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LCA of biofuels in Thailand using Thai Ecological Scarcity method
Journal of Cleaner Production xxx (2016) 1e9
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LCA of biofuels in Thailand using Thai Ecological Scarcity method Naruetep Lecksiwilai a, b, Shabbir H. Gheewala a, b, *, Thapat Silalertruksa a, b, Jitti Mungkalasiri c a The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangkok, 10140, Thailand b Centre of Excellence on Energy Technology and Environment, PERDO, Bangkok, Thailand c National Metal and Materials Technology Centre (MTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, Thailand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 January 2016 Received in revised form 8 July 2016 Accepted 9 July 2016 Available online xxx
Thailand has been promoting biofuel production and consumption to substitute transport fuels in order to utilize domestic resources and contribute to greenhouse gas (GHG) mitigation. This study adopted a Life Cycle Impact Assessment (LCIA) method using “distance-to-target” approach based on the Swiss Eco Scarcity method. It is developed by using Thailand's current situation of pollutants emissions and natural resources use in line with the three related national policy goals i.e. GHG, primary energy and freshwater resources; the Eco-Factors are 1.6, 0.13 and 1.9 Thai Eco Point (TEP) per kgCO2e, MJ NRE-eq and m3 in respective units. These were used for an LCA comparison between cassava-based ethanol and palm oilbiodiesel with their fossil fuel counterparts on an energy basis. The LCA results indicate both biofuels have lower impacts than the equivalent fossil fuels and the main contributor is GHG emissions from biofuel production. The sensitivity analysis also indicated the given policy related to freshwater and GHG influence the biofuel benefits. Therefore, the strong policy and technical options in energy production are still required for sustainable biofuels following the government intention. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Life cycle impact assessment Biofuel Ecological Scarcity method Distance-to-target
1. Introduction In the modern world, broad concerns about climate change, limited fossil fuels supplies and sustainable use of natural resources have evolved a new direction towards alternative energy. Many studies have presented that biofuels are now a viable alternative to fossil fuels (Gheewala, 2012; Damen, 2010). Securing biomass feedstock supplies for the long-term future requires expansion of biofuel feedstock cultivation, the scaling up of production and advanced technology. One of the key challenges is the conversion of biomass feedstock to provide more convenient energy carriers like liquid agrofuels (biofuels) and electricity without losing the balance between economic growth and sustainable resources development. Thailand as one of the top agro-industrial countries has been rapidly developing its biofuel industry due to strong government commitment. Biofuels play a major role to meet an increasing energy demand while restricting the emission of climate change-
* Corresponding author. The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangkok, 10140, Thailand. E-mail address: [email protected] (S.H. Gheewala).
inducing greenhouse gases (GHGs). According to the Thai government's 10-year Alternative Energy Development Plan, AEDP 2012e2021 (DEDE, 2012a), biofuel production targets were set aiming to support the agricultural sector with an emphasis on the need for crop productivity improvement and strengthening competitive advantage. AEDP set the ambitious target to increase the production of ethanol from 1.2 to 9 ML/d by focusing on competitive crops, i.e. sugarcane and cassava, which have potential to increase their production yields. However, the biodiesel target leaping from 2.3 to 6 ML/d is increasing the demand for agricultural land for oil palm cultivation. Another AEDP's objective is GHG emission reduction (DEDE, 2012b), which can be achieved because of the apparent carbon neutrality of biomass-based fuels. Life Cycle Assessment (LCA) is a tool for environmental sustainability assessment. It can be used for examining feasibility of studies as well as exploring optimal measures before implementation. Setting the objectives of an LCA study significantly influences the relevance of result interpretation because a variety of choices are analyzed in order to opt for the best practices that would create least negative impact on the environment. In the past, even though there have been several LCA studies concerning the controversial issues on the sustainability of first-generation
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Please cite this article in press as: Lecksiwilai, N., et al., LCA of biofuels in Thailand using Thai Ecological Scarcity method, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.07.054
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Nomenclature AEDP BAU CED EEDP FU GHG GWP HHV HSD LCI LCIA
Renewable and Alternative Energy Development Plan business as usual Cumulative Energy Demand National Energy Efficiency Development Plan functional unit greenhouse gas global warming potential high heating value high speed diesel life cycle inventory life cycle impact assessment
biofuels (Ho et al., 2014), it has been shown that GHG benefits can be achieved if precautions are taken against indiscriminate land use change from forests. For example, Silalertruksa et al. (2012) investigated the benefit of life cycle cost and externalities of palm oil biodiesel showing results 3e76% lower than fossil-based diesel; the additional GHG emissions due to the conversion of forest to oil palm cultivated land for biodiesel will increase from 1.4 to 6.6 kg CO2/L (production and use) higher than diesel. Nguyen and Gheewala (2008) found that ethanol production from cassava in Thailand contributes to around 6% reduction in fossil energy and global warming impacts relative to gasoline. In addition, utilization of its residues or wastewater could reduce the impacts during the bioenergy conversion e.g. half of GHG emissions are reduced by utilizing the biogas from anaerobic wastewater treatment of cassava ethanol production (Numjuncharoen et al., 2015). However, there are disadvantages in terms of water use leading to competition with food crops which is being debated. Okadera et al. (2014) indicated the water footprint of energy supply (WFES) of Thailand will be increased to eight times greater than 25 years ago due to biomass energy. Most of the LCA studies in Thailand applied European or Japanese LCIA methods for assessing the environmental impacts. Some of the currently used LCIA methods e.g. ReCiPe (Goedkoop et al., 2012) and Japanese LIME (Itsubo and Inaba, 2003), are derived using the scientific data and discussions with stakeholders based in the developer country. Due to the technical difficulties and the demand for resources in developing LCIA methods like these, one possible solution is to develop LCIA based on currently available data and related policy goals under Thailand context. The “distance-to-target” technique is a reliable approach for figuring the normalizing and weighting factors linked to the country's interests and priorities. The Swiss Eco Scarcity method is such an approach that could be used. It can formulate “Eco-Factors” to calculate robust endpoint impact factors with a clear concept and directly related to the goal of the LCA study. The ecological scarcity method was first issued by the Swiss Federal Office for the Environment (FOEN) in 1998. This LCIA methodology is based on the distance to target principle. The calculation of the Eco-Factors is determined by setting the current flow in relation to (or distance from) the critical flow (can be so implied as policy targets). After being first published by Frischknecht, one of the key developers, it has been updated based on new scientific findings and political targets with 2013 as base year (Frischknecht and Büsser, 2013). The previous Swiss Ecological Scarcity 2006 (Frischknecht et al., 2009) was regarded as a policy decision tool for developing the Biofuels Life Cycle Assessment Ordinance (BLCAO) in 2009 (DETEC, 2009) and for several biofuel studies in Switzerland. Other countries used this for developing Eco-Factors with their own national
LHV MCM Mtoe NAMAs NRE OECD RE RON TEF TEP WTA
low heating value million cubic meter million tonnes of oil equivalent Nationally Appropriate Mitigation Actions non-renewable energy Organization for Economic Co-operation and Development renewable energy Research Octane Number Thai Eco-Factor Thai Eco Point withdrawal to availability (ratio)
information. For instance, the Japanese Eco Scarcity method developed by Büsser et al. (2010) adopted Japan Environmental Policy Priorities Index (JEPIX), which is an existing monitoring system to evaluate the current situation as compared to legislation. Although the Swiss Eco Scarcity method could be applied to LCA assessment in Thailand, the results would express Swiss preferences, which may not reflect the Thailand context. Therefore, the objectives of this study are adopting Eco Scarcity concept for Thailand context using proper information and also indicating what the limitations on its application are. The three relevant impact categories with regards to bioenergy are focused on, i.e. primary energy resources, freshwater resource and GHG emissions. Then, a comparative LCA is conducted on the two major biofuels in Thailand, i.e. ethanol and biodiesel, with their counterpart conventional fossil equivalents in utilizing as transportation fuel based on energy as functional unit.
2. Methodology The Ecological Scarcity method is partly based on characterization models used in LCA (e.g. global warming, ozone depletion, acidification, primary energy resources and land use). Other interventions are assessed individually (e.g. NH3, heavy metals) or as a group (e.g. NMVOCs). It is upon the availability of data and importance in the policy view that reflects the policy targets. The general expression adopted from the Swiss Ecological Scarcity method used for all elementary flows and the Eco-Factor is defined as follows:
Eco Factor
¼
K
TEP Fn
Fc Fk
2 c
(1)
With: K ¼ Characterization factor of a pollutant or of a resource substance.Fn ¼ Normalization flow: current annual flow, with nation as system boundary.F ¼ Current flow: current annual flow of substance.Fk ¼ Critical flow: critical maximum acceptable annual flow of substance.TEP ¼ Thai Eco Point as a unit of this assessment.c ¼ constant ¼ 1012/y; to obtain presentable numerical values. Principally, the weighting factor is developed from the current flow and critical flow. For this study, the current flows refer to the 2011 as the base year situation, corresponding to last year of the 5year term of the recent Tenth National Economic and Social Development Plan (2007e2011) published by the National Economic and Social Development Board (NESDB, 2006). The official historical data from reliable and publicly available sources and related studies are extracted to use for current flows. Nevertheless,
Please cite this article in press as: Lecksiwilai, N., et al., LCA of biofuels in Thailand using Thai Ecological Scarcity method, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.07.054
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for some data which cannot be directly used as current flow, different techniques have to be adopted. For instance, extrapolating to base year in case of no record in corresponding year, aggregating data from many sources as a flow, and using sensible assumptions to complete the calculation of flows. The critical annual flows correspond to policy objectives which reflect targets to be achieved within 5e20 years as midterm goals; they depend on the political motivation of each aspect. These are pinned on various years depending on the respective policy target to be considered in the circumstance of both Thailand context and global perspective. Thailand is not as developed as the OECD countries, so the international mandatory measures to reduce environmental impacts such as GHGs emission reduction are not fully adopted yet. However, in accordance of the precautionary principle and conservative perspective, this study has to set the target stricter than the current ones or at least equivalent to the current status with the assumption of no further increasing of impacts.
and liquid fossil-based carriers are 0.95, gaseous is 0.90 and for other biomass carriers cited from EforE (2006). Then, converting from final gross energy demand to PEC is accomplished by using primary energy factor and yield of energy production, calculated from the Thai National LCI database (MTEC, 2015) with Cumulative Energy Demand (CED) version 1.09 (Frischknecht et al., 2007). By CED, we can indicate RE and NRE feedstock requirement to produce each energy carrier. And, characterization factors from the above are applied to calculate the characterized primary energy resources flow. The normalization flow is 94,700 ktoe NRE-eq/year (an example of the calculation is shown in the Supporting information S2). The relevant parameters are shown in Table 1. The current final energy consumption in 2011 was 70,562 ktoe which is used as current flow. Projected to 2021 according to EEDP, 105,241 ktoe of final energy demand is used as critical flow. Hence, TEF can calculated as 1.3 TEP/MJ NRE-eq. According to the above characterization factor, TEF for RE is 0.4 TEP/MJ.
