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Biofuel Production in Vietnam: Cost-Effectiveness, Energy and GHG Balances

Loan T. Le

March, 2015

Comments should be sent to: Loan T. Le, Faculty of Economics, Nong Lam University, Ho Chi Minh city, Linh Trung Ward, Thu Duc district, Ho Chi Minh City, Vietnam Tel: +84-908-121816 Fax: +84-38-961708 Email: [email protected], [email protected]

The Economy and Environment Program for Southeast Asia (EEPSEA) was established in May 1993 to support training and research in environmental and resource economics. Its goal is to strengthen local capacity in the economic analysis of environmental issues so that researchers can provide sound advice to policymakers.

To do this, EEPSEA builds environmental economics (EE) research capacity, encourages regional collaboration, and promotes EE relevance in its member countries (i.e., Cambodia, China, Indonesia, Lao PDR, Malaysia, Myanmar, Papua New Guinea, the Philippines, Thailand, and Vietnam). It provides: a) research grants; b) increased access to useful knowledge and information through regionally-known resource persons and up-to-date literature; c) opportunities to attend relevant learning and knowledge events; and d) opportunities for publication.

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ACKNOWLEDGMENTS

This study was supported by the Economy and Environment Program for Southeast Asia (EEPSEA) and the Erasmus Mundus Boku project under a PhD scholarship (141210–EM-1-2008-AT- ERAMUNDUS-ECW-L14). I would like to express my deep gratitude to Dr. Herminia Francisco, the Director of EEPSEA; Dr. David James, EEPSEA resource person; and Ms. Catherine Ndiaye, EEPSEA grant assistant, for their continuous support.

My special thanks goes to my supervisors: Prof. Ekko C. van Ierland and Dr. Xueqin Zhu, from the Environmental Economics and Natural Resources Group, Wageningen University, and Prof. Justus Wesseler from the Agricultural and Food Economics Group, Technische Universität München, for their contributions to the study. Additional thanks go to Dr. Jan Peter Lesschen from Team Soil Quality and Nutrients, Alterra, Wageningen University and Research Centre, for his valuable advice on the issue of emissions.

I would like to acknowledge the great support I have received on several research issues from Dr.

Nguyen Van Ngai, Dr. Hoang Kim, Dr. Nguyen Ngoc Thuy, Dr. Bui Minh Tri, Dr. Vien Ngoc Nam and Mr. Truong Van Vinh, my colleagues at Nong Lam University. Particular thanks go to Dr. Ngo Thi Lam Giang, the Vice-director of the Research Institute for Oil and Oil plants; Dr. Nguyen Van Thanh, the director of Khe Sanh Rubber Company; Mr. Le Quoc Huy, the Vice-director of the Research Center for Forest Ecology and Environment; and Mr. Tran Cong Khanh and Mr. Vo Van Quang from the Hung Loc Agricultural Research Center for their valuable data, which was used in the study. Special thanks are sent to Pham Hong Loan, Dinh Nguyen Thuy Vi, Pham Duc Thang and Le Xuan Da, for sharing the highs and lows of surveying seven provinces of Vietnam.

All the viewpoints, analyses, and research results featured in this report come from the author and

do not necessarily reflect the views of EEPSEA. The author alone is responsible for any inaccuracies found in the report.

TABLE OF CONTENTS

EXECUTIVE SUMMARY 1

1.0 INTRODUCTION 2

1.1 Problem Description 2

1.2 Research Questions 3

1.3 Scientific Significance and Policy Relevance 3

1.4 Structure of the Report 3

2.0 BIOFUEL PRODUCTION IN VIETNAM 3

2.1 The Cassava-based Ethanol Industry in Vietnam 3

2.2 The Jatropha-based Biodiesel Industry in Vietnam 5

3.0 METHODOLOGY 7

3.1 Methodological Issues of LCA Applied to Biofuels 7

3.2 Data Collection 11

3.3 Energy Balance Analysis 11

3.4 GHG Balance Analysis 12

3.5 Cost-effectiveness Analysis 13

4.0 RESULTS AND DISCUSSION 15

4.1 Cassava-based Ethanol in Vietnam 15

4.2 Jatropha-based Biodiesel in Vietnam 20

4.3 Comparison between Cassava-based Ethanol and Jatropha-based Biodiesel 25

5.0 CONCLUSIONS 26

REFERENCES 27

APPENDICES 32

LIST OF TABLES

Table 1. Cassava-based ethanol companies in Vietnam 4

Table 2. Jatropha-based biodiesel companies in Vietnam 6

Table 3. Properties of gasoline, diesel, ethanol, biodiesel and blends 8

Table 4. Percentage change in fuel consumption of ethanol blends (E5 and E10) w.r.t. gasoline, and biodiesel blends (B5 and B10) w.r.t. diesel in terms of 1 km-1 or g kWh-1

8

Table 5. Fuel efficiency of ethanol component and biodiesel component in blends 9

Table 6. Transportation distances, truck capacities and diesel consumption 10

Table 7. Projection of area and LUC for the production of cassava and jatropha 13

Table 8. Scaled unit damage costs of non-GHG emissions for Vietnam 15

Table 9. Energy input of cassava-based ethanol production in Vietnam 16

Table 10. Energy balance of cassava-based ethanol in Vietnam 17

Table 11. GHG emissions from cassava-based ethanol production in Vietnam 18

Table 12. GHG balance of cassava-based ethanol in Vietnam 19

Table 13. Cost of ethanol production and utilization (VND MJ-1) 19

Table 14. Cost-effectiveness of ethanol and gasoline, at the discount rate of 4% 20

Table 15. Energy input of jatropha-based biodiesel production in Vietnam 21

Table 16. Energy balance of jatropha-based biodiesel in Vietnam 22

Table 17. GHG emissions from jatropha-based biodiesel production in Vietnam 23

Table 18. GHG balance of jatropha-based biodiesel in Vietnam 23

Table 19. Cost of biodiesel production and utilization (VND MJ-1) 24

Table 20. Cost-effectiveness of biodiesel and diesel, at a discount rate of 4% 24

Table 21. Summary of the energy input, GHG emission performance, and social cost of fuels 25

LIST OF FIGURES

Figure 1. Cassava areas and ethanol plants 4

Figure 2. Blending stations 4

Figure 3. Cassava-based ethanol conversion 5

Figure 4. Projected jatropha area, biodiesel plants 6

Figure 5. Blending stations 6

Figure 6. Jatropha-based biodiesel processing 7

Figure 7. Life cycle system of biofuel production and use 10

Figure 8. Energy input of ethanol production by inputs and by phases 16

Figure 9. Energy input of biodiesel production by inputs and by phases 21

1 Economy and Environment Program for Southeast Asia

BIOFUEL PRODUCTION IN VIETNAM: COST-EFFECTIVENESS, ENERGY AND GHG BALANCES

Loan T. Le

EXECUTIVE SUMMARY

Biofuel production has been promoted as a means of saving fossil fuels and reducing greenhouse gas (GHG) emissions. However, there are concerns about the potential of biofuel to improve energy efficiency and contribute to climate change mitigation. This paper investigates energy efficiency, GHG emission performance, and the cost-effectiveness of biofuel as energy for transportation. Energy and GHG balances are calculated for a functional unit of 1 km using life-cycle assessment and considering the effects of land use change (LUC) and managed soils in feedstock production.

The expansion of feedstock production would cause a LUC effect of shifting forest land (12% of the

area of cassava expansion), grassland (31%), and other cropland (57%) to cassava and require a total area of 94,086 ha for the Government of Vietnam’s (GoV) targeted ethanol volume by 2025. This corresponds to 19% of the cassava area or 1.5% of Vietnam’s arable land in 2009. Jatropha production would cause a LUC effect of shifting forest land (15.32% of the jatropha area), grassland (78.92%), and other cropland (5.76%) to jatropha and require a total area of 245,859 ha for the GoV’s target biodiesel volume by 2025. This is equivalent to 5.94% of Vietnam’s unused land area in 2009.

For ethanol, the results show that the energy input for, and GHG emissions from, ethanol

production are 0.93 megajoule (MJ)input MJoutput-1 and 34.95 gram carbon dioxide equivalent per MJ (g CO2e

MJoutput-1), respectively. The ethanol substitution for gasoline in the form of E5 and E10 would achieve energy

savings, provided that their fuel consumption (in terms of l km-1) compared to gasoline does not increase by more than 2.4% and 4.5%, respectively. This substitution achieves a GHG emission saving, provided that the fuel consumption of E5 and E10 compared to gasoline does not increase by more than 3.8% and 7.8%, respectively. Cost-effectiveness analysis shows that the substitution of ethanol for gasoline is cost-effective.

For biodiesel, the analysis of energy balance shows that the energy input of biodiesel is 0.45 MJinput

MJoutput-1 and that the substitution of biodiesel for diesel in the form of B5 and B10 would achieve an energy

saving, provided that their fuel consumption compared to diesel does not increase by more than 0.66% and 4.1%, respectively. For the GoV’s target volume of 237.5 thousand tons (Tt) of biodiesel use by 2025, the corresponding energy savings would reach 1.41 and 4.23 PJ, a contribution of 0.29–0.87% of fuel consumption in the transport sector in 2009, provided that fuel consumption of blends B5 and B10 are equal to diesel. The production and utilization of biodiesel always achieves a GHG emission saving. As for the GoV’s target volume of 237.5 Tt of biodiesel by 2025, the GHG emission saving would be 806–1,183 Tt CO2e, a reduction of 2.24–3.28% of the emissions from fuel production and combustion in the transportation sector in 2009.

Compared to biodiesel, the production and use of ethanol demonstrates higher cost-effectiveness,

energy efficiency, and acceptable GHG emissions performance. These results contribute to the existing literature on energy and GHG balance accounting and confirm its possibilities for energy efficiency and GHG emission savings of ethanol substitution for gasoline in the forms of E5 and E10, and biodiesel substitution for diesel in the forms of B5 and B10 (with their current fuel consumption). These gains could be greater if there were to be further adaptation of vehicle engines alongside increased efficiencies on the part of cassava and ethanol producers. These results are relevant not only for sustainable biofuel development in Vietnam, but also for other Southeast Asian countries.

2 Biofuel Production in Vietnam: Cost-Effectiveness, Energy and GHG Balances

1.0 INTRODUCTION 1.1 Problem Description

Vietnam is currently a net energy exporter; however, it is projected to become a net energy importer after 2015 (APEC 2011; IEE 2009). Imported fuels have grown at an annual rate of 6.5% during 2000–2009 (APEC 2011). In addition, refined oil constituted the highest share of total energy consumption (42%) during the same period (APEC 2011).

Like most rapidly developing countries, the transport sector’s contribution to total energy

consumption and CO2 emissions in Vietnam is increasing (Schipper et al. 2009; Timilsina and Shrestha 2009; MONRE 2010; UN 2011). The sector’s energy consumption has increased at an annual rate of 12% and on average accounted for 24% of total energy consumption throughout 2000–2009 (APEC 2011). The sector’s energy consumption is projected to grow at an annual rate of 6.4% for the period of 2010–2020 and is expected to account for 22% of total energy demand by 2020 (Pham et al. 2010). Such growth in fossil fuel consumption results in corresponding increases in CO2 emissions, contributing to climate change through the greenhouse effect. In Vietnam, fossil fuel-related CO2 emissions from transport accounted for 25% of total CO2 emissions in 2008, and this figure is expected to increase to 35% and 37% by 2020 and 2030, respectively (MONRE 2010; USEIA 2011). Given these figures, the transport sector is a priority for reform and energy efficiency and cleaner fuels should be promoted in the context of climate change (Schipper et al. 2009; MONRE 2010; Leather 2009).

Biofuel production is supported by the Government of Vietnam (GoV) under Decision No.

177/2007/QD-TTg, 2007. The decision sets out a development strategy until 2015 and a broader vision toward 2025. It disseminates the GoV’s incentives for biofuel investment such as research and development (R&D) projects, tax exemptions, and a 20-year land-use right.

The policy has focused on two biofuels: cassava-based ethanol and jatropha-based biodiesel. E5 is

5% ethanol (E100) blended with 95% gasoline, and B5 is 5% biodiesel (B100) blended with 95% diesel. These blends are currently used in domestic transport (2010–2015) but it is proposed that E10 and B10 should be used after 2015 (GoV 2010).

The biofuel output targets are 250 thousand tons per year, which is equivalent to 1% of projected

total fuel demand by 2015; and 1.8 million tons per year, which is equivalent to 5% of projected total fuel demand by 2025 (GoV 2007). Accordingly, as of 2010, four ethanol processing plants have opened in Phu Tho, Quang Nam, Quang Ngai, and Binh Phuoc provinces (GoV 2010), with an annual capacity of 420 million liters of E100. Biodiesel production began in Vietnam in 2008 with jatropha cultivation and processing at seven biodiesel plants in Son La, Lang Son, Quang Tri, Binh Thuan provinces and in the cities of Ha Noi and Ho Chi Minh.

Although there are a range of feedstocks available, cassava and jatropha are the most promising for

application in Vietnam (MOIT 2008; MOIT 2009). Cassava is market-driven based on its availability, while jatropha is strategic in GoV planning owing to three drivers (MOIT 2008; MOIT 2009; MARD 2008; MARD 2010). First, it is a substitute for diesel, which contributed to 46% of imported fuels and grew at an annual rate of 5.2% during 2000–2009. Second, jatropha is an environmentally-friendly plant; it grows on marginal degraded soil, helps to control soil erosion, improves water infiltration, and reclaims fallow land by recycling nutrients from seedcake as fertilizer (MARD 2008; Cushion et al. 2010; FAO 2008). Jatropha is proposed as an effective treatment for a 7.6 million-hectare area affected by desertification, which is threatening the livelihoods of 22 million people (MARD 2008; UNCCD 2007). Third, as a non-edible feedstock, jatropha places less pressure on food security and creates jobs, agricultural market diversification, and potential earnings from carbon emission reductions (CERs). Despite these advantages, studies reveal growing concern about the economic viability of jatropha-based biodiesel (Wang et al. 2011; Ariza-Montobbio and Lele 2010; Wahl et al. 2009).

