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ScienceDirect Energy Procedia 79 (2015) 969 – 975

2015 International Conference on Alternative Energy in Developing Countries and Emerging Economies

Scenario Based Assessment of CO2 Mitigation Pathways: A Case Study in Thai Transport Sector Pradeep R. Jayatilakaa, Bundit Limmeechokchaia* a

School of Manufacturing Systems and Mechnical Engineering, Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22, Rangsit, 12121, Thailand

Abstract “When to implement actions” and “how much to abate emissions” are two major concerns of policy makers when it comes to policy formulation and planning for CO2 mitigation. This study investigates CO2 mitigation potential in the Thailand transport sector under four possible mitigation pathways. Those pathways comprise four mitigation actions and three different time frames as implementation periods (2015, 2025 and 2035 as commencement years). To represent that, along with business as usual (BAU) case (as reference), four countermeasure scenarios were modeled namely L2035, L2025, L2015 and fuel switching (FS). Results show that the L2015 scenario can achieve the highest CO2 mitigation with 23% cumulative reduction against the BAU from 2010-2050. However, due to countermeasure implementation delays (by 10 years in L2025 and by 20 years in L2035), the mitigation potentials have dropped significantly. In addition, early in implementations of actions have resulted considerable reduction in energy consumption and it has allowed more penetrations of bio-fuel blends and electricity as replacements to fossil oils. Moreover, in terms of abatement costs, technologies such as efficient vehicles and modal shifts provide cost savings along with high mitigation potentials. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE. Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE

Keywords: CO2 mitigation, transport sector, Thailand, AIM/Enduse, co-benefits;

* Corresponding author. Tel.: +66-2-986-9009; fax: +66-2-986-9112. E-mail address: [email protected]

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of 2015 AEDCEE doi:10.1016/j.egypro.2015.11.595

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Pradeep R. Jayatilaka and Bundit Limmeechokchai / Energy Procedia 79 (2015) 969 – 975

1. Introduction Global warming due to excessive greenhouse gas emissions is one of the greatest threats faced by the world in this 21st century. Keeping global mean temperature increase below 2◦C when compared to preindustrial level (accumulation of 1,150 Gt-CO2 in atmosphere) [1] is the internationally agreed upon target which has a higher probability of avoiding disastrous consequences, resulting only a manageable risk as forecasts highlighted. However, achieving this goal is an immense challenge unless all the nations, including developing countries, committed to it. In regards to this global issue, Thailand, as the second largest energy consumer and CO2 emitter among ASEAN countries, has an important role to play. In Thailand, the transport sector was responsible for about 28% of CO2 emissions and has consumed about 35% of total energy used in the country in 2010 [2]. This has further emphasized with high dependency on fossil fuels in the sector and inefficient transport modes [3]. In recent past, Thai government has developed variety of national development plans such as “20-Year Energy Efficiency Development Plan” and “Alternative Energy Development Plan (AEDP) to improve the sustainability of the energy system and the economy. Focus of them is aligned with the CO2 mitigation objective since they too aimed to promote efficient technologies, renewable power generation, efficient modes and alternative fuel use in transportation etc. Nevertheless, in the transport sector, yet, significant progress can only be seen in fuel switching from fossil oil to bio-fuels. Moreover, successful implementation of proposed countermeasures depends on variety of factors such as proper investment, public acceptance and availability of technologies. Hence, actual implementation time frames and abatement quantities of those countermeasures are ambiguous. Therefore, in order to retrofit the current plans and for the development of new strategies, it is required to analyze possible pathways of CO 2 mitigation. In literature, few studies can be found that has analyzed CO2 emission and its mitigation in Thai transport sector [3-5]. Out of which, [4] is the most recent one which has carried out detailed analysis on Low Carbon Society (LCS) in Thai transport sector. It has investigated three LCS scenarios with three different CO2 mitigation intensities and analyzed the impact on transport energy system in the period 2010-2050. Furthermore, studies presented in [3, 5] have also carried out along the same lines of aforementioned study, but with less descriptiveness and scope. In addition, studies that investigated CO2 mitigation in more than one sector or whole energy system of Thailand, can also be found in the literature [6, 7], most of which analyzed single mitigation pathway or multiple pathways with varying mitigation intensities. However, none has analyzed how the delay in implementation of mitigation actions will impact on actual mitigation potential and the energy system. Hence, the objective of this study was set as to analyze four probable alternative pathways of CO2 mitigation in Thai transport sector on their mitigation potentials and impacts on energy system. 2. Methodology In this study, the AIM/Enduse model was used to analyze Thai transport sector. It is a bottom-up optimization, recursive dynamic model with detailed technology selection framework [8]. Transport sector was modeled as 16 vehicle categories which comprise 11 passenger and 5 freight transport types belonging to land, water and air modes. Planning period was selected as 2010-2050 with base year as 2010. When developing the transport model, future transport service demand of each vehicle category was calculated using multi variable regression analysis by taking population and transport GDP as independent variables. Historical data of population and GDP were obtained from [9] and [10], while future trends of them were taken from [11] and [12], respectively. Energy prices were collected from annual report (2011) on “Thailand Energy Situation” from Ministry of Energy, Thailand, while CO 2 emission factors of fuels were obtained from “IPCC Guidelines for National Greenhouse Gas Inventories


