (8) Emission factor: urban intensity by fuel from tank to transportation vehicle type wheel structure (%) (MJ/pkm) (t-CO2/MJ) Data source (11) IEA/SPM (2004) (14) JICA website (15) IEA/SPM(2004), IPCC (1996) Figure 4. Estimation flow of the baseline emission (Passenger transportation) The estimation process and the data change with an electric train or a non-electric train (diesel) are indicated in Figure 4. Moreover, the estimation process changes with urban or long-distance railways. The data used for estimation is displayed below. 150
8. Table 2 shows the energy intensity (passenger and freight) of railways in 2000, and the average annual rate of energy improvement. It was assumed that the annual energy intensity changed with improvement rates. Table 2. Rail Energy Intensity Average rail energy Average annual rate intensity, 2000 of improvement Passenger Freight Passenger Freight (MJ/ pkm) (MJ /ton-km) (%) (%) FSU 0.3 0.2 1.0% 2.0% China 0.3 0.2 1.0% 3.0% Other Asia 0.3 0.2 1.0% 3.0% India 0.3 0.2 1.0% 3.0% Africa 0.3 0.2 1.0% 3.0% OECD Pacific 0.3 0.4 1.0% 2.0% Source: IEA/SPM (2004) For example, (3) Calorific value for passenger transportation (MJ) can calculate (1) amount of passenger transportation multiplied by (2) rail energy intensity. For rail transportation, the energy consumption varies greatly according to the type of train. There are two basic types, electric and non-electric (diesel) trains. The estimation method is divided according to railroad type. In the case of the electric train, (3) calorific value changes to (5) power consumption for passenger transportation (GWh) through (4) unit conversion. Estimation of (7) CO2 emission (t-CO2) according to multiply (6) the emission factor: power generation (t-CO2/MJ). Because the (6) emission factor of the power generation sector is different greatly by the energy use structures and the technical levels of a country, so we used the emission factor of each country (Table 3). 151
9. Table 3. Emission Factor of Power Generation (t-CO2/GWh) People's Republic of India Indonesia Philippines Thailand China 2005      2006      2007      2008      2009      2010      2011      2012      2013      2014      2015      Source: Estimation from IEA (2017) The emission factor of power generation is large in a country where fossil fuel use is high, and the energy efficiency of power generation is low. In contrast, the (9) CO2 emission from non-electric railway projects (t-CO2) can be calculated by multiplying the (3) calorific value for passenger transportation (MJ) and (8) the emission factor of fuel (t-CO2/MJ). The value of (8) the emission factor of fuel uses the Intergovernmental Panel on Climate Change (IPCC; Table 4). Hence, we assume that a non-electric train uses diesel as fuel. Table 4. CO2 Emission Factor from Fuel Net calorific values Emission factor (MJ / litter) (g-CO2 / MJ) (g-CO2 / litter) Gasoline 44.8 69.3 3,105 Diesel oil 43.3 74.1 3,209 Source: IPCC (1996) The amount of baseline emissions when a project is not undertaken was estimated along with the flow, as shown in Figure 5. Here, the estimation flow differs according to long- and mid-distance transportation, and urban transportation. Long- and mid-distance transportation assumes that a bus is used 152
10. as baseline transportation. 4. Results The amount of the annual reduction effect was about 6.0 Mt-CO2, and this is equivalent to 0.5% of the CO2 emission from all of Japan in 2015. Although the reduction effect of China is large, the results by passenger and freight differ greatly (Table 5). Table 5 Result of GHGs emission reduction effect by train project (unit: 1,000 t-CO2/year) No. Country For passenger For freight 1 China -5.0 2 China 1,852 3 China -4.3 4 China -6.8 148 5 China -2.0 86 6 China -8.8 313 7 China 1,259 8 China 56 9 China 2,527 10 India 36.2 11 Indonesia 3.1 4 12 Indonesia 0.3 13 Philippine -1.2 14 Thailand 12.5 The reduction effect by freight transportation is greatest, and passenger transport has many projects in which the reduction effect is negative. The minus sign means that the amount of GHGs emissions increased as a result of the modal shift by a project. The GHGs discharged by the vehicles (a bus, car, taxi, two-wheeled vehicle, etc.) of the traffic structure that exists before a project is conducted is smaller than the GHGs discharged from a railroad that is constructed by a project. That is, the GHGs emission from a railway that is built by a project is larger than that of the vehicles (bus, car, taxi, or two-wheeled vehicle) of the traffic structure that exists prior to a project being conducted. 153
11. 5. Considerations This study demonstrated that railway projects funded by yen loans substantially contributed to CO2 reductions. The GHGs reduction effect in Japanese ODA projects that were implemented in 5 countries was about 6 million tons of CO2, which is equivalent to 0.5% of Japan’s total emission in 2015. The reduction effect is large in long- and mid-distance freight railways. In contrast, the reduction effect of passenger transportation by electric railway is negative. Because many GHGs were discharged through the electric generation process, which is the driving force of the electric train, the effect of GHGs reduction was reflected as negative.㻌 While the emission factor of the power generation sector of China, the Philippines was negative, which is the same as that of Japan, other projects displayed an improvement (a reduction contribution is estimated). The modal shift from road traffic, which uses gasoline and diesel as fuel, to a train is also anticipated in the field of energy conservation. However, if the amount of emission of a power generation sector is not considered, the overall amount of emission will increase.㻌To evaluate the GHGs reduction effect of a railway project, comprehensive evaluation is required not only of the transportation sector but also of the energy efficiency and emission structure of the power generation sector. Moreover, assumptions about the baseline also influence the results greatly.㻌Although the reduction effect of a railway for freight transport project is large, one of the reasons for this is the assumption that when a project is not conducted, then trucks are used. In this study, because monitoring data could not be used, estimations were made using macro data. Because a database was not built, the detailed energy data of a developing country are especially difficult to obtain. Although, in this study, the original unit was created and estimated based on the data of the area and the country using it, the estimation result changes greatly according to the original units. Therefore, verification of the basic unit is a future subject for research. Furthermore, because data related to indirect emission or leakage was not used in this study, I would like to focus on these issues as future subjects for research. 154
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