Biofuel substitution has, in one respect, been recommended in literature and promoted under the

GoV’s policies. However, there are some uncertainties and concerns about its energy efficiency, GHG emission savings, and economic viability (Schipper et al. 2009; Leather 2009; Baumuller 2010). Energy and GHG balances are used to measure biofuel energy efficiency and GHG emission performance by comparing


energy and GHG emissions associated with the inputs for ethanol production and utilization with those for the equivalent amount of gasoline. However, previous studies have not completely considered these two indicators (Reijnders 2011; Gnansounou et al. 2009). The GHG emissions associated with the effects of land use change (LUC) and managed soils in feedstock plantation are often missing (Reijnders 2011). The use of a functional unit (FU) in terms of mega joule (MJ) or liter in the comparison between fossil fuel and biofuel should be applied if biofuel is used in the form of heating energy or pure fuel (Gnansounou et al. 2009). A complete comparison of the cost-effectiveness of biofuel compared with fossil fuel is missing from literature if biofuel processing and external costs are not considered (Ariza-Montobbio and Lele 2010; Wahl et al. 2009).

1.2 Research Questions

Here are the research questions this report posed.

a) What are the energy balances of cassava-based ethanol and jatropha-based biodiesel in Vietnam?

b) What are the GHG balances of cassava-based ethanol and jatropha-based biodiesel in Vietnam?

c) What are the private, non-private and social costs of cassava-based ethanol and jatropha-based biodiesel in Vietnam?

d) What are the differences between these two biofuels considering economic viability, energy efficiency and GHG emission performance?

e) What are the policy implications for sustainable biofuel production in Vietnam? 1.3 Scientific Significance and Policy Relevance

Our research aims to fill the gaps in literature by investigating the energy and GHG balances of biofuel production and its utilization in the form of blends (E5 and E10, B5 and B10) as substitutes for fossil fuels. The research contributes to existing literature by considering the effects of LUC and managed soils in feedstock plantation, using an analysis based on a FU of 1 km of transportation by road vehicles, and a sensitivity analysis of the efficiency of ethanol (in E5 and E10) and biodiesel (in B5 and B10). The cost-effectiveness provides an economic assessment of biofuel production considering external costs and benefits, which are currently missing from the GoV’s scheme and literature (MOIT 2008; MOIT 2009). The results are crucial for ongoing issues and can be used to devise a proper scheme and structure of feedstock and biofuel development. Findings regarding the private and non-private costs of biofuel provide rationales to support the GoV. The comparison of biofuels and fossil fuels contributes to policies regarding the GoV’s environmental tax on fossil fuel and biofuel incentives (GoV 2007; MARD 2008; VNA 2010).

1.4 Structure of the Report The structure of the report is as follows. Section 2 gives an overview of biofuel production in

Vietnam. Section 3 presents the methodology applied in this study. The results and discussion are presented in Section 4 and Section 5 contains the conclusions of the research.

2.0 BIOFUEL PRODUCTION IN VIETNAM

2.1 The Cassava-based Ethanol Industry in Vietnam

Cassava-based ethanol production has rapidly developed under GoV policies. Up to 2010, eight plants had entered production (Figure 1), with a total annual capacity of 680 Ml of ethanol (Table 1), of which 420 Ml are used for biofuel, with the remainder given to the alcohol, cosmetic and pharmaceutical industries, and for export. Seven of the eight plants are located in the Central Highlands, South Central Coastal, and Southeast regions (Figure 1); these plants contributed 73% of total cassava output during 2005–


2009. Blending stations operated in four cities and 22 provinces in 2011 (Figure 2) and will be expanded across the whole country by 2012 (Petrovietnam 2011).

Figure 1. Cassava areas and ethanol plants Source: GSO 2010.

Figure 2. Blending stations Source: Survey (2011).

Table 1. Cassava-based ethanol companies in Vietnam

Company name Province Capacity (Ml y-1)

Cassava chip (t y-1)

1. Ethanol plants for biofuel 1.1. Phu Tho Bio-energy Co. Phu Tho 100 250,000 1.2. Dai Tan ethanol plant, Dong Xanh Co. Quang Nam 120 300,000 1.3. Petroleum Centre Zone Ethanol Joint Stock Co. (PCB) Quang Ngai 100 250,000 1.4. Orient Bio-Fuels Co. Binh Phuoc 100 250,000 2. Ethanol plants for other products 2.1 Ethanol DakLak Joint Stock Co. DakLak 66 165,000 2.2 Dai Viet Co. Dak Nong 68 170,000 2.3 Quy Nguyen Co. Binh Phuoc 50 125,000 2.4 Tung Lam Co. Dong Nai 76 190,000 Source: Company websites (2011).

2.1.1 Cassava-based ethanol production

Cassava-based ethanol production includes three phases: cassava production, ethanol conversion, and ethanol distribution and blending.

Cassava production. Cassava in Vietnam is cultivated in less developed provinces such as Gia Lai,

Tay Ninh, Kon Tum, Binh Thuan, Binh Phuoc, DakLak, Dong Nai, and DakNong. Farmers cultivate cassava at the beginning of the rainy season and harvest after 7–10 months. The farmers prepare the land using tractors and do stem cutting, hoeing and seeding by hand. They cultivate the cassava under rain-fed conditions and apply synthetic and organic fertilizers, and low levels of disease control. Weeding and harvesting are done manually. After the harvest, cassava is sliced and dried in the sun before delivery to ethanol plants in the form of dried chips. The conversion ratio from fresh root to dried chips is 2.4; this figure


was derived from our survey (see Section 3.2 for survey details) and verified by other studies (Le 2010; Nguyen et al. 2007a, b; Leng et al. 2008).

Ethanol conversion. There are four sub-processes required to convert dried chips to ethanol

(Figure 3): i) milling; ii) liquefaction; iii) saccharification and fermentation; and iv) distillation and dehydration. Besides ethanol, by-products include dried distillers grains (DDG), which is sold for animal feed production; biogas, which is used as a supplemental heat energy; and CO2, which is collected for sale. The conversion ratio between dried chips and ethanol applied in this study is 2.6 kg l-1; this figure was derived from our survey of ethanol plants in Vietnam and verified by other studies (Le 2010; Nguyen et al. 2007a, b; Leng et al. 2008).

Figure 3. Cassava-based ethanol conversion

Source: Survey (2011).

Ethanol distribution and blending. The ethanol is sold to the oil company and delivered to blending stations by trucks. At the blending stations, the tank blending process uses pumping machines to deliver gasoline and ethanol into one tank and to perform recirculation within this storage tank. Blends are then transported to gas stations for domestic consumption. 2.2 The Jatropha-based Biodiesel Industry in Vietnam

Jatropha is a strategic feedstock for biodiesel development in the four regions of the Northwest, Northeast, North Central Coast and South Central Coast in Vietnam (MARD 2008; MARD 2010). Jatropha plantations were started in 2008 by companies working in cooperation with local farmers and reached an area of 3,359 ha in 2010 (MARD 2010).

In the first three years of planting, jatropha was cautiously developed and the best varieties,

technology, soil suitability, and mechanisms for working in collaboration with farmers were sought. In coming years, the area dedicated to jatropha is projected to rise (Figure 4); this is according to the Forest Science Institute of Vietnam (FSIV) (MARD 2010), the Ministry of Agriculture and Rural Development (MARD) (Le 2011), and the findings of our 2011 survey. The jatropha seed market has gradually developed alongside biodiesel plant procurement.

Using the GoV projects, research on varieties, farming techniques and soil suitability were

conducted by the FSIV, Thanh Tay University, and the Research Institute for Oil and Oil Plants (IOOP). Biodiesel processing experiments were conducted by the IOOP, the Institute of Tropical Biology (ITB), and by processing plants. In the private sector, seven companies are investing in biodiesel and most of them are Vietnamese (except for the Eco-Energy Joint Stock Company, which takes 67% of its investment from Eco-carbone, which is headquartered in Paris) (Table 2). Apart from Dai Dong and Khe Sanh Rubber in Ha Noi and Ho Chi Minh, the other investors are located in jatropha-supplying provinces.

Dried chips

Milling Liquefaction Saccharification and fermentation

Distillation and dehydration

CO2

Ethanol

DDG

Biogas


Figure 4. Projected jatropha area, biodiesel plants Sources: MARD 2008; MARD 2010; Le 2011; Survey (2011).

Figure 5. Blending stations Source: Survey (2011).

Table 2. Jatropha-based biodiesel companies in Vietnam

Company name City/Province 1. Green Energy Biomass (GVB) Joint Stock Co. Binh Thuan 2. Nui Dau Co. Lang Son 3. Eco-Energy Joint Stock Co. Binh Thuan 4. Lung Lo Co. Quang Tri 5. Dai Dong Co. Ha Noi 6. Natural Energy Technology & Development Joint Stock Co. Son La 7. Khe Sanh Rubber Co. Ho Chi Minh Source: Company websites (2011).

2.2.1 Jatropha-based biodiesel production

The jatropha-based biodiesel production, distribution and blending process includes three phases:

jatropha production, biodiesel processing, and biodiesel distribution and blending. Jatropha production. Jatropha plantations have been developed mostly in unused barren land in

the North and Coastal regions (Figure 4). Data on plantations was obtained from FSIV’s experiments and cross-checked with farmers’ performance and available literature (Le 2011; FACT 2010; Achten et al. 2008; Almeida 2011; Ou et al. 2009; Lam et al. 2009; Ndong et al. 2009).

Jatropha is planted at the beginning of the rainy season from seedlings that are propagated by

biodiesel plants. Farmers use tractors to prepare the land and they hoe, plant, apply fertilizers and pesticides, weed, prune, harvest, and husk seeds by hand. The crop density is 1600 trees ha-1. Irrigation is applied for three months during the dry season. Fertilizers are used at the rate of 1.6 t of farmyard manure (FYM) and 160 kg NPK (with ratio of 16:16:8) ha-1y-1, with an additional basal fertilizer of 3.2 t of FYM and 160 kg of NPK ha-

1 in the first year (Le 2011; FACT 2010; Achten et al. 2008; Ou et al. 2009). Fruits are manually harvested, dried under the sun, and husked to obtain seeds for delivery to biodiesel plants. Under these irrigation and fertilization experiment conditions, our study applied a projected seed yield of 4.5 t ha-1, a figure which has been verified by other studies (Le 2011; Almeida et al. 2011; Ou et al. 2009; Lam et al. 2009; Ndong et al. 2009).


Jatropha-based biodiesel production. Data on biodiesel processing was obtained from experiments conducted by biodiesel plants, the IOOP, and the ITB, and cross-checked with other studies (Wang et al. 2011; FACT 2010; Almeida et al. 2011; Prueksakorn and Gheewala 2008). Mechanical extraction is used in Vietnam. The average seed oil content is 0.35, the extraction and transesterification efficiencies are 0.8 and 0.95, respectively.

Figure 6. Jatropha-based biodiesel processing Source: Survey (2011).

Biodiesel blending and distribution. The biodiesel is sold to oil companies and delivered to blending stations by truck (Figure 5). At blending stations, the tank blending process uses pumping machines to deliver diesel and biodiesel into one tank and perform recirculation within storage tanks. The blends (B5 or B10) are then transported to gas stations for domestic consumption.

3.0 METHODOLOGY

3.1 Methodological Issues of LCA Applied to Biofuels The analysis of the energy and GHG balances in this paper follows the LCA and comparative analysis

suggested by Gnansounou et al. (2009). In the comparative analysis, energy and GHG balances are the differences between the energy for and the GHG emissions from the production and utilization of biofuel (ethanol, biodiesel) and those of fossil fuel (gasoline, diesel) for the same functional unit (FU).

3.1.1 Functional unit and sensitivity analysis For the comparison between biofuels and fossil fuels, the FU is considered in terms of either MJ,

based on the lower heating values (LHVs) of fuels (Henke et al. 2005; Almeida et al. 2011; Ndong et al. 2009), or liter, based on the efficiency of blends (Nguyen et al. 2007b; Lechón et al. 2009). These FUs are pertinent if the biofuels are used in the form of heating energy or pure fuel (e.g., E100), but are not pertinent in the case of blends (Gnansounou et al. 2009).

Following Gnansounou et al. (2009), this study applies the FU of traveling 1 km using gasoline or

ethanol as the energy for road vehicles. In order to compare the life-cycle energy and GHG emissions of ethanol to those of gasoline, this study separates the fuel efficiency (MJ km-1) of ethanol (E100) from the obtainable efficiencies of gasoline and E5 and E10. The study assumes that the efficiency of the gasoline component in the blends is the same as its own, and that the efficiency of ethanol is explained by its contribution to the blends after deducting that of the gasoline component (Gnansounou et al. 2009). The same approach is applied to the comparisons between biodiesel and diesel.