(2006)” report. As CO2 mitigation actions, fuel switching, penetration of advanced technologies, modal shift and travel demand management (TDM) have been considered. Out of which, in Thailand, since only fuel switching action has been initiated successfully to date, rest of the actions has been considered as new actions. Hence, in this study, when developing countermeasure scenarios, three different time frames have been defined as implementation periods, beginning of which aforementioned new actions were assumed to be initiated. Thus, together with business-as-usual (BAU), four CM scenarios have been developed. The BAU was modeled with the assumption that any new action or policy will not be introduced during the considered time span. The CM scenarios are defined as follows. 1) L2015: Implementation of all the actions from 2015 onwards. 2) L2025: This scenario assumes 10 year delay in implementation of new actions 3) L2035: This assumes 20 year delay in implementation of new actions 4) FS (Fuel Switching only): This examines the mitigation potential of fuel switching action alone if new actions will not be successful. In addition, energy savings and fuel mix changes under developed scenarios, abatement costs and individual mitigation potentials of considered actions have also been examined. Furthermore, co-benefits of CO2 mitigation in terms of energy security and local air pollutants have also been analyzed. 3. Results 3.1. CO2 emission and mitigation In the BAU scenario, In Fig.1 CO2 emission has increased to 119.8 Mt-CO2 in 2050 with 2.1 times increase from the base year. However, as it can be seen, CM scenarios have shown significant mitigation potentials. L2015 has the highest abatement potential with 23.1% cumulative reduction of CO 2 emissions (840 Mt-CO2) against the BAU from 2010-2050. It was followed by L2025 and L2035 with 17.3% (628 Mt-CO2) and 11.3% (430 Mt-CO2) cumulative mitigations, respectively, against BAU during the same time span. However, mitigation potential has significantly dropped in those two CM scenarios due to the delay in initiation of actions. In fact, it has dropped by about 25% in the L2025 and by about 49% in the L2035 when compared to the L2015. Moreover, in FS scenario (only fuel switching action), CO 2 emission has been reduced only by 8.8% cumulatively, against BAU.

Emission (Mton-CO2 )

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Fig. 1. CO2 emission in all scenarios

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3.2. Energy consumption and fuel mix Fig. 2(a) presents the energy consumption trends in modeled scenarios. In the BAU case, it has risen to 41.1 Mtoe in 2050, increased by about 2.12 times from the base year. However, as it clearly shows, in two CM scenarios, total energy consumption has reduced significantly. The L2015 has achieved the highest energy saving where it has reduced energy consumption by 48% in 2050 against BAU (cumulative saving is 16.6% against the BAU from 2010-2050). The L2025 shows considerable drop in energy consumption with 9.5% cumulative reduction. However, the L2035 and FS have not achieved significant reductions in energy consumption, despite their downward trends towards end year 2050. The major reason behind that is the delay in introduction of advanced technologies where it has delayed by 20 years in L2035 and in FS, it has not implemented. In addition, Fig. 2(b) shows the impact of CO2 mitigation measures on the fuel mix in the BAU and CM scenarios. In the figure, “ethanol blends” category includes E10, E20, E85 and biodiesel blends group includes B5, B10 and B20. LPG stands for liquefied petroleum gas and CNG stands for compressed natural gas. In FS, when compared with the 2050BAU, even though reduction in energy consumption is low, fuel mix has considerably changed due to high penetration of bio-diesel and ethanol. Furthermore, L2035 has the most diversified fuel mix with reduction in fossil fuel and increase in CNG, LPG, bio-fuels and electricity. Rest of CM scenarios followed a similar pattern with less diversification of fuel mix. However, in the L2015 by 2050, gasoline, LPG and CNG shares have dropped to insignificant levels while electricity consumption has increased considerably due to penetration of EVs and plug-in hybrids. 45