3. Results
3.1.2. Freshwater resources Thailand is an agriculture-based country with 71% of total freshwater demand consumed by the agriculture sector. AQUASTAT (FAO, 2013) reported the data of total renewable water resources (including surface runoff, transboundary steam flow and groundwater) in Thailand at 438,000 MCM in 2012. Moreover, FAO also revealed that the total water withdrawal was 57,310 MCM in 2012 (considering only renewable water resources for all sectors). It must be mentioned these two figures are the same as reported since 2007. The Royal Irrigation Department (RID) is the main authority responsible for handling freshwater allocation to the various user sectors in Thailand. The average annual rainfall of 1423 mm can be calculated to 735,000 MCM of total rain water and the stored water drainage annually totaled 59,340 MCM according to RID's online reporting. RID (2012) estimated the total freshwater consumption for all sectors consisting of rain and irrigation water was 998,000 MCM. Comparing the data from two sources i.e. AQUASTAT and RID, it was found that the former was more suitable to be used as “flow” since it considers actual controllable and usable freshwater resources instead of only precipitation which is used for the latter. According to a UNESCO study (Brown and Matlock, 2011) which referred to Raskin et al. (1997), 40% threshold ratio of water withdrawals to total renewable water resources is commonly used as water stress level. This is in line with Oki and Kanae (2006) and Hoekstra et al. (2012) who concurred that exceeding 40% ratio of irrigation water (blue water) withdrawal to annual run-off leads to a severe water stress situation. However, the Swiss Eco Scarcity 2013 and Japan Eco Scarcity (Büsser et al., 2010) preferred using the study of OECD (2003) which categorized the stress would start at 20% of water withdrawal to availability (WTA). As Thailand is located in the tropical zone and is an agriculture based country, 40% is the proper level to be calculated as freshwater withdrawal target. Thus, TEF for freshwater resources can be calculated following the Eq. (1) as 1.9 TEP/m3 (shown the calculation in the Supporting information S3). Anyway, population growth and consequence of climate change will increase the severity of the problem. This TEF may be underestimated for long-term sustainability assessment.
3.1. Eco-Factor development 3.1.1. Primary energy resources Thailand's energy consumption largely relies on fossil-based energy carriers. According to the energy statistics (EPPO, 2013), Thailand consumes around 73 Mtoe per year with most of the energy carriers being petroleum products used for the transportation sector at 48%. The second largest share is electricity at 20% which has grown by 3.3% per year on average during the last 5 years. Renewable energy contributed only 7% (excluding traditional renewable energy, e.g. fuel wood, charcoal, etc.). The Thai government assigned the Ministry of Energy to establish the following energy plans which could lead to energy security for next decade; 1) AEDP 2012e2021 (DEDE, 2012a) which proposes increased utilization of renewable energy resources to 25% in the next 10 years. Key measures included are supporting various types of renewable energy e.g. biomass, waste, solar, etc. to replace fossil resources and reducing dependency on energy imports also. 2) EEDP 2011e2030 (EPPO, 2014) focuses on the target of energy consumption reduction according to energy intensity (energy used per unit of GDP) reduction by 25% for all economic sectors in 2030 from 2005 BAU (Business As Usual) levels. According to the energy situation and future plans mentioned above, energy can be categorized into two types; non-renewable energy (NRE) derived from fossil-based fuels and renewable energy (RE). The basis for characterization is the relative change of energy feedstock types in the final utilization when projected to 2021 corresponding to AEDP and EEDP compared to base year, 2011. The final energy demand of both NRE and RE are still expected to increase 1.14 and 3.74 times, respectively. The relative change normalized by NRE demand can establish the characterization factor on a policy basis. The characterized RE is 0.3 [1.14/3.74] MJ NRE-eq/MJ and of course, NRE is 1 MJ NRE-eq/MJ. In other words, the consumption of primary energy resources derived from NRE will be worse than RE in view of environmental impacts according to policy intentions, i.e. 1 MJ from NRE is rated as equivalent to 0.3 MJ from RE (details shown in the Supporting information S1). The final consumption of energy carrier compositions of 2011 is gathered from the energy balance of the Department of Alternative Energy Development and Efficiency (DEDE 2012a,b). This is converted into primary energy consumption (PEC) by using gross energy content with its LHV/HHV ratio which assume that the solid
3.1.3. Greenhouse gases emission Thailand has been a member of the United Nations Framework Convention on Climate Change (UNFCCC) since June 1992, contributing to international efforts to address climate change issues. In February 1999, Thailand signed the Kyoto Protocol as a Non-Annex I country which did not have binding obligations to limit GHG emissions. Recently, IPCC published the fifth assessment report (AR5) which outlines the global warming potential (GWP)
Please cite this article in press as: Lecksiwilai, N., et al., LCA of biofuels in Thailand using Thai Ecological Scarcity method, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.07.054
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N. Lecksiwilai et al. / Journal of Cleaner Production xxx (2016) 1e9
Table 1 Calculation of characterized primary energy resources. Energy consumption (LHV)
HHV/LHV
ktoe/year
e
Energy consumption (HHV)
Primary energy factor Total
RE
NRE
ktoe/year
e
e
e
ktoe NRE-eq/year
105,241
94,741
1.32
6558
6560
0.00
1.29
7142
7142
1.44 1.44 1.47 1.47 1.47 1.51 1.16 1.16
0.00 0.00 0.07 0.07 0.15 0.87 0.00 0.00
1.44 1.44 1.40 1.40 1.33 0.67 1.16 1.16
3473 47 2447 2145 256 11 5054 13
3470 47 2365 2073 238 7 5053 13
1.14 1.16 1.14
0.00 0.05 0.00
1.14 1.10 1.14
19,067 726 2309
19,064 700 2308
70,562
Fossil energy carriers Petroleum Natural gas
4485
0.90
4983
1.32
0.00
Oil LPG Gasoline RON 91 RON 95 E10 RON 91 E10 RON 95 E20 RON 95 E85 Jet fuel Kerosene Diesel HSD HSD B5 Fuel oil
5001 5462 2292 31 1581 1386 165 7 4150 11 16,527 15,932 595 1916
0.90 0.95
5557
1.29
2413 33 1664 1459 174 7 4368 12
Coal Anthracite Bituminous Coke Briquettes and other coal Lignite Others Renewable Biomass Fuel wood Paddy husk Bagasse Agricultural waste Garbage Biogas Solar energy Traditional renewable Fuel wood Charcoal Paddy husk Agricultural waste Electricity
Total PEC, characterized
ktoe/year
Total
0.95 0.95 0.95
Total PEC,
0.95
16,771 626 2017
6601 165 257 109 4720 1350
0.95 0.95 0.95 0.95 0.95
174 271 115 4968 1421
1.00 1.00 1.00 1.00 1.00
0.00 0.00 0.00 0.00 0.00
1.00 1.00 1.00 1.00 1.00
174 271 115 4988 1427
4070 140 64 3065 801
0.83 0.92 0.80 0.80
169 70 3831 1001
1.00 1.00 1.00 1.00
1.00 1.00 1.00 1.00
0.00 0.00 0.00 0.00
169 70 3831 1001
174 271 115 4988 1427 e e 51 21 1165 304
2 482 2
0.90 0.90 1.00
2 536 2
0.37 0.37 1.85
0.03 0.03 1.61
0.34 0.34 0.24
1 198 4
1 186 1
8582 3463 3047 993 1079 13,271
0.83 1.00 0.92 0.80 1.00
4172 3047 1079 1349 13,271
1.00 1.00 1.00 1.00 2.57
1.00 1.00 1.00 1.00 0.00
0.00 0.00 0.00 0.00 2.57
4172 3047 1079 1349 34,096
e 1268 926 328 410 34,063
for various gases. The 100-year time horizon has been commonly selected and also applied to this study as the characterization factor. According to the Second National Communication on Climate Change (ONEP, 2010), the amount of GHG emission excluding landuse change and forestry sector totaled 237 M tCO2e in 2000. The emission data in 2011 can be projected from 2000 with the average economic annual growth at constant rate of 4.2% (consistent with energy consumption projection). The estimation of characterized three major GHGs plus other GHGs totals 394 Mt CO2-eq/y which is then used as both normalization flow and current flow in Eq. (1). The IPCC's measures encourage the developing countries to participate in GHG mitigation by using the framework called Nationally Appropriate Mitigation Actions (NAMAs). In Thailand, the National Climate Change Committee (NCCC) submitted Thailand's NAMAs that would be attempting to reduce its GHG emissions in the range of 7e20 percent below business as usual (BAU) in the energy and transportation sectors in 2020. The Alternative Energy Development Plan (AEDP) 2012e2021 (DEDE, 2012a) focuses on the energy sector that has a high potential in reducing GHGs. The Thailand Greenhouse Gas Management Organization (Public Organization) expects that by the year 2020, the energy sector would reduce GHG emissions by approximately 73 Mt CO2e annually.