Dried seeds

Extraction & refining Seed cake

Oil

Compost

Transesterification Biodiesel

Glycerin


This study focuses on the blends E5 and E10 for ethanol and B5 and B10 for biodiesel, which are promoted by the GoV (2007 and 2010). Table 3 shows the properties of fuels as a base from which to convert from fuel consumption (l km-1) to the efficiency indicator (MJ km-1). Table 4 shows the available results of vehicle tests concerning the fuel consumption of E5 and E10 with respect to (w.r.t.) gasoline, and of B5 and B10 w.r.t. diesel. Using these results, for ethanol it is argued that the lower LHVs of blends cause higher fuel consumption, while their higher octane values and compression ratios improve thermodynamic properties, and thus may reduce fuel consumption (Gnansounou et al. 2009; Nguyen et al. 2007b; Le et al. 2009; Eyidogan et al. 2010; Ozsezen and Canakci 2011). For biodiesel, the higher fuel consumption of blends is explained by lower calorific value and higher viscosity, causing lower atomization and combustion properties (Devendra and Mukhtar 2011; Jindal et al. 2010; Agarwal 2007). In reality, fuel efficiency is affected not only by fuel properties but also by other factors such as vehicle speed and gear, vehicle model, and road conditions. Table 3. Properties of gasoline, diesel, ethanol, biodiesel and blends

Properties Unit Gasoline Ethanol E5 E10 Diesel Biodiesel B5 B10 Density g l-1 743.00 790.00 745.40 747.70 832.00 879.00 834.35 836.70

LHV MJ l-1 32.17 21.10 31.62 31.06 35.87 32.64 35.71 35.55 Note: The properties of blends are calculated from those of gasoline and ethanol for E5 and E10, and from diesel and biodiesel for B5 and B10 according to the volume shares. Source: Hoang et al. 2010. Table 4. Percentage change in fuel consumption of ethanol blends (E5 and E10) w.r.t. gasoline, and biodiesel blends (B5 and B10) w.r.t. diesel in terms of 1 km-1 or g kWh-1

Blends For E5 For E10 Source Fuel consumption indicators l km-1 g kWh-1 l km-1 g kWh-1

Vehicle Ford Laser Ghia 1.8 -5.182 -4.19 Le et al. (2009)

Honda Super Dream 100 cc -6.37 -5.41 Le et al. (2009)

1.4i SI engine 5.203 2.80-0.204 5.503 3.60-1.504 Ozsezen and Canakci (2011), Eyidogan et al. (2010)

Ford Focus -1.205 Gnansounou et al. (2009)

Renault Megane -0.60 Delgado (2003)

Various car models -5.635 Gnansounou et al. (2009), Reading et al. (2004)

Toyota 1.6 L/2000 1.13 Nguyen et al. (2007b)

XU7JP/L3 engine 5.07 Roayaei and Taheri (2009)

Blends For B5 (l km-1) For B10 (l km-1) Source

Vehicle Single cylinder engine 0 0 Shrake et al. (2010)

Ford Focus 1.8 Tdi 90 VC 0.2963 0.6111 Lechón et al. (2009)

Ford Mondeo XLD 418 TCI 7.7494 Lin et al. (2008) Notes: (1) 2A minus sign means a lower fuel consumption of blends w.r.t. gasoline. (2) 3Ozsezen and Canakci 2011. (3) 4Eyidogan et al. 2010. These two values are measured at the vehicle speed of 80 km h-1 and 100 km h-1, respectively. (4) 5These are average values calculated from the figures presented in Gnansounou et al. (2009).

For this reason, a sensitivity analysis is conducted in this study to evaluate the effects of different blends and their fuel consumption variation w.r.t. fossil fuels and to provide a general assessment of the energy and GHG balances of ethanol and biodiesel. The percentage change in fuel consumption of ethanol blends w.r.t. gasoline is considered at three levels, formulating six scenarios: S1, S2, and S3 represent E5 with 5% higher, the same, and 5% lower levels of fuel consumption, respectively; and S4, S5, and S6 represent E10 with 5% higher, the same, and 5% lower levels of fuel consumption, respectively. The percentage change in fuel consumption of biodiesel blends w.r.t. diesel is considered at two levels, represented by four scenarios: S7 and S8 are B5 with the same and 5% higher levels of fuel consumption, respectively; and S9 and S10 are B10 with the same and 5% higher levels of fuel consumption, respectively. The efficiency of the ethanol and biodiesel components in blends is separated in Table 5. Accordingly, energy use and GHG emissions are


calculated in terms of MJinput km-1 and g CO2e km-1, and are compared to the equivalent values of fossil fuels in terms of energy and GHG balances.

Table 5. Fuel efficiency of ethanol component and biodiesel component in blends

Indicator Blend Gasoline component Ethanol component

l km-1 MJ km-1 km MJ-1 MJgasoline MJblend-1

(%) km MJ-1 MJethanol MJblend

-1 (%)

km MJ-1 MJ km-1

Gasoline 0.07973 2.56401 0.3900 100 0.3900 0 0 0 S1 (E5, +5%) 0.08375 2.6459 0.3779 96.66 0.3900 3.34 0.0283 35.32 S2 (E5, 0%) 0.07976 2.5199 0.3968 96.66 0.3900 3.34 0.5946 1.68 S3 (E5, -5%) 0.07577 2.3939 0.4177 96.66 0.3900 3.34 1.2206 0.82 S4 (E10, +5%) 0.08375 2.5996 0.3847 93.21 0.3900 6.79 0.3115 3.21 S5 (E10, 0%) 0.07966 2.4758 0.4039 93.21 0.3900 6.79 0.5946 1.68 S6 (E10, -5%) 0.07577 2.3520 0.4252 93.21 0.3900 6.79 0.9076 1.10

Diesel component Biodiesel component Diesel 0.05402 1.93704 0.5299 100 0.5299 0 0 0

S7 (B5, 0%) 0.05408 1.9283 0.5186 95.43 0.5299 4.57 0.57 1.76 S8 (B5,+5%) 0.05679 2.0247 0.4939 95.43 0.5299 4.57 0.03 37.01 S9 (B10,0%) 0.05408 1.9195 0.5209 90.82 0.5299 9.18 0.57 1.76 S10 (B10,+5%) 0.05679 2.0155 0.4962 90.82 0.5299 9.18 0.30 3.36 Notes: (1) 1Gnansounou et al. (2009). (2) 2 Lechón et al. (2009). (3) 3This figure is calculated from 2.56 MJ km-1 and the LHV of gasoline. (4) 4This figure is calculated from 0.054 l km-1 and the LHV of diesel. (5) 5-7These figures equal fuel consumption of gasoline multiplied by 1.05, 1, and 0.95 for 5, 6 and 7, respectively. (6) 8-9These figures equal fuel consumption of diesel multiplied by 1 and 1.05, respectively.

3.1.2 Description of the system The LCA is used in this study with a focus on estimating the energy input of and GHG emissions

from the production and utilization of ethanol and biodiesel, and comparing these results with those of gasoline and diesel, respectively, for the same FU. Therefore, a well-to-wheel LCA is chosen since the utilization phase is significantly affected by fuel consumption and GHG emissions from combustion. Figure 7 shows the life cycle systems of the production and use of ethanol and biodiesel.

To calculate the energy input and GHG emissions from the four phases shown in Figure 7 for a FU, I first calculated the energy use and GHG emissions for a MJoutput of ethanol and biodiesel, expressed as MJinput MJoutput

-1 and g CO2e MJoutput-1, respectively. These results were then multiplied by the MJoutput of ethanol and

biodiesel for a FU at each blend and efficiency level. The energy input and GHG emissions associated with transportation were calculated from

transportation distances, truck capacity, and diesel consumption (Table 6). There are three stages of transportation: 1) transporting feedstocks (dried chips from cassava areas to ethanol plants or jatropha seed to biodiesel plants); 2) transporting biofuels (ethanol or biodiesel) from processing plants to blending stations; and 3) transporting blends from blending stations to gas stations. To calculate the amount of diesel used in transportation, three averaged national distances, different truck capacities, and figures for diesel consumption were needed. Each national distance is the average of three regional distances with the weights of corresponding capacities of processing plants.

For the feedstock production phase, the energy use and GHG emissions were first calculated for 1

hectare and then converted to MJinput MJoutput-1 and g CO2e MJoutput

-1 of ethanol using the projected average yield of 33.09 t ha-1 of cassava (Table 7), the conversion ratio from fresh root to ethanol (kg l-1), and the LHV of ethanol (MJoutput l

-1). For the ethanol conversion, distribution and blending phases, energy use and GHG emissions were calculated for 1 liter and converted to MJoutput using the LHV of ethanol. For biodiesel, the dried seed yield of jatropha is 4.5 t ha-1, seed oil content is 0.35, and the extraction and transesterification efficiencies are 0.8 and 0.95, respectively, for the conversion of energy and GHG emissions into MJoutput (Le 2011).


Figure 7. Life cycle system of biofuel production and use

Table 6. Transportation distances, truck capacities and diesel consumption

Items Regional distance (km) by region

Vietnam North Central South

Ethanol processing capacity (%) 23.8 52.4 23.8 100 Dried chip 100 100 100 100 Ethanol 120 180 200 170 Ethanol blends 50 50 50 50 Biodiesel processing capacity (%) 45 15 40 100 Dried seed 100 130 170 133 Biodiesel 200 150 100 153 Biodiesel blends 50 50 50 50

Items National distance (km) Truck capacity

(l truck-1) Diesel consumption

(l km-1) Ethanol Biodiesel Feedstock 100 133 15,0001 0.35 Biofuel 170 153 16,000 0.35 Blends 50 50 16,000 0.35 Note: 1kg truck-1 Source: Survey (2011).

Direct and indirect energy

GHG emissions

Fertilizers (N, P2O5, K2O)

Pesticides

Diesel

Labor

Cassava/Jatropha production in the fields

Use of fertilizers and pesticides Urea application Diesel for operating tractors and

transportation Burning of jatropha residue Managed soils (nitrogen use, crop

residue) C stock change caused by land-use

change

CO2e emissions from use of fertilizers and pesticide

CO2 emissions from urea application CO2e emissions from diesel

combustion CH4, N2O emissions from residue

burning N2O emissions from managed soils CO2 emissions from C stock change

Diesel

Chemicals (NaOH, Methanol)

Electricity from the grids

Biodiesel processing Extraction and refining

Transesterification Ethanol conversion

Milling Liquefaction

Saccharification & fermentation Distillation & dehydration

CO2e emissions from diesel combustion

CO2e emissions from chemical use CO2e emissions from electricity

consumption CO2e emissions from digester,

incomplete combustion of flaring biogas, and coal combustion

CO2 collected for ethanol (a GHG emission saving)

Transportation of blends

Transportation of ethanol

Transportation of dried chips

Fuel combustion

CO2e emissions from diesel combustion

CO2e emissions from electricity consumption

Electricity the from grids

Diesel

Diesel CO2e emissions from diesel combustion

Zero emissions from biofuel combustion

Biofuel production

Distribution and blending


3.2 Data Collection

Except for secondary data cited from literature, the primary data was collected via two surveys. The first survey was conducted during the cassava-harvesting season, from January to April, in 2011. I selected four of the top ten cassava-producing provinces (i.e., Binh Phuoc, Tay Ninh, Dong Nai and DakNong), which serve four of the eight ethanol plants. We interviewed 102 farmers, 8 managers in ethanol plants, 32 stakeholders in the supply chain (i.e., starch processors, input and/or agricultural service suppliers, and laborers), and 24 key informants to obtain data on: 1) farm inputs; 2) on-site conversion ratios of fresh roots to dried chips and to ethanol; 3) ethanol conversion inputs; 4) LUC estimation; and other information.

In the second survey, we interviewed farmers, managers in biodiesel plants, seed collectors and

transporters, and key informants in provinces to obtain data on: 1) farm inputs; 2) the oil content of jatropha seed, the efficiency levels of oil extraction and biodiesel transesterification; 3) biodiesel production inputs and by-products; and 4) general information on jatropha plantation and LUC estimation. We chose the provinces of Binh Thuan, Ninh Thuan, and Dong Nai for the survey and interviewed key informants in 16 jatropha-planting provinces.

3.3 Energy Balance Analysis

The energy balance compares the energy input for the production and utilization of ethanol (or biodiesel) with that of gasoline (or diesel) for the same FU. The energy input is calculated at the primary energy level using the energy input efficiencies from GREET (2011) and Biograce (2011). The energy inputs of gasoline and diesel are 1.1375 and 1.1368 MJinput MJoutput

-1, respectively (GREET 2011). For the energy input of ethanol, I calculated both direct and indirect energy inputs (Figure 7). The

former includes diesel for operating tractors and transportation, coal for ethanol conversion, and electricity for ethanol conversion and blending. The latter is embodied in other inputs including fertilizers, pesticides, labor, chemicals, plant construction, machines and vehicles. Labor in farming is converted into energy using the “Total Food Consumed” method, with a ratio of 2.3 MJ per hour (MJ h-1) (Nguyen et al. 2007a; Silalertruksa et al. 2009; Dai et al. 2006; Ozkan et al. 2004; Romanelli and Milan 2005). The indirect energy inputs of plant construction, machines and vehicles are not considered in this study due to the lack of data and the fact that these amounts are assumed to be trivial (Wang et al. 2011; Nguyen et al. 2007b; Dai et al. 2006).

In addition to these parameters, I collected the energy inputs for ethanol production from the first

survey. For the cassava production phase, 81 days per hectare (8 hours per day) are spent on land clearance, stem-cutting, planting, fertilizer application, weeding, harvesting, slicing, and sun-drying. The average amount of fertilizer applied per hectare is 58 kg N, 47 kg K2O, 53 kg P2O5, and 5 tons of FYM, with the average amount of pesticides at 0.23 kg ha-1 (Appendix 1). The amount of diesel used to operate tractors is 15 l/ ha.