35 Fuel Mix (%)

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Gasoline Diesel LPG Jet Fuel

Ethanol Blends Bio-diesel Blends CNG Electricity

Fig. 2. (a) Energy consumption in all scenarios; (b) Fuel mix in all scenarios in the base year and end year 2050 3.3. Abatement Costs and mitigation potentials of countermeasures

Since extent of carbon emission abatement depends on economical viability and technical strength of nations, it is required to investigate economically beneficial/feasible countermeasures and technologies when formulating policies and developing plans for CO2 mitigation. Hence, in relevance to defined CO2 mitigation pathways, abatement costs of technologies along with their mitigation potentials were presented in Table 1. It presents the aggregated mitigation potentials of each technology/countermeasure in 2050 and average cost per ton of CO2 mitigation (average abatement cost – AAC). Negative costs indicate the cost savings that can be achieved. According to the results, efficient vehicle (conventional technologies with high fuel economies) has the highest mitigation potential in the L2035 and the L2025 and has the second highest in the L2015. Moreover, in the L2035, about 49% of the total mitigation potential remains in efficient vehicles due to delay in implementation has not allowed higher penetration

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of other measures. However, in the L2025, it shows less dominant, despite remains as the highest contributor. On the other hand, electric vehicles have the highest abatement potential in the L2015 and rest of the measures also shows higher potentials comparatively. In terms of costs, modal shifts, TDM and efficient vehicles provide cost savings while other technologies require extra investments for implementation. AACs of efficient vehicles and hybrids have increased when implementations were begun earlier since more low-cost mitigation options become viable with early implementations. However, AACs of plug-in hybrids and EVs have changed slightly, but costs are considerably high. Hence early implementations of countermeasures increase mitigation potential with little increase in investment costs. Table 1. Average abatement costs and mitigation potentials of technologies and countermeasures in 2050 L2035 Scenario L2025 Scenario L2015 Scenario Countermeasure Avg. Cost Mitigation Avg. Cost Mitigation Avg. Cost Mitigation Potential Potential Potential (USD/ (USD/ (USD/ (kt-CO2) (kt-CO2) (kt-CO2) t-CO2) t-CO2) t-CO2) Efficient vehicles - 327 20,819 - 231 17,554 - 47 11,269 Hybrid vehicles 41 2,767 45 6,615 76 9,941 Plug-in Hybrid vehicles 529 2,963 522 6,037 508 8,087 Electric vehicles 551 5,820 549 11,612 559 19,750 TMD - 439 2,139 - 438 2,830 - 438 3,528 Modal shift: bus - 329 1,580 - 438 2,380 - 438 3,164 Modal shift: non- 999 702 - 999 1,049 - 999 1,310 motorized Modal shift: train - 2,065 5,390 - 2,065 7,143 - 2,065 8,881

3.3. Co-benefits Co-benefits that can be acquired from CO 2 mitigation has been investigated in terms of local air pollutant emissions and energy security. CO2 mitigation actions have considerably reduced local air pollutant emissions too. Similar to CO2 mitigation, among CM scenarios, the L2015 has the highest mitigation of all the pollutants. It has cumulatively reduced NOx, SO2, CO and PM by 34%, 33%, 47.1% and 31.4%, respectively. Since local air pollutants can cause for health problems, mitigation of it in transport sector is a significant benefit. In literature, various indicators have been used to examine it. However, in this study, two indicators were selected to analyze the impact on the energy security from CO2 mitigation. Those are diversity of energy mix (DoFM) and carbon intensity (CI). Fig. 3(a) and Fig. 3(b) present DoFM and carbon intensity in scenarios in the base year and 2050, respectively. According to results, DoFM has increased in all CM scenarios significantly where L2035 has the highest with 81.4% in 2050. This has resulted due to comparatively high consumption of CNG, LPG, biofuels and electricity in L2035 towards 2050. On the other hand, CI has dropped in BAU in 2050 when compare with base year. However, it has further dropped in CM scenarios. L2015 has the lowest value with 0.59 t-CO2/1000US$. Hence, it can be highlighted that CO 2 mitigation in the transport sector will positively impact to energy security. 3.00

90%

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.