Moreover, the projected emission of 2020 as BAU adjusted for the expected reduction can be calculated as 495 Mt CO2e. Moreover, 2 Mt CO2e of other GHGs i.e. SF6 and HCFCs (WRI, 2014) are assumed not to increase by appropriate and effectively launched programs. Therefore, a critical flow of this assessment is 497 Mt CO2e. Therefore, the TEF can be calculated according to Eq. (1) is 1.6 TEP/kg CO2e. 3.2. LCA study with biofuel cases The three abovementioned impact categories selected for developing the TEF were used for the LCA study comparing biofuels and fossil fuels comprising palm oil methyl ester, fossil-based diesel, cassava-based ethanol and conventional gasoline henceforth referred to in the text as biodiesel, diesel, ethanol and gasoline. The system boundary of all the considered fuels is shown in Fig. 1; it includes feedstock acquisition, treatment and refining, energy conversion, and direct use as biofuel for vehicles substituting its counterpart fuel. The functional unit is considered as 100 MJ energy basis for each of the energy carriers. This assessment excludes the other benefit of ethanol substituting fossil MTBE in gasoline in order to increase octane rating helping prevent
Please cite this article in press as: Lecksiwilai, N., et al., LCA of biofuels in Thailand using Thai Ecological Scarcity method, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.07.054
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Fig. 1. System boundary for LCA study with Eco Scarcity Method.
engine knocking. The LCI and background data for this study were gathered from various sources shown in Table 2. Primary energy resources for fossil-based analyses were from the Thailand National LCI database (MTEC, 2015) calculated by applying the techniques of Cumulative Energy Demand (CED) 1.08. Data for biodiesel and cassava-ethanol were extracted from Silalertruksa and Gheewala (2012, 2009). Freshwater consumption for fuel productions considers only total irrigation water use. The greenhouse gases emission due to land use change and the modification of engine for compatibility with biofuels are not taken to account for this study. The CO2 emitted during combustion of biofuels being generally of biogenic origin, is not included in the calculations. However, the methanol used in biodiesel conversion is fossil-based, so this fossil carbon content in the resulting biodiesel contributes to nonbiogenic GHG emission and is thus counted (Pleanjai et al., 2009). The non-CO2 GHG emissions are calculated from IPCC (2006) using the global warming potentials from AR5 GWP100 (IPCC, 2013). The result of TEF assessment of the four fuel types is shown in Fig. 2. The overall impact of biodiesel (21.1 TEP/FU) is about 33% lower than diesel (28.8 TEP/FU). Biodiesel feedstock acquisition (palm oil cultivation) needs to consume a lot of freshwater, while the fossil feedstock acquisition does not. Oil palm is grown in southern Thailand where the rainfall rate is high and climate conditions are suitable; however, even then some farmers use irrigation water in the dry season in order to maintain productivity. Furthermore, this assessment result includes water embodied in agrochemicals used. The produced water from fossil oil production is calculated, but due to its insignificant amount, it is not shown in Fig. 2. Biodiesel gets the benefit of biomass feedstock because CO2 from biofuel combustion is not accounted. Bioethanol (25.2 TEP/FU) Table 2 Life cycle inventory for 1 L of fuels and data sources for LCA assessment. Unit Primary energy resource RE resources NRE resources Freshwater resource GHGs emissions Production Use Energy value (NCV) Density
Biodiesel
Diesel
Bioethanol
Gasoline
MJ MJ MJ m3 kgCO2e kgCO2e kgCO2e MJ/L kg/L
33.85 15.82 0.70 1.50 1.34 0.15 33.5 0.86
/1 /1 /2
/1 /3
0.01 41.40 0.02 3.22 0.48 2.74 43.4 0.84
/4 /4 /5
/5 /6
24.33 8.91 0.57 1.34 1.32 0.01 21.2 0.79
/7 /7 /8
/9 /6
0.05 46.13 0.03 2.65 0.38 2.27 31.8 0.74
/4 /4 /5
/5 /6
Source: /1 Silalertruksa and Gheewala, 2012; /2 Pongpinyopap et al., 2014; /3 Pleanjai et al., 2009; /4 MTEC, 2015; /5 EcoInvent 3.0; 6/ IPCC, 2006; 7/ Silalertruksa and Gheewala, 2009; 8/ Gheewala et al., 2013; 9/ Kawasaki et al., 2015.
Fig. 2. LCA result of selected fuels assessed by Thai Eco Scarcity method.
also performs better than gasoline (32.3 TEP/FU). The energy content of biofuels are lower than their counterparts. As a result, TEFs of biofuels are not much lower than their fossil counterparts when compared on a MJ basis. 4. Discussion Referring to the Thai Eco Scarcity development and exercise with LCA study on biofuels compared with fossil-derived fuels, further observations are discussed in the sections below. 4.1. Comparing with the Swiss and Japan Eco Scarcity Methods As the Thai Eco Scarcity method is based on the same principles as the original Swiss and Japanese Eco Scarcity methods, a comparative analysis would yield interesting insights into the differences arising from the policy perspectives in particular the three environmental aspects considered in the assessment with biodiesel case (the details are shown in Section 3.2). The Swiss, Japan and Thai Eco-Factors of the considered impact categories are shown in Table 3. In result of the different policy targets used for Eco-Factor development, the value of the Eco-Factors could not be compared directly across nations. In the Swiss method, GHG emission obviously dominates other two impacts since it sets a very intensive reduction target of 80% under 1990 levels or one fifth of 2009 levels. This is because of their leading and undertaking role in global GHG mitigation. So, this makes the other impacts much less significant. Another point is that the Swiss economy, being based on service sectors including only
Please cite this article in press as: Lecksiwilai, N., et al., LCA of biofuels in Thailand using Thai Ecological Scarcity method, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.07.054
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Table 3 Comparing between Thai, Swiss and Japanese Eco Scarcity methods in the three selected impact categories. Impact category
Unit
Switzerland (UBP/unit)
Japan (EP/unit)
Thailand (TEP/unit)
Primary energy resources Nonrenewable energy Renewable energy Freshwater resource GHG Emission
/MJ NRE /MJ RE /m3 /kg CO2e
3.4 1.1 23 460
0.1 0.034 12 18
0.13 0.04 1.9 1.6
small contribution from agriculture (11%) and heavy industries (7%), has low GHG emissions (UNFCCC, 2014). That is the reason why the national flow used as normalization flow is fairly low. Hence, this results in a high Eco-factor for GHG compared to other countries. Although Japan is also a developed country, energy demand of the industrial sector as well as the urban population is still increasing. In 2007, the total GHGs emitted by Japan were 1374 Mt CO2e (excluding LULUFC, 30-times larger than Switzerland). Moreover, they expected to reduce 25% below the 1990 levels or 23% from present. The Japanese Eco-Factor is therefore weaker than the Swiss one. On the other hand, Thailand being a Non-Annex-1 country did not set the reduction target based on 1990 levels. According to the recent NAMAs pledged, Thailand has committed to decelerate the GHG emissions compared to BAU by 2020. Notwithstanding, at that target year Thailand is allowed to increase GHG emissions compared to 2011 (394 / 497 Mt CO2e) which results in a relatively low GHG Eco-Factor (as compared to the other countries). All countries expect to increase the RE demand partly for supporting GHG mitigation actions and internal energy security issues. Characterization factors are formulated on different bases, e.g. the Swiss Eco-Factor is derived from composition of energy carriers in final energy demand, whereas those of Japan and Thailand are derived from the increasing rate of NRE and RE following policy targets. Eventually, the characterization factors of RE are around one third that of NRE. Although Thailand is located in the tropical latitudes, it has been indicated that it is a water stressed country because of high water consumption in cultivating cash crops, in particular paddy rice and sugarcane. In addition, the irrigation system still needs to be improved and expansion as policy target is expected to increase irrigated area from 30 million rai to 60 million rai (1 ha ¼ 6.25 rai) in 2018. Water requirement for urbanization increases from time to time but the renewable water availability is the same. This causes results in the TEF of freshwater resources in Thailand being relatively higher than Switzerland and Japan. This result is aligned with the current situation of Thailand and will be more pronounced in drought years. Japan and Switzerland have followed the same criteria recommended by OECD. In particular, Japan belongs to the temperate zone with humid subtropical climate, so it has abundant rainfall. However, the causes of population and economic characteristics lead to water withdrawal to availability (19%) ratio in Japan higher than Switzerland (5%) which is reflected in their Eco-factors. Comparison of the normalized results in Fig. 3 is shown as the distribution of impact assessment of biodiesel using the Thai, Switzerland, and Japan Eco Scarcity methods. These reflect where the differences of the weighted policy targets are derived from. It can be seen that Thailand has less strict target on GHG emissions reduction than the other two countries. GHG emission impact of Thailand contributes only 34% to the total score, whereas Switzerland and Japan contribute 82% and 71%, respectively. The combined energy resource impact of Thailand is almost half of the total because the GHG emission is less dominant. These results show a robust and consistent Eco-Factors development in Thailand, the differences with the other countries being explained in line
Fig. 3. A comparison of normalized results of biodiesel with Eco-Factors of Thailand, Switzerland, and Japan Eco Scarcity methods.
with their policy targets, economic and ecological conditions.