For the ethanol conversion phase, the amounts of electricity, coal, and chemicals per liter of ethanol

are calculated from the total amounts for the production capacity of 100 million liters per year. For the distribution and blending phase, the amount of electricity used in pumping is calculated based on a 7.5-horsepower engine with a capacity of 60,000 l/ hour. The amount of diesel used for transportation is presented in Section 3.1.2.

Concerning the energy input of biodiesel, the jatropha production, the amount of diesel used by

tractors is 15 l/ha. Farmers use 20 days, 15 days, and 15 days per hectare on land clearance, planting, fertilizer application, weeding, and pruning in years 1, 2 and 3, respectively. In years 4–16, farmers use 12 days per hectare on these activities. The working day for picking, de-husking, and drying is calculated from a seed capacity of 30 kg/day. For biodiesel processing, the amount of electricity used to produce 1 liter of biodiesel is taken from the experiments conducted by the ITB and by biodiesel plants. For the blending phase, the amount of electricity used in pumping is the same as for ethanol.


3.4 GHG Balance Analysis

The GHG balance compares the GHG emissions from the production and utilization of ethanol and biodiesel with that of gasoline and diesel, respectively, for the same FU. The three GHGs (i.e., carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O)) are aggregated to give the CO2 equivalent (CO2e) using the global warning potential (GWP) factors of 1 for CO2, 25 for CH4, and 298 for N2O (Biograce 2011; IPCC 2006). GHG emissions from the production and combustion of fossil fuel are 83.8 g CO2e MJ-1 (Biograce 2011; European Parliament 2009). GHG emissions from ethanol combustion are zero, according to the Renewable Energy Directive (RED) (European Parliament 2009). GHG emissions from ethanol production are calculated using guidelines from the IPCC, RED, and the Biograce project (Biograce 2011; IPCC 2006; European Parliament 2009; EU 2010). GHG emissions from plant construction and the production of equipment and vehicles for transportation are not taken into account in the cut-off criteria suggested in Ou et al. (2009), Ndong et al. (2009) and Lechón et al. (2009). A detailed explanation of the GHG emission calculations and parameters is presented in Appendix 2. The GHG emission sources of each phase are listed in Figure 7.

In the cassava production phase, seven emission sources are considered, namely: fertilizer use,

pesticide use, diesel consumption, urea application, burning of cassava residue, N2O emissions from managed soils, and carbon stock (CS) change caused by LUC (Appendix 2). The first three emission sources are calculated by multiplying the emission factors (EFs) and the corresponding amounts of these inputs per liter of ethanol or biodiesel. The GHG emissions from manure application are taken to be zero (Biograce 2011). The fourth emission source is calculated from the amount of urea per hectare of cassava or jatropha.

For the emissions created by burning cassava residue, the weight of cassava residue is estimated

from the harvest index reported by Hoang et al. (2010). A total of 79% of cassava residue is burned, and 21% is returned to the soil (based on 2011 survey). For N2O emissions from managed soils, the EFs for N2O-N from IPCC (2006) and the amounts of nitrogen in 1) organic fertilizers, 2) synthetic fertilizers, and 3) cassava (or jatropha) residue are needed. The amount of nitrogen in organic fertilizers is calculated by multiplying the average amount of manure and its nitrogen content of 0.0032 (Le et al. 2009). The nitrogen content of cassava residue is 0.015 (Howeler 2010). The nitrogen content in dry matter (DM) of jatropha leaves and foliage are 0.0177 and 0.005, respectively (UNFCCC 2011). For the N2O emissions from managed soils, the amount of nitrogen associated with the loss of soil carbon stock (SOC) due to LUC is calculated from the SOC change, percentages of LUC, and carbon nitrogen ratios (Appendix 3). The same approach is applied to jatropha-based biodiesel. The jatropha area is calculated from the seed yield and the biodiesel conversion ratio. The weight of DM of fruit husks, leaves, pruned foliage and roots are reported by Nallathambi (2009) and Reinhardt et al. (2008). The carbon content of these jatropha components are collected from existing literature (Reinhardt et al. 2007; Firdaus et al. 2010; Kratzeisen and Müller 2009). Jatropha residue ratios actually burned or returned to soil are 40% and 60%, respectively (based on 2011 survey).

Concerning LUC due to the expansion of cassava production, the ethanol industry could use cassava from newly cultivated areas and other sources. With a focus on the impact of GHG emissions from cassava cultivation, this study assumes that cassava for biofuel feedstock comes wholly from new domestic cultivation as a result of LUC. Table 7 shows the projected area for cassava cultivation, needed for the GoV’s target ethanol volume of 600 Tt by 2025, and an estimation of LUC from the 14 leading cassava-producing provinces, amounting to a contribution of 66% to the total cassava area in 2005–2009 (Appendix 4). The survey indicated that 88% of newly-cultivated cassava would be expanded as a mono-crop, and the remaining 12% as an intercrop with other perennial crops such as rubber or cashew. To meet the GoV’s target ethanol volume of 600 Tt, a cassava area of 94,086 ha needs to be cultivated by 2025, which is equivalent to 19% of the cassava area or 1.5% of Vietnam’s arable land in 2009 (GSO 2010). A similar calculation is applied for jatropha, and the percentages of LUC forest land, grassland, and annual cropland to jatropha are 15.32%, 78.92%, and 5.76%, respectively, all derived from GoV policy and from our survey across 16 jatropha-producing provinces (MARD 2008, 2010; Le 2011). The GoV’s target biodiesel volume of 237.5 Tt requires 245,859 ha of jatropha by 2025, equivalent to 5.94% of the unused land area in Vietnam in 2009 (GSO 2010).


Table 7. Projection of area and LUC for the production of cassava and jatropha

For cassava production: - Initial production in 2009 103 t 2 - Cassava yield in 2010 t ha-1 17.17 (UNCCD) - Targeted volume in 2025 103t 600 (GoV) - Annual growth rate of cassava yield % y-1 7.20 (UNCCD)

- Projected average cassava yield 2010–2025 t ha-1 33.091

Total Forest land Grassland2 Annual

cropland Perennial cropland

Mono-cropped cassava (%) 87.83 11.68 31.15 26.09 18.92 Inter-cropped cassava (%) 12.17 - - 2.46 9.71

% of total 100.00 11.68 31.15 28.55 28.63 ha in 2025 94,086 10,989 29,305 26,856 26,936

For jatropha production: - Initial production in 2009 103 t 2.86 - Projected average jatropha yield 2010–2025 t ha-1 4.5 (Le 2011) - Targeted volume in 2025 103 t 237.50

Total Forest land Grassland (UNCCD)

Annual cropland

% of total 100.00 15.32 78.92 5.76 ha in 2025 245,859 37,666 194,032 14,161 Note: (1) 1The projected average cassava yield from 2010–2025 is calculated from the annual growth rate of cassava yield, technological growth, and the weight of planted areas. (2) 2Barren land and denuded hills in Vietnam are classified as grassland. Source: GoV 2007; UNCCD 2007; Le 2011; Survey (2011).

For emissions from CS change caused by LUC, the CS is the sum of SOC and the vegetation carbon

stock (CVEG), reflecting change in plant carbon sequestration (Appendix 3). The CO2 emissions from CS change are calculated by multiplying the CS change and corresponding percentages of LUC (Appendix 4).

Figures for the GHG emissions produced by the ethanol conversion process (using an anaerobic

digester) were collected from the project design document published by the UNFCCC (2011) and were used to calculate the emissions from chemical use. Advances in technology allow ethanol plants to collect CO2 from the fermentation process and sell it to the food or chemical industries for further use. Therefore, the eventual GHG effect is the GHG emission minus the amount of CO2 collected for sale (Appendix 2.2). Biodiesel processing incurs emissions from the electricity used in the extraction and refining of jatropha oil and biodiesel transesterification. In the distribution and blending phase, GHG emissions from electricity use and diesel combustion for transportation are calculated by multiplying the EFs of electricity and diesel and their amounts mentioned in Section 3.3 (Appendix 2.3).

3.5 Cost-effectiveness Analysis In this study, the cost-effectiveness analysis aims to compare alternative fuels (ethanol with

gasoline, biodiesel with diesel) in terms of the social costs of production and utilization for a FU. To calculate the social cost for a FU, the social cost of 1 MJ of fuel (VND MJ-1) is first calculated and then multiplied by the amount of MJ needed for a FU (MJ km-1) in each scenario in Table 5.

3.5.1 Break-even price calculation The social costs of fuels are calculated as the break-even price, which is identified by setting the net

present values of fuel projects equal to zero at a given discount rate. These break-even prices are the average costs for every MJ of fuel produced and used. This study follows Kovacevic and Wesseler (2010) by considering both private and non-private costs and benefits in the social cost calculation.


The net present value (NPV) can be calculated as follows:

NPV= �pF (t)

qt

T

t=0− ��

C(t) − B(t)qt

T

t=0�,

or

NPV= p�F (t)qt

T

t=0− ��

C(t)-B(t)qt

T

t=0� because p is constant.

By setting NPV equal to zero we obtain

p= �∑ C(t)-B(t)

qtTt=0 �

∑ F (t)

qtTt=0

,

where p is the break-even price or the average cost for 1 MJ of fuel produced and used; C(t) is annual cost of biofuel production at year t; B(t) is annual benefit of by-products; F(t) is annual fuel production in terms of MJ; q-t is discount factor with q = 1+ i, and i is the discount rate; and T is time frame of the project.

The time frame is 16 years (2010–2025), with ethanol and biodiesel production volumes in 2025 at

600 Tt and 237.5 Tt, respectively, derived from GoV policies (GoV 2007; MARD 2008, 2010). The biofuel production volume in year t is calculated using the initial output of 2009 and the target volume for 2025, assuming a straight-line increase in outputs. Three discount rates of 4%, 8%, and 10% are considered. The price factor is constant, using the prices of 2010 during the time frame period.

3.5.2 Private costs Private production cost of gasoline and diesel. For the cost-effectiveness analysis, the private costs

of gasoline and diesel are the prices at gas stations in 2010, exclusive of all distorting factors particular to Vietnam, including tariffs, taxes, fees, and stabilization fund contribution. As Vietnam currently imports 70% of fuel, private costs were calculated by adding the imported price and transportation costs (from the dock, to the warehouse, to the gas station).

Private production cost of ethanol and biodiesel. The private production costs of ethanol (or

biodiesel) include cassava (or jatropha) production costs, the cost of ethanol (or biodiesel) processing, and the cost of electricity and diesel for distribution and blending. First, cassava (or jatropha) production costs are composed of the land opportunity cost, comprising rental costs, the cost of seedlings, fertilizers, pesticides, diesel for the tractor and water pumping machine, maintenance, labor, and seed transportation. Second, the ethanol (or biodiesel) processing costs include the capital cost, the cost of power (electricity), labor, water and chemicals. Revenues derived from the by-products of CO2, cassava stillage for ethanol and those of glycerine and compost for biodiesel, are included in the private cost with negative value as benefits in biofuel production. Third, the cost of electricity and diesel for distribution and blending equal the two energies multiplied by their prices in 2010.

3.5.3 External costs and benefits The externalities are from three sources: 1) GHG emissions from biofuel production; 2) non-GHG

emissions from fuel combustion; and 3) the energy security (supply) of fossil fuels (for gasoline and diesel). GHG emissions and non-GHG emissions. The external cost of GHG emissions is calculated from the

amount of CO2e emissions and the global external cost of CO2e emissions of 25€ t-1 (Maibach et al. 2008). Non-GHG emissions of HC, NOx, PM, and SO2 from fuel combustion are considered in this study. The external costs equal the amount of emissions multiplied by scaled unit damage costs. The emission amounts are estimated from the GoV’s regulations for 2010–2025 on the basis of European standards. The scaled unit damage costs are first derived from the external damage costs reported by CASES (2008) for 2010–2025 at the 2000 price. They are then adjusted to the 2010 price using the Harmonised Indices of Consumer Prices


(HICPs) reported by Eurostat. In order to apply these unit damage costs to Vietnam, these costs need to be adjusted to reflect differences in 1) the willingness-to-pay or damage valuation, and 2) the physical damage scale per ton of pollutants (Nguyen 2008). These scaling factors are measured using ratios of GDPs (PPP) per capita and population density between the EU and Vietnam (Table 8). The costs in Euro are multiplied by the 2010 exchange rate to obtain values in VND.

Table 8. Scaled unit damage costs of non-GHG emissions for Vietnam

Pollutants External damage costs

for EU (€) Scaling factors

External damage costs for Vietnam (€)

2010 2025 Valuation Physical 2010 2025 HC 908 517 0.092 2.244 237 116

NOx 6,165 6,653 0.092 2.244 1,609 1,518 PM 22,947 21,068 0.092 2.244 5,990 4,844 SO2 5,934 6,058 0.092 2.244 1,549 1,387

Note: For the EU and Vietnam in 2010, GDPs (PPP) per capita are USD 33,729 and USD 3,104 and population densities are 116.00 and 260.32 persons per km2, respectively. Source: CASES 2008.