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Fig. 3. (a) DoFM in scenarios in the base year and 2050; (b) Carbon intensity in scenarios in the base year and 2050

4. Conclusion This study investigated four likely pathways for CO2 mitigation in the transport sector of Thailand in the period 2010-2050 using AIM/Enduse model. Along with BAU, four CM scenarios were modeled namely FS, L2015, L2025 and L2035 which were based on three different time frames for countermeasure implementations. Results show that highest CO2 mitigation can be achieved in the L2015 with 23.1% cumulative mitigation against the BAU from 2010-2050. However, mitigation potential has dropped by 25% in the L2025 and 49% in the L2035 when countermeasure implementations were delayed by 10 and 20 years, respectively. In addition, significant reduction in energy consumption was also observed with early implementations of actions. However, all the CM scenarios have achieved more diversification in fuel mix where bio-fuel blends and electricity have penetrated more, while fossil oil shares have reduced over the time. In terms of abatement cost, countermeasures such as modal shift, TDM and efficient vehicles have given cost saving, while EVs and plug-in hybrids have high costs even in 2050, despite their high mitigation potentials in the L2015. In addition, co-benefit of CO2 mitigation was also analyzed in terms of air pollutant emissions and energy security. Results indicate reduction in air pollutant emissions positive impacts on energy security. Acknowledgements Authors would like to thank the National Institute for Environment Studies (NIES) of Japan for the financial assistance, Mizuho Information and Research Institute of Japan for the technical support received, and Sirindhorn International Institute of Technology, Thammasat University for the scholarship. References [1] National Research Council. Climate Change: Evidence, Impacts and Choices. National Academy of Sciences, Washington D.C.; 2012. [2] Department of alternative Energy Development and Efficiency. Annual Report: Thailand Energy Situation 2011. Ministry of Energy, Bangkok; 2012. [3] Sritong, N. Low carbon Societies in Thailand’s Transport Sector, in School of Manufacturing Systems and Mechanical Engineering, SIIT, Thammasat University, Thailand; 2013. [4] Selvakkumaran S, Limmeechokchai B. Low carbon society scenario analysis of transort sector of an emerging economy – The AIM/Enduse modelling approach, Energy Policy (2014), http://dx.doi.org/10.1016/j.enpol.2014.10.005i [5] Pongthanaisawan J, Sorapipatana C. Greenhouse gas emissions from Thiland’s transport sector: Trends and mitigation options. Applied Energy, 2013. 101: 288-98. [6] SIIT, AIT, NIES Japan, Kyoto University, Mizuho Information and Research Intitute. Roadmap to Low Carbon Thailand towards 2050. Bangkok, Thailand; 2013. [7] Winyuchakrit P, Limmeechokchai B, Matsuoka Y, Gomi K, Kainuma M, Fujino J, et al. Thiland’s low-carbon scenario 2030: Analysis of demand side CO2 mitigation options. Energy for Sustainable Development, 2011. 15(4): 460-66. [8] Kainuma M, Matsuoka Y, Morita T. The AIM/Enduse model and its appliction to forecast Japanese carbon dioxide emissions. European Journal of Operational Research, 2000. 122(2): 416-25. [9] World Bank. Population (Total). Population Databae. http://data.worldbank.org/indicator/SP.POP.TOTL. Retrived 3 March 2014.


[10] NESDB. National Income Report. National Economic and Social Development Board. Bankgkok; 2011. [11] UN. World Population Prospects: The 2012 Revision. United Nations. New York; 2013. [12] MoEN. Power Development Plan. Energy Policy and Planning Office, Ministry of Energy. Bangkok; 2011.

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