4.2. Synthesis of sensitivity results from the various Eco-Factors A sensitivity analysis on LCA of biofuels cases involving the use of different TEFs was conducted in order to identify and focus on key data and assumptions that have the most influence on the results or uncertainty of the developed method. This study is on the effect of varying the parameters establishing the TEFs and considering the effect on the results in terms of benefit of biofuels over their fossil fuel counterparts. The fuel LCI remains the same as the base case, but the TEFs will be derived corresponding to target development and implementation. The policy-based scenarios consist of 1) strong policy setting: based on possible targets of each environmental aspects that originate from internal political will or international pressures and 2) weak policy setting: induced by disregarding and no serious action on environmental problems related to future threats. The list of changed parameters for each scenario compared with the base case is shown in Table 4 and description of scenario assumptions is shown below. Scenario 1: Strong policy setting. SC1A: Assume that there is no longer an increase in NRE or it is capped at the current level, and PEC will fulfill the further energy demand only with RE. In other words, RE feedstock utilization will increase to about 35%. The political weighting of RE:NRE then be changed from 3.3 / 5.0 and Fn will be decreased to 93,200 ktoe NRE-eq/year. (TEF is not changed significantly). SC1B: The water stress criteria follows more conservative threshold like OECD recommendation, WTA shifted up from 0.4 / 0.2. So, the Fk will be recalculated to 87,600 MCM. (TEF increases four times; 1.9 / 7.5 TEP/MCM). SC1C: Given GHG emission reduction target increases similar to Annex-I countries under the UNFCCC. Assumed target is 5% GHG reduction below the 1990 levels by 2020, this leads to Fk at 213 Mt CO2e. (TEF increases about five times; 1.6 / 8.5 TEP/kg CO2e). Scenario 2: Weak policy setting. SC2A: Assumed that EEDP will be reduced 50% from expected target. So, only the Fk would be increased to 107,500 ktoe, ceteris paribus. (EEDP planned to reduce final energy consumption of 115,000 ktoe to 99,800 ktoe as a target in 2021; TEF slightly
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7
Table 4 Parameters for sensitivity analysis for calculating the TEFs. Impact
Parameter
Primary energy Fn resource Fk Freshwater resources Fc Fk GHG emission Fk
SC2C
Unit
Base case Strong
Weak
ktoe NRE-eq ktoe MCM MCM M t CO2e
94,700 99,800 57,310 175,200 497
94,700 107,500 41,770 219,000 530
93,200 99,800 57,310 87,600 213
SC2B SC2A SC1C SC1B
decreased, 0.13 / 0.11 TEP/MJ NRE-eq; RE 0.04 / 0.03 TEP/MJ RE). SC2B: Assuming water stress criteria are weakened by changing the WTA threshold for initiation of stress situation from 0.4 to 0.5. The new Fk is recalculated as 219,000 MCM. (TEF decreased; 1.9 / 1.2 TEP/MCM). SC2C: As Thailand NAMAs indicated to reduce GHG emission 7e20% below BAU by 2020, this study hence specifies the minimum range as a target which leads to the new Fk of 530 Mt CO2e. (TEF slightly decreased; 1.6 / 1.4 TEP/kg CO2e). Life-cycle benefit gains of biofuels over their counterpart fossil fuels compared with the different scenario sets are presented in Table 5. For instance, the base case of biodiesel/diesel gains the benefit 26% (biodiesel has impact score 26% lower than diesel) and bioethanol/gasoline is 22%. For SC1, although both biofuels still have advantage over fossil fuels, only biodiesel has an increase in the life cycle benefit (þ4%), in contrast of bioethanol which loses benefit (14%). In the detailed analysis, SC1 indicated GHG TEF has the most influence for all results and the freshwater TEF has increased effects to only biofuel production. SC2 is less concentrated on GHG emissions, the primary energy resources then rise up to modest benefits (þ6%) because the biofuels gain some benefit from using mostly biomass in biofuel production. Although the various supposed policy target settings directly affect the TEFs, the results of this study are responsive mainly to the GHG emission impact. The individual sensitivity analysis comparing with base case was done in order to consider how and to what extent parameters of each scenario could influence the benefit gained from biofuel use. According to Fig. 4, the positive percentage presents gaining more benefit with respect to the given policy scenario. As can be seen, the results mostly correspond with the aggregated scenario set where the GHG emission impact still dominates the results. This can be seen in SC1B that shows the importance of freshwater resource used in biodiesel and bioethanol. If the weighting of this impact increases due to halving of water availability, all biofuel benefit will be lost. This is in line with what is mentioned in Damen (2010) that biomass production has a hotspot in water requirement especially to produce cassava. Meanwhile, SC2A and SC2C give less significant TEF of energy resources and GHG emissions; however, the benefits still remains but are reduced in their disadvantageous aspects. For instance, bioethanol requires a lot of energy feedstock; if the importance of this impact is reduced as given by SC2A, the overall biofuel benefit then will be reduced as well. Moreover, for SC2B, both biofuels gained benefit from weak TEF of freshwater resources. According to the results, freshwater resources related to policy has the most influence on biofuel benefit. Hence, freshwater
Table 5 The LCA benefit of biofuels varied by scenario TEF compared to base case result. Biofuel
Base case
SC1: High
SC2: Low
biodiesel/diesel bioethanol/CG
26% 22%
30% (þ4%) 8% (- 14%)
32% (þ6%) 28% (þ6%)
SC1A -60%
-50%
-40%
-30%
biodiesel/diesel benefit
-20%
-10%
0%
10%
20%
bioethanol/gasoline benefit
Fig. 4. The individual sensitivity analysis for evaluating the biofuel benefit according to various scenarios.
resources’ Eco-Factor should be considered carefully in biofuel LCA study because this TEF is derived from water stress criteria, which is adjustable depending on political will and the availability of renewable water resources (rainfall can fluctuate annually) of that specific year.
4.3. Determine critical flow by various targeting approaches Primary energy resource impact means energy resources originating from the earth or sun that can be used directly in the form of natural resources e.g. coal, gas, wood (biomass), sunlight, wind, etc. However, in the sense of consumption flow according to the related Thai energy policies i.e. AEDP is focused on increasing RE feedstock used, that directly corresponds to this impact; and, EEDP focused on how to reduce final energy demand relative to economic growth. The policy preferences are used as the basis of calculating normalized flow in unit of MJ-NRE-eq and the squared ratio of current total energy consumption to target amount (derived from BAU adjusted for expected reduction) is expressed to be a weighting. Thailand still allows increase in energy consumption; however, there are heavy efforts to push up the RE, particular in biomass utilization. So, combining the two targets for calculating energy resource flow results in a stronger weighting factor (0.50) than that for freshwater resources impact (0.11). These are obviously seen from the LCA results where energy resource impact due to biofuel production is not as low as might be expected when compared with quantity of fossil feedstock depleted for producing fossil fuels. Eventually, overall impact of biofuels are lower than fossil fuels. Actually, energy conservation measures are most intensively implemented as compared to GHG mitigation measures because these energy measures might have a more direct effect to economics than GHG measures. In contrast, for freshwater resources, using the OECD's water scarcity criteria seems to be too conservative for the context of Thailand. Therefore, it needs to be mentioned that when determining the weighting factors, if new localized indications are available, they should be adopted and updated. The target derived from BAU approach is another reason which is incomparable across other policy targets because the BAU scenario will principally be used in case that present data are not available or other considered factors have to be relied on. Adopting the targets derived from BAU may result in an increase of impacts in the future years. Also, the BAU targets maybe not reflect the actual government efforts on, for example, GHG emission mitigation. Ideally, the critical flows should be derived from pollution control targets and natural resources conservation targets corresponding to the carrying capacity.
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4.4. Consideration for promoting sustainable development of biofuels in Thailand The Thai government has high expectations on the utilization of the abundant agro-industrial products including their residues. However, valuable natural resources are close to the limit already. For instance, Permpool et al. (2015) noted that an additional 91,200 ha of arable land (around 15% from 2012) are required for oil palm expansion in order to meet the AEDP target. Moreover, Gheewala et al. (2013) indicated the irrigation water requirement for cassava plantation only serving bioethanol production is 958 MCM/year or about 2% of annual total renewable water withdrawal. Even though the result of this assessment is focused only on the major environmental aspects of biofuels, it is foreseen that there are many impacts such as toxicity from agro-chemical contamination in nature, air pollution from energy conversion processes which may also be very important. In order to prolong the 1st generation biofuels requires the modern energy conversion techniques and, increasing biomass production yield. For instance, Jakrawatana et al. (2015) investigated the benefit from ethanol production options. Utilization of residues and waste from fuel conversion e.g. produced biogas by wastewater in bioethanol plant were studied by Moriizumi et al. (2012). Moreover, other social and environmental issues should also be considered in accordance with various international standards e.g. compliance with Roundtable on Sustainable Palm Oil (RSPO) scheme for oil palm plantation (Saswattecha et al., 2015). In terms of policy, the government should have a solid mandate on renewable energy policy and action with comprehensive consideration regarding environmental and social impacts besides economic benefits. Therefore, the limitation of natural capital or environmental carrying capacity concept should be adopted to formulate the proper policy measures and targets. As for the societal dimension, it should be ensured that social equity and equality in resources distribution are included determining in ultimate goals. 5. Conclusions This study develops an LCIA method for Thailand following the Swiss Eco Scarcity method introducing three Thai Eco-Factors derived from “distance-to-target” approach e primary energy resources, freshwater resources and GHG emissions. These are based on available current situation data and related policy targets. These TEFs are then applied to a case study on comparative LCA assessment of biofuels (cassava ethanol and palm biodiesel) and fossil fuels (diesel and gasoline). The result shows biodiesel and ethanol have environmental advantages over their fossil-based equivalents. The TEFs of primary energy resources and GHG emissions are derived based on Thailand's targets which have been set to allow increase in order to support economic growth. Those differ from TEF for freshwater resources that is derived from the target corresponding to water stress criteria. When compared to Swiss and Japan Eco Scarcity methods which have the same principles, there are some differences in weighting due to the diversified policy targets and national circumstances i.e. economic characteristics, geography, and political preferences which are the most meaningful. Hence, the variation in policy targets and uncertainty in gathered data are included in sensitivity analysis based on strong and weak policy targets. The results show both biofuels still perform better than fossil fuels (considering only three environmental aspects). As anticipated based on the results of previous assessments, GHG emissions and water resources are revealed in this study too as significantly affecting the comparative results. GHG emission results affect both biofuels and fossil fuels, whereas