Energy security. Security of energy supply is a priority and a motivation for the development of biofuel in most countries (APEC 2011; Maibach et al. 2008; CEC 2007). Energy security is defined as supply reliability at affordable prices (Greene 2007). The external cost of the security of fossil fuel supply was 0.0236 € l-1 in 2004, estimated by Leiby (2007) as the incremental benefits to society of the reduction of fossil fuel imports. In the European Community (EC) biofuels progress report (CEC 2007), energy security is considered to be a benefit of biofuel and is estimated as the reduction of oil stocks facilitated by biofuel substitution. In other estimations (Edwards et al. 2008), biofuels retain a security of supply benefit of 0.11-0.13 € l-1 (2006) based on the cost of keeping stocks for an expected period, and the cost of the time needed to start up a biofuel program without subsidies. Greene and Ahmad (2005) estimated the cost of oil dependency at 0.128 € l-1. In summary, the cost of energy security (via different formulas) has been estimated as ranging from 0.0236 to 0.13 € l-1. In this study, I considered the external cost of energy security of supply, starting from the lowest estimation in the literature, and used an estimation of 0.0236 € l-1 in my calculation. This value is adjusted to the 2010 price using the HICPs reported by Eurostat and multiplied by the exchange rate in 2010 to obtain the value in VND (2010).

4.0 RESULTS AND DISCUSSION

4.1 Cassava-based Ethanol in Vietnam 4.1.1 Energy balance analysis Energy input of cassava-based ethanol production Direct energy inputs include coal, electricity and diesel; indirect energy inputs are chemicals,

fertilizers, pesticides, and farm labor (Figure 8). The LHVs and energy input efficiencies are presented in Table 9. Biogas by-product in the form of methane is used in the conversion process itself. In terms of energy inputs, coal is the most important, accounting for 80.99% of total energy. Indirect inputs in farming contribute the lowest portions of 1.37% for labor and 3.78% for fertilizers and pesticides. Electricity accounts for 8.17% of total energy and is mostly used in the conversion of the ethanol. The contribution of diesel is 4.97%, of which 89% is for transportation and 11% is for operating tractors.

Regarding the energy allocation of the three phases, ethanol conversion is the most energy-

consuming phase, amounting to 89.77% of total energy. Cassava production accounts for 8.13% of total energy. The distribution and blending phase incurs 2.10% of total energy, mostly for transportation. The


total energy input is 19.71 MJ l-1 or 0.9342 MJinput MJoutput-1, showing its feasibility compared to fossil fuel

when energy input efficiency is considered.

Figure 8. Energy input of ethanol production by inputs and by phases

Source: Authors’ calculations.

Table 9. Energy input of cassava-based ethanol production in Vietnam

Inputs Unit Energy content

(MJ unit-1)

Energy input efficiency

(MJinputMJoutput-1

or MJinput unit-1)*

Input (Unit l-1)

Energy input

MJ l-1 %

Cassava production 1.60 8.13 Fertilizers and pesticides 0.75 N kg 48.992 0.0104 0.51 P2O5 kg 15.232 0.0085 0.13 K2O kg 9.682 0.0096 0.09 Pesticides kg 268.402 0.00004 0.01 Labor h 2.303 0.1175 0.27 Diesel for operating tractors liters 35.871 1.144 0.0027 0.11 Diesel for transportation liters 35.871 1.144 0.0117 0.48 Ethanol conversion 17.69 89.77 Chemicals 0.14 NaOH kg 10.222 0.0030 0.03 Urea kg 22.784 0.0030 0.07 DAP kg 8.605 0.0030 0.03 Enzyme kg 15.005 0.0011 0.02 Electricity kWh 3.601 1.576 0.2810 1.59 Coal kg 24.441 1.092 0.6000 15.96 Distribution and blending 0.41 2.10 Diesel for transportation liter 35.871 1.144 0.0096 0.39 Electricity for blending kWh 3.601 1.576 0.0037 0.02

Total 19.71 100 Notes: *MJinputMJoutput

-1 for direct energy inputs and MJinput unit-1 for indirect energy inputs. Source: (1) 1Davis et al. 2010. (2) 2Biograce 2011. (3) 3Nguyen et al. 2007a; Silalertruksa et al. 2009; Dai et al. 2006; Ozkan et al. 2004; Romanelli and Milan 2005. (4) 4GREET 2011. (5) 5Nielsen et al. 2007. (6) 6APEC 2011, and author’s calculations.

3.78% 1.37%

0.72% 4.97% 8.17%

80.99%

Energy by inputs

Coal

Electricity

Diesel

Chemicals

Labor

Fertilizers and pesticides

8.13%

89.77%

2.10%

Energy by phases


Ethanol conversion

Cassava production


Energy balance of cassava-based ethanol

Different blends and their fuel consumption result in different energy balances in terms of MJ km-1 and percentages (Table 10). Except for S1 and S4, other scenarios achieve an energy saving. For instance, the substitution of ethanol for gasoline in the form of E5 would save 1.35 MJ or 46.14% of primary energy input for every kilometer in scenario S2. In scenarios S1 and S4, with 5% higher fuel consumption of E5 and E10 w.r.t. gasoline, the energy balances are positive, meaning that the substitution of ethanol for gasoline would not save energy input. Seeking break-even points, a zero energy balance is found at 2.4% and 4.5% higher fuel consumption of E5 and E10 w.r.t. gasoline, respectively. This means that the substitution of ethanol for gasoline in the forms of E5 or E10 would achieve an energy saving, provided that the fuel consumption of E5 and E10, in terms of l km-1 compared to gasoline, does not increase by more than 2.4% and 4.5%, respectively.

Table 10. Energy balance of cassava-based ethanol in Vietnam

MJinput MJoutput-1 MJoutput km-1

MJinput km-1 Energy balance Ethanol Gasoline MJ km-1 %

(1) (2) (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=((5):(4))x100 Gasoline 1.1375 2.56 2.9166 Ethanol

S1 (E5, +5%) 0.9342 35.32 32.99 2.9166 30.07 1,031.12 S2 (E5, 0%) 0.9342 1.68 1.57 2.9166 -1.35 -46.14 S3 (E5, -5%) 0.9342 0.82 0.77 2.9166 -2.15 -73.76 S4 (E10, +5%) 0.9342 3.21 3.00 2.9166 0.08 2.83 S5 (E10, 0%) 0.9342 1.68 1.57 2.9166 -1.35 -46.14 S6 (E10, -5%) 0.9342 1.10 1.03 2.9166 -1.89 -64.71

Note: A minus sign means an energy saving. Source: Author’s calculations.

In the aggregate, every liter of ethanol produced and used would save 17, 55, 17, and 36 MJ of

primary fossil energy input for S2, S3, S5, and S6, respectively (Appendix 5). These savings are equivalent to the energy inputs needed to produce 0.46, 1.51, 0.46, and 0.99 liters of gasoline, respectively. As for the GoV’s target volume of 600 Tt by 2025, the corresponding energy savings would reach 12.82, 42.08, 12.82, and 27.45 PJ for S2, S3, S5, and S6, respectively. These savings are respectively equivalent amounts of energy inputs to produce 350, 1,150, 350, and 750 Ml of gasoline, contributing to 2.62–8.60% of fuel consumption in the transport sector in 2009.

The ethanol plants are located around cassava areas to minimize transportation distances, and

almost all the ethanol plants use biogas by-products to supplement energy in the conversion process. Opportunities for reducing energy input lie in the improvement of cassava yield, more sustainable cultivation (shifting from chemical to organic fertilizers), a higher energy-efficient substitute for coal, and shorter distances, with more blending and gas stations. In addition to the improved efficiency efforts of cassava producers and the ethanol industry, the energy balance could be improved through further adaptation of vehicle engines.

4.1.2 GHG balance analysis

GHG emissions from cassava-based ethanol production The literature mentions a lack of explicit attention to the effects of LUC and managed soils in

feedstock plantations on GHG emissions, causing increasing doubt regarding the GHG emission savings attributed to biofuels (Schipper et al. 2009; Leather 2009; Reijnders 2011). These effects are considered in this study following guidelines from the IPCC, RED, and the Biograce project (Silalertruksa et al. 2009; IPCC 2006; European Parliament 2009; EU 2010).

Emission factors (EFs) are listed in Table 11. Using ethanol would result in GHG emissions of 738 g

CO2e l-1 or 34.95 g CO2e MJoutput-1. The GHG emissions from cassava production of 1,068 g CO2e l-1 are derived


from seven sources. The emissions from carbon stock change caused by LUC and the N2O emissions from managed soil account for 66% and 15% of emissions, respectively. GHG emissions from the use of fertilizers (including urea application) and pesticides almost equal that of burning cassava residue, each accounting for 7% of emissions from cassava production. GHG emissions from diesel consumption contribute 4%, of which 80% is derived from the transportation of dried chips, and 20% from operating tractors.

Table 11. GHG emissions from cassava-based ethanol production in Vietnam

Emission sources Unit GHG emission

coefficient (g CO2e unit-1)

Input (Unit l-1)

Emissions (g CO2e l-1)

Emissions from cassava production 1,067.73 Fertilizers and pesticides 75.54

N kg 5,880.601 0.0104 61.43 P 2 O5 kg 1,010.701 0.0085 8.56 K 2O kg 576.101 0.0096 5.55 Pesticides kg 10,971.301 0.00004 0.46

Diesel consumption 43.24 Diesel for operating tractors liter 3,005.911 0.0027 8.17 Diesel for transportation of dried chips liter 3,005.911 0.0117 35.07

Urea ha 37,227.05 0.0002 6.75 Emissions from burning cassava residue ha 415,826.09 0.0002 75.39 N2O emissions from managed soil ha 886,552.24 0.0002 160.73

Direct emissions ha 689,456.71 0.0002 125.00 Indirect emissions ha 197,095.53 0.0002 35.73

Annualized emissions from carbon stock changes caused by LUC ha 3,892,012.58 0.0002 705.62 Emissions from ethanol conversion -361.26 CO2 collected -490.00 Chemicals 16.57

DAP kg 1,527.001 0.0030 4.59 Urea kg 3,167.001 0.0030 9.52 Enzyme kg 1,000.002 0.0011 1.05 NaOH kg 469.301 0.0030 1.41

Other emissions 112.17 Emissions from distribution and blending 31.07 Electricity for blending kWh 565.201 0.0037 2.08 Diesel for transportation liter 3,005.911 0.0075 28.99

Total 737.55 Note: A minus sign means a GHG emission saving. Sources: (1) 1Biograce 2011. (2) 2Nielsen et al. 2007, and author’s calculations.

During ethanol conversion (via anaerobic digester) the ethanol plants emit GHGs of 129 g CO2e l-1,

of which 112 g CO2e l-1 comes from the ethanol conversion facilities, e.g., CH4 emissions from the anaerobic digester and the incomplete combustion of flaring biogas, CO2e emissions from electricity consumption, and the combustion of coal (UNFCCC 2011). The remaining 17 g CO2e l-1 is attributed to the use of chemicals. However, 490 g CO2e l-1 is collected from the fermentation process and sold to other industries for further use. The eventual effect is a net GHG emission saving of 361 g CO2e l-1. The distribution and blending phase contributes a small amount of 31 g CO2e l-1.

GHG balance of cassava-based ethanol All the scenarios result in a GHG emission saving except for S1 (Table 12). Different blends and fuel

consumptions have different GHG balances in terms of g CO2e km-1 and percentages. For example, the substitution of ethanol for gasoline in the form of E5 would save 156 CO2e or 72.64% of GHG emissions for every kilometer in S2. In S1, with 5% higher fuel consumption of E5 w.r.t. gasoline, the GHG balance is positive; this means that the substitution would cause an increase in GHG emissions. Looking at break-even points, a zero GHG balance is found at 3.8% and 7.8% higher fuel consumption of E5 and E10 w.r.t. gasoline, respectively. This means that the substitution of ethanol for gasoline in the form of E5 and E10 would


achieve a GHG emission saving, provided that consumption of E5 and E10, in terms of l km-1 compared to gasoline, does not increase by more than 3.8% and 7.8%, respectively.

In the aggregate, every liter of ethanol produced and used would result in a GHG emission saving of

675–4,796 g CO2e in S2–S6 (Appendix 5). Every hectare of cassava for ethanol as a substitute fuel would result in a GHG emission saving of 3.72–26.45 t CO2e. With a current ethanol substitution capacity of 420 Ml y-1, Vietnam would achieve a GHG emission saving of 284–2,014 Tt CO2e y-1. As for the GoV’s target volume of 600 Tt by 2025, GHG emission savings would reach 512–3,643 Tt CO2e, reducing 1.42–10.11% of the emissions from fuel production and combustion in the transportation sector in 2009. Opportunities for further GHG emission savings lie in improved agricultural practices; in particular, more cassava residue should be returned to the soil and biomass burning should be avoided. In addition, intensive cassava cultivation should be encouraged alongside sustainable land management to minimize the effects of LUC, and fertilizer with lower levels of nitrogen should be applied in order to reduce N20 emissions.

Table 12. GHG balance of cassava-based ethanol in Vietnam

g CO2e MJoutput-1 MJoutput km-1

g CO2e km-1 GHG balance Ethanol Gasoline g CO2e km-1 %

(1) (2) (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=((5):(4))x100 Gasoline 83.80 2.56 214.86 Ethanol

S1 (E5, +5%) 34.95 35.32 1,234.46 214.86 1,019.60 474.53 S2 (E5, 0%) 34.95 1.68 58.78 214.86 -156.08 -72.64 S3 (E5, -5%) 34.95 0.82 28.64 214.86 -186.22 -86.67 S4 (E10, +5%) 34.95 3.21 112.22 214.86 -102.64 -47.77 S5 (E10, 0%) 34.95 1.68 58.78 214.86 -156.08 -72.64 S6 (E10, -5%) 34.95 1.10 38.51 214.86 -176.35 -82.08

Note: A minus sign means a GHG emission saving. Source: Author’s calculations.