water resources are significant only for biofuels. However, it is recommended to extend this study to a more complete assessment considering all environmental aspects which will bring about more relevant and precise results and utilize the full potential of LCA. Acknowledgments The research is supported by the Joint Graduate School of Energy and Environment (JGSEE) at King Mongkut's University of Technology Thonburi and the Thailand Graduate Institute of Science and Technology scholarship, TGIST G-33-20-58-057D. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2016.07.054. References Brown, A., Matlock, M.D., 2011. A Review of Water Scarcity Indices and Methodologies. White paper 106. Büsser, K.S., Frischknecht, R., Kono, J., Hayashi, K., 2010. Ecological Scarcity Japan. Uster, Switzerland [online], Available: www.esu-services.ch/fileadmin/ download/buesser-2012-ecological_scarcity_Japan.pdf (accessed 12.07.13). Damen, B., 2010. BEFS Thailand: Key Results and Policy Recommendations for Future Bioenergy Development. Environment and Natural Resources Working Paper No. 43-FAO, Rome, Italy. 59 pp.. DEDE, 2012a. The Renewable and Alternative Energy Development Plan. Department of Alternative Energy Development and Efficiency - Ministry of Energy, Bangkok, Thailand. DEDE, 2012b. The Implementation of Reduction in GHGs Emission in Thailand [in Thai]. Department of Alternative Energy Development and Efficiency, Ministry of Energy, Bangkok, Thailand. DETEC, 2009. DETEC Ordinance on Proof of the Positive Aggregate Environmental Impact of Fuels from Renewable Feedstock (Biofuels Life Cycle Assessment Ordinance, BLCAO). Federal Department of Environment, Transport, Energy and Communications, Swiss Confederation, Bern, Switzerland. EforE, 2006. Biomass (in Thai). Energy for Environment Foundation, Bangkok, Thailand. EPPO, 2013. Energy Statistics of Thailand 2012. Energy Policy and Planning Office, Ministry of Energy, Bangkok, Thailand. EPPO, 2014. 20eYear Energy Efficiency Action Plan (2011-2030). Energy Policy and Planning Office, Ministry of Energy, Bangkok, Thailand. FAO, 2013. AQUASTAT Database - Food and Agriculture Organization of the United Nations [online], Available: www.fao.org/nr/water/aquastat/data/cf/readPdf. html?f¼THA-CF_eng.pdf (Accessed on 05.06.14). Frischknecht, R., Büsser, K.S., 2013. Swiss Eco-factors 2013 According to the Ecological Scarcity Method. Methodological fundamentals and their application in Switzerland. Environmental studies no. 1330. Federal Office for the Environment, Bern, Switzerland, 254 pp. Frischknecht, R., Jungbluth, N., Althaus, H., Bauer, C., Doka, G., Dones, R., Hischier, R., € llner, T., 2007. Implementation of Life Cycle Impact Hellweg, S., Humbert, S., Ko Assessment Methods. Ecoinvent report No. 3, v2.0. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland. Frischknecht, R., Steiner, R., Jungbluth, N., 2009. The Ecological Scarcity Methodeeco-factors 2006-A Method for Impact Assessment in LCA. Environmental Studies No. 0906. Federal Office for the Environment, Bern, Switzerland, 188 pp. Gheewala, S.H., 2012. Food, fuel and climate change: policy prospects and performance of biofuels in Thailand. In: Damen, B., Tvinnereim, S. (Eds.), Sustainable Bioenergy in Asia: Improving Resilience to High Food Prices and Climate Change. RAP Publication 2012/14. Food and Agriculture Organization of the United Nations (FAO), Bangkok, Thailand, 105 pp. Gheewala, S.H., Silalertruksa, T., Nilsalab, P., Mungkung, R., Perret, S.R., Chaiyawannakarn, N., 2013. Implications of the biofuels policy mandate in Thailand on water: the case of bioethanol. Bioresour. Technol. 150, 457e465. Goedkoop, M.J., Heijungs, R., Huijbregts, M., De Schryver, A., Struijs, J., Van Zelm, R., 2012. ReCiPe 2008, A Life Cycle Impact Assessment Method Which Comprises Harmonised Category Indicators at the Midpoint and the Endpoint Level first ed. (revised), Report I: Characterisation. 130 pp. Ho, D.P., Ngo, H.H., Guo, W., 2014. A mini review on renewable sources for biofuel. Bioresour. Technol. 169, 742e749. Hoekstra, A.Y., Mekonnen, M.M., Chapagain, A.K., Mathews, R.E., Richter, B.D., 2012. Global monthly water scarcity: blue water footprints versus blue water availability. PLoS One 7, e32688. IPCC, 2006. In: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), Chapter 3: Mobile Combustion, Volume 2: Energy, 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. IGES, Japan. IPCC, 2013. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K.,
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LCA of biofuels in Thailand using Thai Ecological Scarcity method Naruetep Lecksiwilai a, b, Shabbir H. Gheewala a, b, *, Thapat Silalertruksa a, b, Jitti Mungkalasiri c a The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangkok, 10140, Thailand b Centre of Excellence on Energy Technology and Environment, PERDO, Bangkok, Thailand c National Metal and Materials Technology Centre (MTEC), National Science and Technology Development Agency (NSTDA), Pathumthani, Thailand
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 January 2016 Received in revised form 8 July 2016 Accepted 9 July 2016 Available online xxx
Thailand has been promoting biofuel production and consumption to substitute transport fuels in order to utilize domestic resources and contribute to greenhouse gas (GHG) mitigation. This study adopted a Life Cycle Impact Assessment (LCIA) method using “distance-to-target” approach based on the Swiss Eco Scarcity method. It is developed by using Thailand's current situation of pollutants emissions and natural resources use in line with the three related national policy goals i.e. GHG, primary energy and freshwater resources; the Eco-Factors are 1.6, 0.13 and 1.9 Thai Eco Point (TEP) per kgCO2e, MJ NRE-eq and m3 in respective units. These were used for an LCA comparison between cassava-based ethanol and palm oilbiodiesel with their fossil fuel counterparts on an energy basis. The LCA results indicate both biofuels have lower impacts than the equivalent fossil fuels and the main contributor is GHG emissions from biofuel production. The sensitivity analysis also indicated the given policy related to freshwater and GHG influence the biofuel benefits. Therefore, the strong policy and technical options in energy production are still required for sustainable biofuels following the government intention. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Life cycle impact assessment Biofuel Ecological Scarcity method Distance-to-target
1. Introduction In the modern world, broad concerns about climate change, limited fossil fuels supplies and sustainable use of natural resources have evolved a new direction towards alternative energy. Many studies have presented that biofuels are now a viable alternative to fossil fuels (Gheewala, 2012; Damen, 2010). Securing biomass feedstock supplies for the long-term future requires expansion of biofuel feedstock cultivation, the scaling up of production and advanced technology. One of the key challenges is the conversion of biomass feedstock to provide more convenient energy carriers like liquid agrofuels (biofuels) and electricity without losing the balance between economic growth and sustainable resources development. Thailand as one of the top agro-industrial countries has been rapidly developing its biofuel industry due to strong government commitment. Biofuels play a major role to meet an increasing energy demand while restricting the emission of climate change-
* Corresponding author. The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangkok, 10140, Thailand. E-mail address: [email protected] (S.H. Gheewala).
inducing greenhouse gases (GHGs). According to the Thai government's 10-year Alternative Energy Development Plan, AEDP 2012e2021 (DEDE, 2012a), biofuel production targets were set aiming to support the agricultural sector with an emphasis on the need for crop productivity improvement and strengthening competitive advantage. AEDP set the ambitious target to increase the production of ethanol from 1.2 to 9 ML/d by focusing on competitive crops, i.e. sugarcane and cassava, which have potential to increase their production yields. However, the biodiesel target leaping from 2.3 to 6 ML/d is increasing the demand for agricultural land for oil palm cultivation. Another AEDP's objective is GHG emission reduction (DEDE, 2012b), which can be achieved because of the apparent carbon neutrality of biomass-based fuels. Life Cycle Assessment (LCA) is a tool for environmental sustainability assessment. It can be used for examining feasibility of studies as well as exploring optimal measures before implementation. Setting the objectives of an LCA study significantly influences the relevance of result interpretation because a variety of choices are analyzed in order to opt for the best practices that would create least negative impact on the environment. In the past, even though there have been several LCA studies concerning the controversial issues on the sustainability of first-generation
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Nomenclature AEDP BAU CED EEDP FU GHG GWP HHV HSD LCI LCIA
Renewable and Alternative Energy Development Plan business as usual Cumulative Energy Demand National Energy Efficiency Development Plan functional unit greenhouse gas global warming potential high heating value high speed diesel life cycle inventory life cycle impact assessment
biofuels (Ho et al., 2014), it has been shown that GHG benefits can be achieved if precautions are taken against indiscriminate land use change from forests. For example, Silalertruksa et al. (2012) investigated the benefit of life cycle cost and externalities of palm oil biodiesel showing results 3e76% lower than fossil-based diesel; the additional GHG emissions due to the conversion of forest to oil palm cultivated land for biodiesel will increase from 1.4 to 6.6 kg CO2/L (production and use) higher than diesel. Nguyen and Gheewala (2008) found that ethanol production from cassava in Thailand contributes to around 6% reduction in fossil energy and global warming impacts relative to gasoline. In addition, utilization of its residues or wastewater could reduce the impacts during the bioenergy conversion e.g. half of GHG emissions are reduced by utilizing the biogas from anaerobic wastewater treatment of cassava ethanol production (Numjuncharoen et al., 2015). However, there are disadvantages in terms of water use leading to competition with food crops which is being debated. Okadera et al. (2014) indicated the water footprint of energy supply (WFES) of Thailand will be increased to eight times greater than 25 years ago due to biomass energy. Most of the LCA studies in Thailand applied European or Japanese LCIA methods for assessing the environmental impacts. Some of the currently used LCIA methods e.g. ReCiPe (Goedkoop et al., 2012) and Japanese LIME (Itsubo and Inaba, 2003), are derived using the scientific data and discussions with stakeholders based in the developer country. Due to the technical difficulties and the demand for resources in developing LCIA methods like these, one possible solution is to develop LCIA based on currently available data and related policy goals under Thailand context. The “distance-to-target” technique is a reliable approach for figuring the normalizing and weighting factors linked to the country's interests and priorities. The Swiss Eco Scarcity method is such an approach that could be used. It can formulate “Eco-Factors” to calculate robust endpoint impact factors with a clear concept and directly related to the goal of the LCA study. The ecological scarcity method was first issued by the Swiss Federal Office for the Environment (FOEN) in 1998. This LCIA methodology is based on the distance to target principle. The calculation of the Eco-Factors is determined by setting the current flow in relation to (or distance from) the critical flow (can be so implied as policy targets). After being first published by Frischknecht, one of the key developers, it has been updated based on new scientific findings and political targets with 2013 as base year (Frischknecht and Büsser, 2013). The previous Swiss Ecological Scarcity 2006 (Frischknecht et al., 2009) was regarded as a policy decision tool for developing the Biofuels Life Cycle Assessment Ordinance (BLCAO) in 2009 (DETEC, 2009) and for several biofuel studies in Switzerland. Other countries used this for developing Eco-Factors with their own national
LHV MCM Mtoe NAMAs NRE OECD RE RON TEF TEP WTA
low heating value million cubic meter million tonnes of oil equivalent Nationally Appropriate Mitigation Actions non-renewable energy Organization for Economic Co-operation and Development renewable energy Research Octane Number Thai Eco-Factor Thai Eco Point withdrawal to availability (ratio)
information. For instance, the Japanese Eco Scarcity method developed by Büsser et al. (2010) adopted Japan Environmental Policy Priorities Index (JEPIX), which is an existing monitoring system to evaluate the current situation as compared to legislation. Although the Swiss Eco Scarcity method could be applied to LCA assessment in Thailand, the results would express Swiss preferences, which may not reflect the Thailand context. Therefore, the objectives of this study are adopting Eco Scarcity concept for Thailand context using proper information and also indicating what the limitations on its application are. The three relevant impact categories with regards to bioenergy are focused on, i.e. primary energy resources, freshwater resource and GHG emissions. Then, a comparative LCA is conducted on the two major biofuels in Thailand, i.e. ethanol and biodiesel, with their counterpart conventional fossil equivalents in utilizing as transportation fuel based on energy as functional unit.