4.1.3 Cost-effectiveness analysis

Private costs, external costs, and social costs of ethanol production and utilization

Table 13 summarizes the social cost and its components for ethanol and gasoline. For gasoline, the

private cost is calculated from the import price (exclusive of any distorting factors) plus the cost of domestic transport from the dock to warehouses, then on to gas stations. For ethanol, the cost of cassava production amounts to 54.87–57.82% of the private cost, the cost of ethanol conversion accounts for 39.58–42.59%, and the cost of distribution and blending contributes 2.54–2.60%. The private cost of ethanol is 355–399 VND MJ-1, which is 18–33% higher than that of gasoline.

Table 13. Cost of ethanol production and utilization (VND MJ-1)

Ethanol Gasoline

4% 8% 10% 4% 8% 10% Social cost 380.58 411.06 426.82 377.43 377.67 377.79 Private cost 354.82 383.88 398.91 300.75 300.85 300.91 Cassava production 205.16 214.26 218.88 Ethanol conversion 140.44 159.81 169.89 Distribution and blending 9.22 9.82 10.14 External cost 25.76 27.18 27.90 76.68 76.81 76.88

GHG emissions 23.33 24.66 25.34 52.62 52.62 52.62 Non-GHG emissions 2.42 2.52 2.57 3.00 3.14 3.21 Security of supply N/A N/A N/A 21.05 21.05 21.05

Note: N/A means ‘not applicable’. Source: Author’s calculations.


The external costs are categorized according to three kinds of externalities: GHG emissions, non-GHG emissions, and the energy security (supply) of fossil fuel. The external cost of energy security incurred by gasoline is implicitly an external benefit to ethanol in a comparative analysis. The external cost of gasoline is 2.76–2.98 times higher than that of ethanol owing to its higher cost components. Overall, the social cost of ethanol is found to be 8.34–12.98% higher than that of gasoline. These cost components are considered in terms of per MJ. However, this is just one input of the complete cost-effectiveness analysis of a functional unit (VND km-1) when considering the fuel efficiency of substituting ethanol for gasoline.

Cost-effectiveness of gasoline and ethanol With the inclusion of fuel efficiency, the substitution of ethanol for gasoline is found to be cost-

effective except in scenarios S1 and S4 (Table 14 and Appendix 6). For instance, the substitution of ethanol for gasoline in the form of E5 would save VND 327.70, or 33.86% of the social cost for every kilometer in scenario S2. In scenarios S1 and S4, with 5% higher fuel consumption of E5 and E10 w.r.t. gasoline, the cost differences are positive, meaning that the substitution of ethanol for gasoline would not be economical or cost-effective. Table 14. Cost-effectiveness of ethanol and gasoline, at the discount rate of 4%

VND MJoutput-1 MJoutput

km-1 VND km-1 Cost difference

Ethanol Gasoline VND km-1 % (1) (2) (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=(5)x100/(4)

Gasoline 377.43 2.564 967.73 Ethanol

S1 (E5, +5%) 380.58 35.32 13,440.49 967.73 12,472.76 1,288.87 S2 (E5, 0%) 380.58 1.68 640.02 967.73 -327.70 -33.86 S3 (E5, -5%) 380.58 0.82 311.81 967.73 -655.92 -67.78 S4 (E10, +5%) 380.58 3.21 1,221.86 967.73 254.14 26.26 S5 (E10, 0%) 380.58 1.68 640.02 967.73 -327.70 -33.86 S6 (E10, -5%) 380.58 1.10 419.33 967.73 -548.40 -56.67

Note: A minus sign means cost-effectiveness. Source: Author’s calculations.

Looking at break-even points, a zero cost difference is found at 1.72% and 3.51% higher fuel

consumption of E5 and E10 w.r.t. gasoline, respectively, at the discount rate of 4%, provided that other factors are constant. This means that the substitution of ethanol for gasoline in the form of E5 or E10 would be cost-effective, provided that the fuel consumption of E5 and E10, in terms of l km-1 compared to gasoline, does not increase by more than 1.72% and 3.51%, respectively. These two figures for E5 and E10 are respectively 1.45% and 2.95% at a discount rate of 8%, and 1.31% and 2.66% at a discount rate of 10%.

In the aggregate, every kilometer traveled which substitutes ethanol for gasoline would save VND

250–619 for S2, S3, S5, and S6, respectively (Appendix 5) or every liter of ethanol produced and consumed as fuel in the forms of E5 and E10 could save VND 3,148–15,941 in these four scenarios. As for the GoV’s target volume of 600 Tt by 2025, corresponding savings would reach VND 2,391–12,107 billion for S2, S3, S5, and S6. 4.2 Jatropha-based Biodiesel in Vietnam

4.2.1 Energy balance analysis Energy inputs of jatropha-based biodiesel production

Jatropha-based direct energy inputs include electricity and diesel. Indirect inputs are chemicals,

fertilizers, pesticides, and farm labor (Figure 9). Chemicals are the most important energy input, accounting for 51.24% of total energy. Indirect inputs from farming contribute 15.81% for labor and 13.28% for fertilizers


and pesticides. Electricity accounts for 6.09% of total energy and is mostly used in processing the biodiesel. Diesel contributes 13.58% of energy input, 60% of which is used in transportation and 40% for operating tractors (Table 15).

With regard to energy allocation during production, biodiesel processing is the most energy-

consuming phase, amounting to 57.20% of total energy used (Figure 9). Jatropha production accounts for 40.21% of total energy. The distribution and blending production phases incur 2.59% of total energy, mostly for transportation (Table 15). The total energy input is 14.65 MJ l-1 or 0.4488 MJinput MJoutput

-1, showing its feasibility compared to fossil fuel with reference to energy input efficiency.

Figure 9. Energy input of biodiesel production by inputs and by phases

Source: Author’s calculations. Table 15. Energy input of jatropha-based biodiesel production in Vietnam

Unit Energy content

(MJ unit-1)

Energy input efficiencies

(MJinputMJoutput-1

or MJinput unit-1)*

Input (Unit l-1)

Energy inputs

MJ l-1 %

Jatropha production 5.89 40.21 Fertilizers and pesticides 1.95

N kg 48.992 0.02803 1.37 P2O5 kg 15.232 0.02803 0.43 K2O kg 9.682 0.01401 0.14 Pesticides kg 268.402 0.00004 0.01

Labor h 2.303 1.00732 2.32 Diesel for farming liter 35.871 1.144 0.01951 0.80 Diesel for seed transportation liter 35.871 1.144 0.02043 0.83 Biodiesel processing 8.38 57.20 Chemical 7.51

NaOH kg 10.222 0.01 0.09 Methanol kg 33.022 0.22 7.42

Electricity kWh 3.601 1.576 0.15 0.87 Distribution and blending 0.38 2.59 Electricity for dispensing kWh 3.601 1.576 0.0037 0.02 Diesel for transportation liter 35.871 1.144 0.0089 0.36

Total 14.65 100 Notes: * MJinputMJoutput

-1 for direct energy inputs and MJinput unit-1 for indirect energy inputs. Sources: (1) 1Davis et al. 2010.

(2) 2Biograce 2011. (3) 3Nguyen et al. 2007a; Silalertruksa et al. 2009; Dai et al. 2006; Ozkan et al. 2004; Romanelli and Milan 2005. (4) 4GREET 2011. (5) 5Nielsen et al. 2007. (6) 6APEC 2011, and author’s calculation.

13.28%

15.81%

51.24%

13.58% 6.09%

Energy by inputs

Electricity

Diesel

Chemicals

Labor

Fertilizers and pesticides 40.21%

57.20%

2.59%

Energy by phases


Biodiesel processing

Jatropha production


Energy balance of jatropha-based biodiesel

Concerning variations in fuel consumption and in different blends, except for scenario S8, all the other scenarios achieve an energy saving (Table 16). For example, biodiesel substitution for diesel in the form of B5 would save 1.41 MJ or 64.06% of primary energy input for every kilometer in scenario S7. In scenario S8, with 5% higher fuel consumption of B5 w.r.t. diesel, the energy balances are positive, meaning that the substitution would not save energy input. The break-even points (with a zero energy balance) are found at 3.31% and 6.85% higher fuel consumption of B5 and B10 compared to diesel, respectively. This means that biodiesel substitution for diesel in the form of B5 or B10 would achieve an energy saving provided that the fuel consumption of B5 and B10, in terms of l km-1 compared to diesel, does not exceed the consumption of diesel by more than 3.31% and 6.85%, respectively.

Almost all of the biodiesel plants are located in jatropha-producing areas in order to minimize

transportation distances. Further opportunities for reducing energy inputs lie in the improvement of jatropha yield (nearly half of total energy is attributed to this phase), more sustainable cultivation (shifting from chemical to organic fertilizers), an improvement in the use of chemicals in biodiesel processing, and a reduction in the use of human labor in dried seed production by using mechanical husking. The energy balance could also be further improved by adapting vehicle engines to biodiesel use.

Table 16. Energy balance of jatropha-based biodiesel in Vietnam

Note: A minus sign means an energy saving. Source: Author’s calculations.

4.2.2 GHG balance analysis GHG emissions from jatropha-based biodiesel

Biodiesel production would result in a GHG emission saving of 2,143 g CO2e l-1 or 65.65 g CO2e

MJoutput-1 (Table 17). This ultimate GHG emission saving comes from the jatropha production phase, with a

GHG emission saving from the LUC effect of 3,069. This amount is reconciled with other emissions in the jatropha production phase consisting of 202 g CO2e l-1 for the use of fertilizers and pesticides, 120 g CO2e l-

1 for diesel consumption due to farming activities and 133 g CO2e l-1 for emissions from burning jatropha residue and managed soils. The combined effect of these jatropha production phases achieves a GHG emission saving of 2,615 g CO2e l-1.

In the biodiesel processing phase the extraction and transesterification processes give GHG

emissions of 444 g CO2e l-1, of which 80.40% can be attributed to the use of chemicals and 19.60% from the consumption of electricity. The distribution and blending phases contribute a small amount of 28.72 g CO2e l-1.

MJinput MJoutput-

1 MJoutput km-1

MJinput km-1 Energy balance Biodiesel Diesel MJ km-1 %

(1) (2) (3)=(1)x(2) for biodiesel

(4)=(1)x(2) for diesel (5)=(3)-(4) (6)=((5):(4))x100

Diesel 1.1368 1.9370 2.2019 Biodiesel

S7 (B5, 0%) 0.4490 1.7626 0.7913 2.2019 -1.41 -64.06 S8 (B5,+5%) 0.4490 37.0137 16.6176 2.2019 14.42 654.71 S9 (B10,0%) 0.4490 1.7626 0.7913 2.2019 -1.41 -64.06 S10 (B10,+5%) 0.4490 3.3649 1.5107 2.2019 -0.69 -31.39


Table 17. GHG emissions from jatropha-based biodiesel production in Vietnam

Unit GHG emission coefficient

(g CO2e unit-1) Input amount

(Unit l-1) g CO2e l-1

Emissions from jatropha production -2,615.45 Fertilizers and pesticides 202.07

N kg 5,880.601 0.0280 164.82 P2O5 kg 1,010.701 0.0280 28.33 K2O kg 576.101 0.0140 8.07 Pesticides kg 10,971.301 0.00004 0.43

Diesel consumption 120.05 Diesel for operating tractors liter 3,005.911 0.01951 58.63 Diesel for seed transportation liter 3005.911 0.02043 61.42

Emissions from burning jatropha residue ha 74,982.84 0.00097 72.72 N2O emissions from managed soils ha 61,938.08 0.00097 60.07

Direct emissions ha 34,589.42 0.00097 33.55 Indirect emissions ha 27,348.66 0.00097 26.52

Annualized emissions from carbon stock changes caused by LUC

ha -3,165,362.51 0.00097 -3,069.94

Emissions from biodiesel processing 443.82 Chemical 356.81

NaOH kg 469.301 0.00880 4.13 Methanol kg 1,569.731 0.22467 352.68

Electricity for processing kWh 565.201 0.15395 87.01 Emissions from distribution and blending 28.72 Electricity for blending kWh 565.201 0.003675 2.08 Diesel for transportation 26.64

B100 liter 3005.911 0.00667 20.06 Blends liter 3005.911 0.00219 6.58

Total -2,142.92 Note: A minus sign means a GHG emission saving. Sources: 1Biograce 2011 and author’s calculations.

GHG balance of jatropha-based biodiesel As the production and use of biodiesel produces a GHG emission saving, the substitution of

biodiesel for diesel achieves a GHG emission saving across all the scenarios (Table 18). However, its consumption should be considered alongside other aspects of the positive contribution of biodiesel to blends, energy efficiency, and cost-effectiveness.

Table 18. GHG balance of jatropha-based biodiesel in Vietnam

g CO2 e MJoutput

-1 MJoutput km-1 g CO2e km-1 GHG balance

Biodiesel Diesel g CO2e km-1 %

(1) (2) (3)=(1)x(2) for biodiesel

(4)=(1)x(2) for diesel (5)=(3)-(4) (6)=

((5):(4))x100 Diesel 83.80 1.9370 162.32 Biodiesel -65.65

S7 (B5, 0%) -65.65 1.7626 -115.72 162.32 -278.04 -171.29 S8 (B5,+5%) -65.65 37.0137 -2,430.07 162.32 -2,592.39 -1,597.10 S9 (B10,0%) -65.65 1.7626 -115.72 162.32 -278.04 -171.29 S10 (B10,+5%) -65.65 3.3649 -220.92 162.32 -383.23 -236.10

Note: A minus sign indicates a GHG emission saving. Source: Author’s calculations.