2. Methodology The Ecological Scarcity method is partly based on characterization models used in LCA (e.g. global warming, ozone depletion, acidification, primary energy resources and land use). Other interventions are assessed individually (e.g. NH3, heavy metals) or as a group (e.g. NMVOCs). It is upon the availability of data and importance in the policy view that reflects the policy targets. The general expression adopted from the Swiss Ecological Scarcity method used for all elementary flows and the Eco-Factor is defined as follows:
Eco Factor
¼
K
TEP Fn
Fc Fk
2 c
(1)
With: K ¼ Characterization factor of a pollutant or of a resource substance.Fn ¼ Normalization flow: current annual flow, with nation as system boundary.F ¼ Current flow: current annual flow of substance.Fk ¼ Critical flow: critical maximum acceptable annual flow of substance.TEP ¼ Thai Eco Point as a unit of this assessment.c ¼ constant ¼ 1012/y; to obtain presentable numerical values. Principally, the weighting factor is developed from the current flow and critical flow. For this study, the current flows refer to the 2011 as the base year situation, corresponding to last year of the 5year term of the recent Tenth National Economic and Social Development Plan (2007e2011) published by the National Economic and Social Development Board (NESDB, 2006). The official historical data from reliable and publicly available sources and related studies are extracted to use for current flows. Nevertheless,
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for some data which cannot be directly used as current flow, different techniques have to be adopted. For instance, extrapolating to base year in case of no record in corresponding year, aggregating data from many sources as a flow, and using sensible assumptions to complete the calculation of flows. The critical annual flows correspond to policy objectives which reflect targets to be achieved within 5e20 years as midterm goals; they depend on the political motivation of each aspect. These are pinned on various years depending on the respective policy target to be considered in the circumstance of both Thailand context and global perspective. Thailand is not as developed as the OECD countries, so the international mandatory measures to reduce environmental impacts such as GHGs emission reduction are not fully adopted yet. However, in accordance of the precautionary principle and conservative perspective, this study has to set the target stricter than the current ones or at least equivalent to the current status with the assumption of no further increasing of impacts.
and liquid fossil-based carriers are 0.95, gaseous is 0.90 and for other biomass carriers cited from EforE (2006). Then, converting from final gross energy demand to PEC is accomplished by using primary energy factor and yield of energy production, calculated from the Thai National LCI database (MTEC, 2015) with Cumulative Energy Demand (CED) version 1.09 (Frischknecht et al., 2007). By CED, we can indicate RE and NRE feedstock requirement to produce each energy carrier. And, characterization factors from the above are applied to calculate the characterized primary energy resources flow. The normalization flow is 94,700 ktoe NRE-eq/year (an example of the calculation is shown in the Supporting information S2). The relevant parameters are shown in Table 1. The current final energy consumption in 2011 was 70,562 ktoe which is used as current flow. Projected to 2021 according to EEDP, 105,241 ktoe of final energy demand is used as critical flow. Hence, TEF can calculated as 1.3 TEP/MJ NRE-eq. According to the above characterization factor, TEF for RE is 0.4 TEP/MJ.
3. Results
3.1.2. Freshwater resources Thailand is an agriculture-based country with 71% of total freshwater demand consumed by the agriculture sector. AQUASTAT (FAO, 2013) reported the data of total renewable water resources (including surface runoff, transboundary steam flow and groundwater) in Thailand at 438,000 MCM in 2012. Moreover, FAO also revealed that the total water withdrawal was 57,310 MCM in 2012 (considering only renewable water resources for all sectors). It must be mentioned these two figures are the same as reported since 2007. The Royal Irrigation Department (RID) is the main authority responsible for handling freshwater allocation to the various user sectors in Thailand. The average annual rainfall of 1423 mm can be calculated to 735,000 MCM of total rain water and the stored water drainage annually totaled 59,340 MCM according to RID's online reporting. RID (2012) estimated the total freshwater consumption for all sectors consisting of rain and irrigation water was 998,000 MCM. Comparing the data from two sources i.e. AQUASTAT and RID, it was found that the former was more suitable to be used as “flow” since it considers actual controllable and usable freshwater resources instead of only precipitation which is used for the latter. According to a UNESCO study (Brown and Matlock, 2011) which referred to Raskin et al. (1997), 40% threshold ratio of water withdrawals to total renewable water resources is commonly used as water stress level. This is in line with Oki and Kanae (2006) and Hoekstra et al. (2012) who concurred that exceeding 40% ratio of irrigation water (blue water) withdrawal to annual run-off leads to a severe water stress situation. However, the Swiss Eco Scarcity 2013 and Japan Eco Scarcity (Büsser et al., 2010) preferred using the study of OECD (2003) which categorized the stress would start at 20% of water withdrawal to availability (WTA). As Thailand is located in the tropical zone and is an agriculture based country, 40% is the proper level to be calculated as freshwater withdrawal target. Thus, TEF for freshwater resources can be calculated following the Eq. (1) as 1.9 TEP/m3 (shown the calculation in the Supporting information S3). Anyway, population growth and consequence of climate change will increase the severity of the problem. This TEF may be underestimated for long-term sustainability assessment.
3.1. Eco-Factor development 3.1.1. Primary energy resources Thailand's energy consumption largely relies on fossil-based energy carriers. According to the energy statistics (EPPO, 2013), Thailand consumes around 73 Mtoe per year with most of the energy carriers being petroleum products used for the transportation sector at 48%. The second largest share is electricity at 20% which has grown by 3.3% per year on average during the last 5 years. Renewable energy contributed only 7% (excluding traditional renewable energy, e.g. fuel wood, charcoal, etc.). The Thai government assigned the Ministry of Energy to establish the following energy plans which could lead to energy security for next decade; 1) AEDP 2012e2021 (DEDE, 2012a) which proposes increased utilization of renewable energy resources to 25% in the next 10 years. Key measures included are supporting various types of renewable energy e.g. biomass, waste, solar, etc. to replace fossil resources and reducing dependency on energy imports also. 2) EEDP 2011e2030 (EPPO, 2014) focuses on the target of energy consumption reduction according to energy intensity (energy used per unit of GDP) reduction by 25% for all economic sectors in 2030 from 2005 BAU (Business As Usual) levels. According to the energy situation and future plans mentioned above, energy can be categorized into two types; non-renewable energy (NRE) derived from fossil-based fuels and renewable energy (RE). The basis for characterization is the relative change of energy feedstock types in the final utilization when projected to 2021 corresponding to AEDP and EEDP compared to base year, 2011. The final energy demand of both NRE and RE are still expected to increase 1.14 and 3.74 times, respectively. The relative change normalized by NRE demand can establish the characterization factor on a policy basis. The characterized RE is 0.3 [1.14/3.74] MJ NRE-eq/MJ and of course, NRE is 1 MJ NRE-eq/MJ. In other words, the consumption of primary energy resources derived from NRE will be worse than RE in view of environmental impacts according to policy intentions, i.e. 1 MJ from NRE is rated as equivalent to 0.3 MJ from RE (details shown in the Supporting information S1). The final consumption of energy carrier compositions of 2011 is gathered from the energy balance of the Department of Alternative Energy Development and Efficiency (DEDE 2012a,b). This is converted into primary energy consumption (PEC) by using gross energy content with its LHV/HHV ratio which assume that the solid
3.1.3. Greenhouse gases emission Thailand has been a member of the United Nations Framework Convention on Climate Change (UNFCCC) since June 1992, contributing to international efforts to address climate change issues. In February 1999, Thailand signed the Kyoto Protocol as a Non-Annex I country which did not have binding obligations to limit GHG emissions. Recently, IPCC published the fifth assessment report (AR5) which outlines the global warming potential (GWP)
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Table 1 Calculation of characterized primary energy resources. Energy consumption (LHV)
HHV/LHV
ktoe/year
e
Energy consumption (HHV)
Primary energy factor Total
RE
NRE
ktoe/year
e
e
e
ktoe NRE-eq/year
105,241
94,741
1.32
6558
6560
0.00
1.29
7142
7142
1.44 1.44 1.47 1.47 1.47 1.51 1.16 1.16
0.00 0.00 0.07 0.07 0.15 0.87 0.00 0.00
1.44 1.44 1.40 1.40 1.33 0.67 1.16 1.16
3473 47 2447 2145 256 11 5054 13
3470 47 2365 2073 238 7 5053 13
1.14 1.16 1.14
0.00 0.05 0.00
1.14 1.10 1.14
19,067 726 2309
19,064 700 2308
70,562
Fossil energy carriers Petroleum Natural gas
4485
0.90
4983
1.32
0.00
Oil LPG Gasoline RON 91 RON 95 E10 RON 91 E10 RON 95 E20 RON 95 E85 Jet fuel Kerosene Diesel HSD HSD B5 Fuel oil
5001 5462 2292 31 1581 1386 165 7 4150 11 16,527 15,932 595 1916
0.90 0.95
5557
1.29
2413 33 1664 1459 174 7 4368 12
Coal Anthracite Bituminous Coke Briquettes and other coal Lignite Others Renewable Biomass Fuel wood Paddy husk Bagasse Agricultural waste Garbage Biogas Solar energy Traditional renewable Fuel wood Charcoal Paddy husk Agricultural waste Electricity
Total PEC, characterized
ktoe/year
Total
0.95 0.95 0.95
Total PEC,
0.95
16,771 626 2017
6601 165 257 109 4720 1350
0.95 0.95 0.95 0.95 0.95
174 271 115 4968 1421
1.00 1.00 1.00 1.00 1.00
0.00 0.00 0.00 0.00 0.00
1.00 1.00 1.00 1.00 1.00
174 271 115 4988 1427
4070 140 64 3065 801
0.83 0.92 0.80 0.80
169 70 3831 1001
1.00 1.00 1.00 1.00
1.00 1.00 1.00 1.00
0.00 0.00 0.00 0.00
169 70 3831 1001
174 271 115 4988 1427 e e 51 21 1165 304
2 482 2
0.90 0.90 1.00
2 536 2
0.37 0.37 1.85
0.03 0.03 1.61
0.34 0.34 0.24
1 198 4
1 186 1
8582 3463 3047 993 1079 13,271
0.83 1.00 0.92 0.80 1.00
4172 3047 1079 1349 13,271
1.00 1.00 1.00 1.00 2.57
1.00 1.00 1.00 1.00 0.00
0.00 0.00 0.00 0.00 2.57
4172 3047 1079 1349 34,096
e 1268 926 328 410 34,063
for various gases. The 100-year time horizon has been commonly selected and also applied to this study as the characterization factor. According to the Second National Communication on Climate Change (ONEP, 2010), the amount of GHG emission excluding landuse change and forestry sector totaled 237 M tCO2e in 2000. The emission data in 2011 can be projected from 2000 with the average economic annual growth at constant rate of 4.2% (consistent with energy consumption projection). The estimation of characterized three major GHGs plus other GHGs totals 394 Mt CO2-eq/y which is then used as both normalization flow and current flow in Eq. (1). The IPCC's measures encourage the developing countries to participate in GHG mitigation by using the framework called Nationally Appropriate Mitigation Actions (NAMAs). In Thailand, the National Climate Change Committee (NCCC) submitted Thailand's NAMAs that would be attempting to reduce its GHG emissions in the range of 7e20 percent below business as usual (BAU) in the energy and transportation sectors in 2020. The Alternative Energy Development Plan (AEDP) 2012e2021 (DEDE, 2012a) focuses on the energy sector that has a high potential in reducing GHGs. The Thailand Greenhouse Gas Management Organization (Public Organization) expects that by the year 2020, the energy sector would reduce GHG emissions by approximately 73 Mt CO2e annually.
Moreover, the projected emission of 2020 as BAU adjusted for the expected reduction can be calculated as 495 Mt CO2e. Moreover, 2 Mt CO2e of other GHGs i.e. SF6 and HCFCs (WRI, 2014) are assumed not to increase by appropriate and effectively launched programs. Therefore, a critical flow of this assessment is 497 Mt CO2e. Therefore, the TEF can be calculated according to Eq. (1) is 1.6 TEP/kg CO2e. 3.2. LCA study with biofuel cases The three abovementioned impact categories selected for developing the TEF were used for the LCA study comparing biofuels and fossil fuels comprising palm oil methyl ester, fossil-based diesel, cassava-based ethanol and conventional gasoline henceforth referred to in the text as biodiesel, diesel, ethanol and gasoline. The system boundary of all the considered fuels is shown in Fig. 1; it includes feedstock acquisition, treatment and refining, energy conversion, and direct use as biofuel for vehicles substituting its counterpart fuel. The functional unit is considered as 100 MJ energy basis for each of the energy carriers. This assessment excludes the other benefit of ethanol substituting fossil MTBE in gasoline in order to increase octane rating helping prevent
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Fig. 1. System boundary for LCA study with Eco Scarcity Method.
engine knocking. The LCI and background data for this study were gathered from various sources shown in Table 2. Primary energy resources for fossil-based analyses were from the Thailand National LCI database (MTEC, 2015) calculated by applying the techniques of Cumulative Energy Demand (CED) 1.08. Data for biodiesel and cassava-ethanol were extracted from Silalertruksa and Gheewala (2012, 2009). Freshwater consumption for fuel productions considers only total irrigation water use. The greenhouse gases emission due to land use change and the modification of engine for compatibility with biofuels are not taken to account for this study. The CO2 emitted during combustion of biofuels being generally of biogenic origin, is not included in the calculations. However, the methanol used in biodiesel conversion is fossil-based, so this fossil carbon content in the resulting biodiesel contributes to nonbiogenic GHG emission and is thus counted (Pleanjai et al., 2009). The non-CO2 GHG emissions are calculated from IPCC (2006) using the global warming potentials from AR5 GWP100 (IPCC, 2013). The result of TEF assessment of the four fuel types is shown in Fig. 2. The overall impact of biodiesel (21.1 TEP/FU) is about 33% lower than diesel (28.8 TEP/FU). Biodiesel feedstock acquisition (palm oil cultivation) needs to consume a lot of freshwater, while the fossil feedstock acquisition does not. Oil palm is grown in southern Thailand where the rainfall rate is high and climate conditions are suitable; however, even then some farmers use irrigation water in the dry season in order to maintain productivity. Furthermore, this assessment result includes water embodied in agrochemicals used. The produced water from fossil oil production is calculated, but due to its insignificant amount, it is not shown in Fig. 2. Biodiesel gets the benefit of biomass feedstock because CO2 from biofuel combustion is not accounted. Bioethanol (25.2 TEP/FU) Table 2 Life cycle inventory for 1 L of fuels and data sources for LCA assessment. Unit Primary energy resource RE resources NRE resources Freshwater resource GHGs emissions Production Use Energy value (NCV) Density
Biodiesel
Diesel
Bioethanol
Gasoline
MJ MJ MJ m3 kgCO2e kgCO2e kgCO2e MJ/L kg/L
33.85 15.82 0.70 1.50 1.34 0.15 33.5 0.86
/1 /1 /2
/1 /3
0.01 41.40 0.02 3.22 0.48 2.74 43.4 0.84
/4 /4 /5
/5 /6
24.33 8.91 0.57 1.34 1.32 0.01 21.2 0.79
/7 /7 /8
/9 /6
0.05 46.13 0.03 2.65 0.38 2.27 31.8 0.74
/4 /4 /5
/5 /6
Source: /1 Silalertruksa and Gheewala, 2012; /2 Pongpinyopap et al., 2014; /3 Pleanjai et al., 2009; /4 MTEC, 2015; /5 EcoInvent 3.0; 6/ IPCC, 2006; 7/ Silalertruksa and Gheewala, 2009; 8/ Gheewala et al., 2013; 9/ Kawasaki et al., 2015.
Fig. 2. LCA result of selected fuels assessed by Thai Eco Scarcity method.
also performs better than gasoline (32.3 TEP/FU). The energy content of biofuels are lower than their counterparts. As a result, TEFs of biofuels are not much lower than their fossil counterparts when compared on a MJ basis. 4. Discussion Referring to the Thai Eco Scarcity development and exercise with LCA study on biofuels compared with fossil-derived fuels, further observations are discussed in the sections below. 4.1. Comparing with the Swiss and Japan Eco Scarcity Methods As the Thai Eco Scarcity method is based on the same principles as the original Swiss and Japanese Eco Scarcity methods, a comparative analysis would yield interesting insights into the differences arising from the policy perspectives in particular the three environmental aspects considered in the assessment with biodiesel case (the details are shown in Section 3.2). The Swiss, Japan and Thai Eco-Factors of the considered impact categories are shown in Table 3. In result of the different policy targets used for Eco-Factor development, the value of the Eco-Factors could not be compared directly across nations. In the Swiss method, GHG emission obviously dominates other two impacts since it sets a very intensive reduction target of 80% under 1990 levels or one fifth of 2009 levels. This is because of their leading and undertaking role in global GHG mitigation. So, this makes the other impacts much less significant. Another point is that the Swiss economy, being based on service sectors including only
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Table 3 Comparing between Thai, Swiss and Japanese Eco Scarcity methods in the three selected impact categories. Impact category
Unit
Switzerland (UBP/unit)
Japan (EP/unit)
Thailand (TEP/unit)
Primary energy resources Nonrenewable energy Renewable energy Freshwater resource GHG Emission
/MJ NRE /MJ RE /m3 /kg CO2e
3.4 1.1 23 460
0.1 0.034 12 18
0.13 0.04 1.9 1.6
small contribution from agriculture (11%) and heavy industries (7%), has low GHG emissions (UNFCCC, 2014). That is the reason why the national flow used as normalization flow is fairly low. Hence, this results in a high Eco-factor for GHG compared to other countries. Although Japan is also a developed country, energy demand of the industrial sector as well as the urban population is still increasing. In 2007, the total GHGs emitted by Japan were 1374 Mt CO2e (excluding LULUFC, 30-times larger than Switzerland). Moreover, they expected to reduce 25% below the 1990 levels or 23% from present. The Japanese Eco-Factor is therefore weaker than the Swiss one. On the other hand, Thailand being a Non-Annex-1 country did not set the reduction target based on 1990 levels. According to the recent NAMAs pledged, Thailand has committed to decelerate the GHG emissions compared to BAU by 2020. Notwithstanding, at that target year Thailand is allowed to increase GHG emissions compared to 2011 (394 / 497 Mt CO2e) which results in a relatively low GHG Eco-Factor (as compared to the other countries). All countries expect to increase the RE demand partly for supporting GHG mitigation actions and internal energy security issues. Characterization factors are formulated on different bases, e.g. the Swiss Eco-Factor is derived from composition of energy carriers in final energy demand, whereas those of Japan and Thailand are derived from the increasing rate of NRE and RE following policy targets. Eventually, the characterization factors of RE are around one third that of NRE. Although Thailand is located in the tropical latitudes, it has been indicated that it is a water stressed country because of high water consumption in cultivating cash crops, in particular paddy rice and sugarcane. In addition, the irrigation system still needs to be improved and expansion as policy target is expected to increase irrigated area from 30 million rai to 60 million rai (1 ha ¼ 6.25 rai) in 2018. Water requirement for urbanization increases from time to time but the renewable water availability is the same. This causes results in the TEF of freshwater resources in Thailand being relatively higher than Switzerland and Japan. This result is aligned with the current situation of Thailand and will be more pronounced in drought years. Japan and Switzerland have followed the same criteria recommended by OECD. In particular, Japan belongs to the temperate zone with humid subtropical climate, so it has abundant rainfall. However, the causes of population and economic characteristics lead to water withdrawal to availability (19%) ratio in Japan higher than Switzerland (5%) which is reflected in their Eco-factors. Comparison of the normalized results in Fig. 3 is shown as the distribution of impact assessment of biodiesel using the Thai, Switzerland, and Japan Eco Scarcity methods. These reflect where the differences of the weighted policy targets are derived from. It can be seen that Thailand has less strict target on GHG emissions reduction than the other two countries. GHG emission impact of Thailand contributes only 34% to the total score, whereas Switzerland and Japan contribute 82% and 71%, respectively. The combined energy resource impact of Thailand is almost half of the total because the GHG emission is less dominant. These results show a robust and consistent Eco-Factors development in Thailand, the differences with the other countries being explained in line
Fig. 3. A comparison of normalized results of biodiesel with Eco-Factors of Thailand, Switzerland, and Japan Eco Scarcity methods.
with their policy targets, economic and ecological conditions.