4.2.3 Cost-effectiveness analysis Private cost, external cost, and social cost of biodiesel production and utilization Table 19 shows that the social cost per MJ of biodiesel is much higher than that of diesel due to its

higher private cost component. The higher external costs related to emissions and security of supply lead to a higher external cost for diesel, while the positive effect of land use change (due to the planting of jatropha) achieves an emissions saving or an external benefit for biodiesel.

Even with this external benefit, the social cost of biodiesel is much higher than that of diesel. These

findings hold for the three discount rates and the differences are larger at the higher discount rates. For instance, the social cost of biodiesel is 52.4% higher than that of diesel at a discount rate of 10%.

Table 19. Cost of biodiesel production and utilization (VND MJ-1)

Biodiesel Diesel

4% 8% 10% 4% 8% 10% Social cost 514.37 540.92 554.75 363.00 363.63 363.96 Private cost 556.71 584.17 598.49 284.14 284.24 284.29

Jatropha production 541.58 550.21 554.93 Biodiesel processing 9.59 28.00 37.39 Distribution and blending 5.54 5.96 6.17

External cost -42.34 -43.24 -43.74 78.85 79.39 79.68 GHG (eqCO2) emissions -49.27 -50.61 -51.34 52.62 52.62 52.62 Non-GHG emissions 6.93 7.37 7.60 7.35 7.89 8.18 Security of supply N/A N/A N/A 18.88 18.88 18.88

Source: Author’s calculations. Note: N/A – not applicable.

In terms of a functional unit, biodiesel substitution for diesel is not cost-effective for all scenarios

(Table 20). At a discount rate of 4%, biodiesel substitution for diesel in the form of B5 would increase social costs to 203.5 VND km-1 or 29.0% of the social cost per functional unit compared to diesel (see columns 5 and 6 in Table 20) for scenario S7. Looking for break-even points, the zero cost difference is found at the 1.4% and 2.8% lower fuel consumption of B5 and B10, respectively, compared to diesel, provided that other factors are constant. This means that biodiesel substitution for diesel in the form of B5 or B10 would be cost-effective provided that the fuel consumption of B5 and B10, in terms of l km-1 compared to diesel, decreases by more than 1.4% and 2.8%, respectively, at the discount rate of 4%.

Table 20. Cost-effectiveness of biodiesel and diesel, at a discount rate of 4%

Scenarios VND MJoutput-1 MJoutput

km-1 VND km-1 Cost difference

Biodiesel Diesel VND km-1 % (1) (2)1 (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=(5)x100/(4)

Diesel 362.8 1.9 702.8 Biodiesel

S7 (B5, 0%) 514.2 1.8 906.3 702.8 203.52 29.0 S8 (B5,+5%) 514.2 37.0 19,032.2 702.8 18,329.53 2,608.2 S9 (B10,0%) 514.2 1.8 906.3 702.8 203.5 29.0 S10 (B10,+5%) 514.2 3.4 1,730.2 702.8 1,027.4 146.2

Notes: (1) 1See Table 5. (2) 2A plus sign means cost-ineffectiveness. (3) 3The high cost-ineffectiveness in S8 is due to the low contribution of biodiesel to B5. Source: Author’s calculations.

Similar results are found at the discount rates of 8% and 10% (Appendix 7). Biodiesel substitution for

diesel is not cost-effective for all scenarios. Considering the break-even points, the higher discount rate


requires lower fuel consumption of biodiesel blends in terms of l km-1 to achieve the cost-effectiveness of biodiesel substitution for diesel. For instance, fuel consumption of B5 and B10 compared to diesel should decrease by more than 1.4% and 2.8% compared to diesel, respectively, at the discount rate of 4%; these figures are respectively 1.9% and 3.7% at a discount rate of 10%. However, these fuel consumption levels for biodiesel blends have not been reached in reality.

4.3 Comparison between Cassava-based Ethanol and Jatropha-based Biodiesel

In terms of per MJ output, both biofuels have the advantage of energy input efficiency and GHG emission performance; however, they both incur higher social costs, especially biodiesel. In terms of a functional unit of 1 km, with current levels of vehicle engine development, the production and use of ethanol demonstrates higher advantages of cost-effectiveness and energy efficiency, and acceptable GHG emission performance compared to biodiesel. Table 21. Summary of the energy input, GHG emission performance, and social cost of fuels

Gasoline Diesel Ethanol Biodiesel Energy input (MJinput MJoutput

-1) 1.1375 1.1368 0.9342 0.4488 Emission performance (g CO2e MJoutput

-1) 83.8 83.8 34.95 -65.65 Social cost (VND MJoutput

-1) 377-378 363-364 381-427 514-555 Comparison between ethanol and biodiesel Energy balance

(MJ km-1) GHG balance (g CO2 km-1)

Cost difference (VND km-1)

Ethanol

In comparison with gasoline, the increase in fuel consumption of blends to achieve zero energy balance, zero GHG balance, or zero cost difference

2.4% for E5 and 4.5% for E10 3.8% for E5 and 7.8% for E10 1.31-1.72% for E5 and 2.66-3.51% for E10

S1 (E5, +5%) 30.07 1,019.60 12,472.76 S2 (E5, 0%) -1.35 -156.08* -327.70*** S3 (E5, -5%) -2.15 -186.22 -655.92 S4 (E10, +5%) 0.08 -102.64 254.14 S5 (E10, 0%) -1.35 -156.08 -327.70 S6 (E10, -5%) -1.89 -176.35 -548.40

Biodiesel

In comparison with diesel, the increase (+) or decrease (-) in fuel consumption of blends to achieve zero energy balance, zero GHG balance, or zero cost difference

3.31% for B5 and 6.85% for B10 Always achieve a negative GHG balance

-1.4 to -1.9% for B5 and -2.8 to -3.7% for B10

S7 (B5, 0%) -1.41** -278.04 203.5† S8 (B5,+5%) 14.42†† -2,592.39 18,329.5 S9 (B10,0%) -1.41** -278.04 203.5 S10 (B10,+5%) -0.69 -383.23 1,027.4 Notes: (1) * A minus sign means a GHG emission saving. (2) ** A minus sign means an energy saving. (3) *** A minus sign means cost-effectiveness. (4) † A plus sign means cost-ineffectiveness. (5) †† S8 appears a negative contribution of biodiesel to blend B5.


5.0 CONCLUSIONS This study aims to analyze the energy efficiency and GHG emission savings created by producing

and using biofuel as a substitute for fossil fuel. Expanding the use of cassava would need LUC to shift forest land (12% of the area of cassava expansion), grassland (31%), and other cropland (57%) to cassava and require a total area of 94,086 ha to achieve the GoV’s target for volume of ethanol by 2025. This corresponds to 19% of the cassava area or 1.5% of the arable land in 2009 in Vietnam. An increase in jatropha production would demand a shift in land use from forest land (15.32% of the jatropha area), grassland (78.92%), and other cropland (5.76%) to jatropha, requiring a total area of 245,859 ha to meet the GoV’s target for volume of biodiesel by 2025, which is equivalent to 5.94% of the unused land area in Vietnam in 2009.

The analysis of energy balance shows that the energy input of ethanol is 0.93 MJinput MJoutput

-1 and that the substitution of ethanol for gasoline in the forms of E5 and E10 would achieve an energy saving, as long as their fuel consumption compared to gasoline does not increase by more than 2.4% and 4.5%, respectively. As for the GoV’s target volume of 600 Tt by 2025, energy savings would reach 12.82–42.08 PJ, contributing to 2.62–8.60% of the fuel consumption of the transport sector in 2009, provided that the fuel consumption of E5 and E10 is equal to 5% or lower than that of gasoline. On the other hand, the energy input of biodiesel is 0.45 MJinput MJoutput

-1 and the substitution of biodiesel for diesel in the forms of B5 and B10 would achieve an energy saving, provided that their fuel consumption compared to diesel does not increase by more than 3.31% for B5 and 6.85% for B10.

The opportunities for energy efficiency improvement in biofuel processing lie in the use of a more

energy-efficient substitute for coal and chemicals, an improvement in feedstock yield, more sustainable cultivation with a reduction in chemical fertilizer use, and shorter transportation distances, with more blending and gas stations.

The analysis of GHG balance shows that ethanol production would result in GHG emissions of 738 g

CO2e l-1 and that the substitution of ethanol for gasoline in the forms of E5 and E10 would achieve a GHG emission saving, provided that their consumption compared to gasoline does not increase by more than 3.8% and 7.8%, respectively. With a current ethanol substitution capacity of 420 Ml y-1, Vietnam would achieve GHG emission savings of 284–2,014 Tt CO2e y-1 in every scenario except for scenario S1, with a 5% higher level of fuel consumption of E5 w.r.t. gasoline. With reference to the GoV’s target volume of 600 Tt by 2025, GHG emission savings would reach 512–3,643 Tt CO2e, a reduction of 1.42–10.11% of the emissions from fuel production and combustion generated by the transport sector in 2009. On the other hand, the production and use of biodiesel achieves a GHG emission saving in all scenarios used in this study.

Opportunities for further GHG emission savings are possible via an increase in the amount of

cassava residue returned to the soil and a decrease in its burning, and also via intensive cassava cultivation with sustainable land management and the application of fertilizers with lower levels of nitrogen. Comparing energy input for, and GHG emissions from, ethanol production between studies shows that variations are due to the coverage of the effects of LUC, managed soils, and CO2 absorption from cassava cultivation, as well as differences in cassava yields, energy efficiency in farming, and by-product analyses.

The results contribute to the existing literature on energy and GHG balance accounting and confirm

the possibilities for energy efficiency and GHG emission savings via ethanol substitution for gasoline in the forms of E5 and E10, and biodiesel substitution for diesel in the forms of B5 and B10 (with current levels of fuel consumption). These achievements could become even more significant if vehicle engines were to be adapted and if cassava and ethanol producers were to instigate further improvements in production.

However, the cost-effectiveness analysis shows cost-effectiveness for ethanol substitution, but cost-

ineffectiveness for biodiesel substitution. These results are relevant to the development of sustainable biofuel in Vietnam, and to the development of sustainable biofuel in other Southeast Asian countries.


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Appendix 1. Average amounts of fertilizers and pesticides

Figures for the average amounts of fertilizers per ha (58 kg N, 47 kg K2O, 53 kg P2O5, and 5 t of farmyard manure) and pesticides (0.23 kg ha-1) were collected from the survey and applied in this study for the project period of 2010–2025, assuming that the growth rate cassava yield keeps pace with technological growth.

This assumption is justified by the work of Agrifood Consulting International (ACI 2004) and Tran

(2011). A study by ACI attributed cassava yield improvement to many factors, however the contribution of genetic technology was found to be significant. The current variety of cassava is KM94, which was released in 1995; the adoption of advanced varieties will improve yield in coming years. In addition, a study by Tran (2011) has suggested new techniques to improve cassava yield, including i) maintaining soil fertility by rotating cassava with other crops, e.g. beans, ii) growing a living fence of Gliricidia sepium or Leucaena sp as a source of green manure from their leaves, iii) adjusting the seeding time, density and methods (from standing to lying), and iv) the application of organic fertilizers. Experiments regarding the adoption of technology show an increase in cassava yield alongside the same application of fertilizers.

Current adoption of cassava varieties in Vietnam (2008)

Variety Year of release Adoption rate (%) Yield (t ha-1)

Mean On farm trial KM98-5 2008 4.50 20.60 34.50 KM140 2007 5.40 20.00 35.00 KM98-1 2005 3.24 20.30 32.20 KM94 1995 75.54 16.90 33.00

SM937-26 1995 2.70 19.80 32.20 KM98-7 1998 1.44 17.00 31.60

HL23 1.08 13.50 16.50 XVP 2.70 12.00 15.10

Others 3.42 6.50 14.90 Source: Hoang et al. (2010). Evaluation of advanced varieties in Vietnam in 2009

Variety On farm trial yield (t ha-1) KM316 49.00 KM414 45.70 KM325 43.67 KM397 43.40 KM228 39.10 KM1401 39.20

HB60 38.73 KM7 38.67

KM419 37.72 Source: Hoang et al. (2010). Note: 1KM140 is a hybrid bred by crossing KM140 with KM1400.

http://www.fao.org/ag/AGP/agpc/doc/PUBLICAT/Gutt-shel/x5556e01.jpg

http://www.fao.org/ag/AGP/agpc/doc/PUBLICAT/Gutt-shel/x5556e01.jpg


Appendix 2. Calculation of GHG emissions in ethanol production and combustion1

E= Eec + El + Ep + Etd + Eu - Eccr - Esca - Eccs - Eee Eq. (SI.2)

E = Total emissions from production and utilization of cassava-based ethanol (g CO2e l-1); Eec = Emissions from cassava cultivation (g CO2e l-1);W El = Annualised emissions from carbon stock changes caused by LUC (g CO2e l-1); Ep = Emissions from ethanol conversion (g CO2e l-1); Etd = Emissions from transport and distribution (g CO2e l-1); Eu = Emissions from the fuel in use (g CO2e l-1); Eccr = Emission saving from carbon capture and replacement (g CO2e l-1); Esca = Emission saving from soil carbon accumulation via improved agricultural management (g CO2e l-1); Eccs = Emission saving from carbon capture and geological storage (g CO2e l-1); and Eee = Emission saving from excess electricity from cogeneration (g CO2e l-1).

Notes: Eu is zero for the ethanol component in fuel blends according to the Renewable Energy Directive (RED), Annex V [36.1]. Eccr is the amount of CO2 collected from the fermentation process and sold to food or chemical industries for further use. This amount is considered in the ethanol conversion phase (Ep). Esca, Eccs , Eee are not applicable in this study.