4.2. Synthesis of sensitivity results from the various Eco-Factors A sensitivity analysis on LCA of biofuels cases involving the use of different TEFs was conducted in order to identify and focus on key data and assumptions that have the most influence on the results or uncertainty of the developed method. This study is on the effect of varying the parameters establishing the TEFs and considering the effect on the results in terms of benefit of biofuels over their fossil fuel counterparts. The fuel LCI remains the same as the base case, but the TEFs will be derived corresponding to target development and implementation. The policy-based scenarios consist of 1) strong policy setting: based on possible targets of each environmental aspects that originate from internal political will or international pressures and 2) weak policy setting: induced by disregarding and no serious action on environmental problems related to future threats. The list of changed parameters for each scenario compared with the base case is shown in Table 4 and description of scenario assumptions is shown below. Scenario 1: Strong policy setting. SC1A: Assume that there is no longer an increase in NRE or it is capped at the current level, and PEC will fulfill the further energy demand only with RE. In other words, RE feedstock utilization will increase to about 35%. The political weighting of RE:NRE then be changed from 3.3 / 5.0 and Fn will be decreased to 93,200 ktoe NRE-eq/year. (TEF is not changed significantly). SC1B: The water stress criteria follows more conservative threshold like OECD recommendation, WTA shifted up from 0.4 / 0.2. So, the Fk will be recalculated to 87,600 MCM. (TEF increases four times; 1.9 / 7.5 TEP/MCM). SC1C: Given GHG emission reduction target increases similar to Annex-I countries under the UNFCCC. Assumed target is 5% GHG reduction below the 1990 levels by 2020, this leads to Fk at 213 Mt CO2e. (TEF increases about five times; 1.6 / 8.5 TEP/kg CO2e). Scenario 2: Weak policy setting. SC2A: Assumed that EEDP will be reduced 50% from expected target. So, only the Fk would be increased to 107,500 ktoe, ceteris paribus. (EEDP planned to reduce final energy consumption of 115,000 ktoe to 99,800 ktoe as a target in 2021; TEF slightly
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Table 4 Parameters for sensitivity analysis for calculating the TEFs. Impact
Parameter
Primary energy Fn resource Fk Freshwater resources Fc Fk GHG emission Fk
SC2C
Unit
Base case Strong
Weak
ktoe NRE-eq ktoe MCM MCM M t CO2e
94,700 99,800 57,310 175,200 497
94,700 107,500 41,770 219,000 530
93,200 99,800 57,310 87,600 213
SC2B SC2A SC1C SC1B
decreased, 0.13 / 0.11 TEP/MJ NRE-eq; RE 0.04 / 0.03 TEP/MJ RE). SC2B: Assuming water stress criteria are weakened by changing the WTA threshold for initiation of stress situation from 0.4 to 0.5. The new Fk is recalculated as 219,000 MCM. (TEF decreased; 1.9 / 1.2 TEP/MCM). SC2C: As Thailand NAMAs indicated to reduce GHG emission 7e20% below BAU by 2020, this study hence specifies the minimum range as a target which leads to the new Fk of 530 Mt CO2e. (TEF slightly decreased; 1.6 / 1.4 TEP/kg CO2e). Life-cycle benefit gains of biofuels over their counterpart fossil fuels compared with the different scenario sets are presented in Table 5. For instance, the base case of biodiesel/diesel gains the benefit 26% (biodiesel has impact score 26% lower than diesel) and bioethanol/gasoline is 22%. For SC1, although both biofuels still have advantage over fossil fuels, only biodiesel has an increase in the life cycle benefit (þ4%), in contrast of bioethanol which loses benefit (14%). In the detailed analysis, SC1 indicated GHG TEF has the most influence for all results and the freshwater TEF has increased effects to only biofuel production. SC2 is less concentrated on GHG emissions, the primary energy resources then rise up to modest benefits (þ6%) because the biofuels gain some benefit from using mostly biomass in biofuel production. Although the various supposed policy target settings directly affect the TEFs, the results of this study are responsive mainly to the GHG emission impact. The individual sensitivity analysis comparing with base case was done in order to consider how and to what extent parameters of each scenario could influence the benefit gained from biofuel use. According to Fig. 4, the positive percentage presents gaining more benefit with respect to the given policy scenario. As can be seen, the results mostly correspond with the aggregated scenario set where the GHG emission impact still dominates the results. This can be seen in SC1B that shows the importance of freshwater resource used in biodiesel and bioethanol. If the weighting of this impact increases due to halving of water availability, all biofuel benefit will be lost. This is in line with what is mentioned in Damen (2010) that biomass production has a hotspot in water requirement especially to produce cassava. Meanwhile, SC2A and SC2C give less significant TEF of energy resources and GHG emissions; however, the benefits still remains but are reduced in their disadvantageous aspects. For instance, bioethanol requires a lot of energy feedstock; if the importance of this impact is reduced as given by SC2A, the overall biofuel benefit then will be reduced as well. Moreover, for SC2B, both biofuels gained benefit from weak TEF of freshwater resources. According to the results, freshwater resources related to policy has the most influence on biofuel benefit. Hence, freshwater
Table 5 The LCA benefit of biofuels varied by scenario TEF compared to base case result. Biofuel
Base case
SC1: High
SC2: Low
biodiesel/diesel bioethanol/CG
26% 22%
30% (þ4%) 8% (- 14%)
32% (þ6%) 28% (þ6%)
SC1A -60%
-50%
-40%
-30%
biodiesel/diesel benefit
-20%
-10%
0%
10%
20%
bioethanol/gasoline benefit
Fig. 4. The individual sensitivity analysis for evaluating the biofuel benefit according to various scenarios.
resources’ Eco-Factor should be considered carefully in biofuel LCA study because this TEF is derived from water stress criteria, which is adjustable depending on political will and the availability of renewable water resources (rainfall can fluctuate annually) of that specific year.
4.3. Determine critical flow by various targeting approaches Primary energy resource impact means energy resources originating from the earth or sun that can be used directly in the form of natural resources e.g. coal, gas, wood (biomass), sunlight, wind, etc. However, in the sense of consumption flow according to the related Thai energy policies i.e. AEDP is focused on increasing RE feedstock used, that directly corresponds to this impact; and, EEDP focused on how to reduce final energy demand relative to economic growth. The policy preferences are used as the basis of calculating normalized flow in unit of MJ-NRE-eq and the squared ratio of current total energy consumption to target amount (derived from BAU adjusted for expected reduction) is expressed to be a weighting. Thailand still allows increase in energy consumption; however, there are heavy efforts to push up the RE, particular in biomass utilization. So, combining the two targets for calculating energy resource flow results in a stronger weighting factor (0.50) than that for freshwater resources impact (0.11). These are obviously seen from the LCA results where energy resource impact due to biofuel production is not as low as might be expected when compared with quantity of fossil feedstock depleted for producing fossil fuels. Eventually, overall impact of biofuels are lower than fossil fuels. Actually, energy conservation measures are most intensively implemented as compared to GHG mitigation measures because these energy measures might have a more direct effect to economics than GHG measures. In contrast, for freshwater resources, using the OECD's water scarcity criteria seems to be too conservative for the context of Thailand. Therefore, it needs to be mentioned that when determining the weighting factors, if new localized indications are available, they should be adopted and updated. The target derived from BAU approach is another reason which is incomparable across other policy targets because the BAU scenario will principally be used in case that present data are not available or other considered factors have to be relied on. Adopting the targets derived from BAU may result in an increase of impacts in the future years. Also, the BAU targets maybe not reflect the actual government efforts on, for example, GHG emission mitigation. Ideally, the critical flows should be derived from pollution control targets and natural resources conservation targets corresponding to the carrying capacity.
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4.4. Consideration for promoting sustainable development of biofuels in Thailand The Thai government has high expectations on the utilization of the abundant agro-industrial products including their residues. However, valuable natural resources are close to the limit already. For instance, Permpool et al. (2015) noted that an additional 91,200 ha of arable land (around 15% from 2012) are required for oil palm expansion in order to meet the AEDP target. Moreover, Gheewala et al. (2013) indicated the irrigation water requirement for cassava plantation only serving bioethanol production is 958 MCM/year or about 2% of annual total renewable water withdrawal. Even though the result of this assessment is focused only on the major environmental aspects of biofuels, it is foreseen that there are many impacts such as toxicity from agro-chemical contamination in nature, air pollution from energy conversion processes which may also be very important. In order to prolong the 1st generation biofuels requires the modern energy conversion techniques and, increasing biomass production yield. For instance, Jakrawatana et al. (2015) investigated the benefit from ethanol production options. Utilization of residues and waste from fuel conversion e.g. produced biogas by wastewater in bioethanol plant were studied by Moriizumi et al. (2012). Moreover, other social and environmental issues should also be considered in accordance with various international standards e.g. compliance with Roundtable on Sustainable Palm Oil (RSPO) scheme for oil palm plantation (Saswattecha et al., 2015). In terms of policy, the government should have a solid mandate on renewable energy policy and action with comprehensive consideration regarding environmental and social impacts besides economic benefits. Therefore, the limitation of natural capital or environmental carrying capacity concept should be adopted to formulate the proper policy measures and targets. As for the societal dimension, it should be ensured that social equity and equality in resources distribution are included determining in ultimate goals. 5. Conclusions This study develops an LCIA method for Thailand following the Swiss Eco Scarcity method introducing three Thai Eco-Factors derived from “distance-to-target” approach e primary energy resources, freshwater resources and GHG emissions. These are based on available current situation data and related policy targets. These TEFs are then applied to a case study on comparative LCA assessment of biofuels (cassava ethanol and palm biodiesel) and fossil fuels (diesel and gasoline). The result shows biodiesel and ethanol have environmental advantages over their fossil-based equivalents. The TEFs of primary energy resources and GHG emissions are derived based on Thailand's targets which have been set to allow increase in order to support economic growth. Those differ from TEF for freshwater resources that is derived from the target corresponding to water stress criteria. When compared to Swiss and Japan Eco Scarcity methods which have the same principles, there are some differences in weighting due to the diversified policy targets and national circumstances i.e. economic characteristics, geography, and political preferences which are the most meaningful. Hence, the variation in policy targets and uncertainty in gathered data are included in sensitivity analysis based on strong and weak policy targets. The results show both biofuels still perform better than fossil fuels (considering only three environmental aspects). As anticipated based on the results of previous assessments, GHG emissions and water resources are revealed in this study too as significantly affecting the comparative results. GHG emission results affect both biofuels and fossil fuels, whereas
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