Appendix 2.1 1. GHG emissions in cassava production phase

Ephase-1 = Eec + El Eq. (SI.2.1) Eec= Efertilizer + Epesticide + Ediesel-1 + Eurea + Eburning + EN2O

Efertilizer = CO2e emissions from fertilizer use (g CO2e l-1); Epesticide = CO2e emissions from pesticide use (g CO2e l-1); Ediesel = CO2e emissions from diesel consumption (g CO2e l-1); Eburning = CO2e emissions from burning of cassava residue (g CO2e l-1); EN2O = N2O emissions from managed soils (g CO2e l-1); and Eurea = CO2 emissions from urea application (g CO2 l-1). 2. CO2e emissions from fertilizer use

Efertilizer = EFN × AmountN + EFP2O5 × AmountP2O5 + EFK2O × AmountK2O Note: The GHG emissions from manure application are taken to be zero according to Biograce [36.3].

EFN = Emission factor of N (g CO2e kg-1 of N); EFP2O5 = Emission factor of P2O5 (g CO2e kg-1 of P2O5); EFK2O = Emission factor of K2O (g CO2e kg-1 of K2O); AmountN = Amount of N applied (kg of N l-1); AmountP2O5 = Amount of P2O5 applied (kg of P2O5 l-1); and AmountK2O = Amount of K2O applied (kg of K2O l-1). 3. CO2e emissions from pesticide use

Epesticide = EFpesticide × Amountpesticide

EFpesticide = Emission factor of pesticide (g CO2e kg-1); Amountpesticide = Amount of pesticide applied (kg l-1). 4. CO2e emissions from diesel consumption

Ediesel-1 = EFdiesel × LHV × Amountdiesel-1

EFdiesel = Emission factor of diesel (g CO2e MJ-1); LHV = Low heating value of diesel (MJ l-1). Amountdiesel-1 = Amount of diesel consumption for operating tractors and cassava transportation (l of diesel l-1)

1 Abstracted from Biograce 2011; IPCC 2006; Directive 2009/28/EC; Commission Decision 2010/335/EU


5. CO2 emissions from urea application

EUREA=106 × P-1 × 4412

× Murea × EFurea

EFurea = Emission factor of urea (t C (t urea)-1) MSF = Amount of urea applied (t of urea ha-1) 6. CO2e emissions from burning of cassava residue

Eburning = 106 × P-1 × MB × Cf-B × (EFN2O, Agri. residue × GWPN2O + EFCH4, Agri. residue × GWPCH4)

P = Productivity (l of ethanol ha-1) MB = Weight of dry matter (DM) of cassava residue available for burning (t DM ha-1) Cf-B = Proportion of cassava residue that is actually burnt EFN2O,Agri. residue = N2O emission factor of burning (t N2O (t DM)-1) EFCH4, Agri. residue = CH4 emission factor of burning (t CH4 (t DM)-1) GWPN2O = Global Warming Potential of nitrous oxide (t CO2e (t N2O)-1) GWPCH4 = Global Warming Potential of methane (t CO2e (t CH4)-1)

MB x Cf-B = 79% of cassava residue (with 21% residue for seed and returning to soil)

= 79% �1 -HI�yield of root

HI × dried to fresh ratio (22%)

with Harvest Index = yield of fresh root

yield of fresh root+residue = 0.55 [45]

7. N2O emissions from managed soils

EN2O=106 × P-1 × GWPN2O × 4412

�EN2O-N,dir + EN2O-N,ind�

EN2O-N,dir = Direct N2O-N emissions from managed soils (t N2O-N ha-1) EN2O-N,ind = Indirect N2O-N emissions from managed soils (t N2O-N ha-1) Direct N2O-N emissions: EN20-N,dir = (FON+ FSN+ FCR+ FSOM) x EFN2O-N,dir

FON = Amount of organic fertilizer nitrogen applied (t N ha-1) FSN = Amount of synthetic fertilizer nitrogen applied (t N ha-1) FCR = Amount of N in crop residues, returned to the soil (t N ha-1) FSOM = Amount of N mineralized in association with loss of soil C due to LUC and managed soils (t N ha-1) EFN2O-N,dir = Emission factor for direct nitrous oxide emissions from N inputs (t N2O-N (t N) -1)

FSN = MSF × WN-SF FON = MOF xWN-OF FCR = MB × Cf-R × WN, AG

FSOM = �∑SOChistoric-i − SOCPJ-j

Ti=1-4, j=1-2 × RPJ-i� × 1

R

WN-SF = weight fraction of nitrogen in synthetic fertilizer (t N (t synthetic fertilizer)-1) WN-OF = weight fraction of nitrogen in organic fertilizer (t N (t organic fertilizer)-1) MSF = Amount of synthetic fertilizer applied (t) MOF = Amount of organic fertilizer applied (t) Cf-R = Proportion of cassava residue returned to the soil WN, AG = N content in dry matter of cassava foliage residue (t N (t DM)-1)

MB x Cf-R = 21%�1-HI�yield of root

HI × dried to fresh ratio (22%)

R = C:N ratio of the soil organic matter SOChistoric-I = Soil carbon stock of land use case i before cassava cultivation (t C ha-1)

There are four cases of land use before cassava cultivation including forest land, grassland, other annual crop land, and perennial crop land.

SOCPJ-j = Soil carbon stock under cassava cultivation case j (t C ha-1) There are two cases of cassava cultivation including mono-and inter-cropping.

RPJ-I = Ratio of land use case i T = Time dependence of the stock change factors (y)


SOChistoric-i = SOCREF × FLU, historic-i × FMG, historic-i × FI, historic-i

SOCREF = Reference soil carbon stock value (t C ha-1) FLU, historic-I = Land use factor under land-use case i before cassava cultivation FMG,historic-i = Management factor under land-use case i before cassava cultivation FI, historic-I = Input factor under land-use case i before cassava cultivation

SOCPJ-j = SOCREF × FLU,PJ-j × FMG,PJ-j × FI,PJ-j

FLU,PJ-j = Land use factor under cassava cultivation case j FMG,PJ-j = Management factor under cassava cultivation case j FI,PJ-j = Input factor under the cassava cultivation case j Indirect N2O-N emissions: EN2O-N,ind = EN2O-N,ind,ATD + EN2O-N,ind,L EN2O-N,ind,ATD = Indirect N2O-N emissions due to atmospheric deposition of nitrogen volatilized (t N2O-N ha-1) EN2O-N,ind,L = Indirect N2O-N emissions from leaching/run-off due to nitrogen application (t N2O-N ha-1)

PEN2O-N,ind,ATD = (FSN x FracGASF + FON × FracGASM) × EFN2O-N,ATD

PEN2O-N,ind,L = (FSN + FON + FSOM + FCR ) × FracLEACH × EFN2O-N,L

FracGASF = Fraction of synthetic fertilizer N that volatilizes as NH3 and NOX (t N volatilized (t N applied) -1) FracGASM = Fraction of organic N fertilizer that volatilizes as NH3 and NOX (t N volatilized (t N applied) -1) FracLEACH = Fraction of all N added to/mineralized in the soil that is lost through leaching and runoff

(t N leached and runoff (t N applied) -1) EFN2O-N,ATD = Emission factor for N-atmospheric deposition on soils (t N2O-N (t N volatilized)-1) EFN2O-N,L = Emission factor for N2O emissions from N leaching and runoff (t N2O-N (t N leached and runoff) -1) 8. Annualized emissions from carbon stock changes caused by LUC

El = 106 × P-1 × �∑CShistoric-i− CSPJ-i

T × RPJ-ii=1-4,j=1-2 � × 44

12

CShistoric-i = SOChistoric-i + CVEG- historic-i

CSPJ-j = SOCPJ-j + CVEG-PJ-j

CShistoric-i = Carbon stock of land use case i before cassava cultivation ( t C ha-1); CSPJ-j = Carbon stock under cassava cultivation case j ( t C ha-1); CVEG- historic-i = Vegetation carbon stock of land use case i before cassava cultivation ( t C ha-1); CVEG-PJ-j = Vegetation carbon stock under cassava cultivation case j ( t C ha-1). Appendix 2.2 GHG emissions in ethanol conversion phase

Ephase-2 = Ep = EFDAP × AmountDAP + EFUrea × AmountUrea+ EFEnzyme × AmountEnzyme+ EFNaOH + Eothers- Eccr Eq. (SI.2.2)

EFDAP = Emission factor of diesel (g CO2e kg-1)

EFUrea = Emission factor of diesel (g CO2e kg-1)

EFEnzyme = Emission factor of diesel (g CO2e kg-1)

EFNaOH = Emission factor of diesel (g CO2e kg-1) AmountDAP = Amount of DAP for ethanol conversion (kg l-1) AmountUrea = Amount of urea for ethanol conversion (kg l-1) AmountEnzyme = Amount of enzyme for ethanol conversion (kg l-1) AmountNaOH = Amount of NaOH for ethanol conversion (kg of diesel l-1) Eothers = Emissions from other sources (g CO2e l-1) Eccr = Amount of CO2 collected (g CO2e l-1) Appendix 2.3 GHG emissions in distribution and blending phase

Ephase-3 = Etd = EFelectricity × Amountelectricity + EFdiesel-2 × LHV × Amountdiesel-2

Eq. (SI.2.3)

EFelectricity = Emission factor of electricity (g CO2e kg-1 of kWh); Amountelectricity = Amount of electricity for blending (kWh l-1); Amountdiesel-2 = Amount of diesel consumption for transportation of fuels (l of diesel)


Appendix 3. List of parameters applied in the calculation

Parameters Value Source a-b. Emission factors of fertilizers and pesticides

EFN 5,880.60 Biograce 2011 EFP2O5 1,010.70 Biograce 2011 EFK2O 576.10 Biograce 2011 EFPesticide 10,971.30 Biograce 2011

c. Emission factor and LHV of diesel LHV 35.87 Davis et al. 2010 EFdiesel 83.8 Biograce 2011

d. CO2e emissions from burning of cassava residue GWPCH4 25 Biograce 2011; IPCC 2006 GWPN2O 298 Biograce 2011; IPCC 2006 EFCH4, Agri.residue 0.0027 IPCC 2006 EFN2O,Agri.residue 0.00007 IPCC 2006

e. N2O emissions from managed soils FRACGASM 0.20 IPCC 2006 FRACLEACH 0.30 IPCC 2006 EFNO2-N,dir 0.01 IPCC 2006 EFN2O-N,L 0.0075 IPCC 2006 EFN2O-N,ATD 0.01 IPCC 2006 WN, AG 0.0157 Howeler 2010 WN, ON 0.0032 Hongwei 2004 R 15 IPCC 2006

f. Urea application EFurea 0.2 IPCC 2006

g. CO2 emissions from land-use change1 SOCREF (t C ha-1) 47 IPCC 2006 SI.1.2 Emission factors of inputs in ethanol conversion

EFDAPi 1,527.00 Biograce 2011 EFUrea 3,167.00 Biograce 2011 EFEnzyme 1,000.00 Biograce 2011 EFNaOH 469.30 Biograce 2011

SI.1.2 Emission factor of electricity for blending EFelectricity 565.20 Biograce 2011

Note: 1See Appendix 3a below for further details

CO2 emissions from land use change

Forest land Grassland

Annual cropland

Perennial cropland

Intercropping1

FLU 1 1 0.48 1 0.93 FMG 1 0.97 1.15 1.15 1.15 FI 1 1 1 1 1.00 SOC (t C ha-1) 47.00 45.59 25.94 54.05 50.36 CVEG 21.00 8.10 - 14.40 12.51 CS 68.00 53.69 25.94 68.45 62.87 Sources: Biograce 2011; IPCC 2006; Directive 2009/28/EC. Note: 1These figures are calculated from parameters of annual cropland and perennial cropland.

37

Econ

omy a

nd En

viron

men

t Pro

gram

for S

outh

east

Asia

App

endi

x 4.

Est

imat

ion

of la

nd-u

se c

hang

e du

e to

cas

sava

pro

duct

ion

expa

nsio

n

App

endi

x 4.

1 Pr

ojec

tion

of c

assa

va c

ultiv

ated

are

a

20

09

2010

20

11

2012

20

13

2014

20

15

2020

20

23

2024

20

25

Etha

nol

10

3 t 2.

00

39.3

8 76

.75

114.

13

151.

50

188.

88

226.

25

413.

13

525.

25

562.

63

600.

00

Etha

nol

10

6 l 2.

53

49.8

4 97

.15

144.

46

191.

77

239.

08

286.

39

522.

94

664.

87

712.

18

759.

49

Cass

ava

fres

h ro

ot

10

3 t 15

.19

299.

05

582.

91

866.

77

1,15

0.63

1,

434.

49

1,71

8.35

3,

137.

66

3,98

9.24

4,

273.

10

4,55

6.96

Yi

eld

t ha-1

17.1

7 18

.41

19.7

4 21

.16

22.6

8 24

.31

34.4

2 42

.40

45.4

5 48

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Culti

vate

d ar

ea

103

ha

17

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31.6

6 43

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9 63

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70.6

8 91

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94.0

9 94

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93.5

2 A

ppen

dix

4.2

Land

-use

cha

nge

due

to c

assa

va p

rodu

ctio

n ex

pans

ion

Prov

ince

A

vera

ge

cass

ava

area

in

2005

–200

9 (h

a)

Wei

ght

(%)

% o

f LU

C to

cas

sava

(mon

o-cr

op) f

rom

%

of L

UC

to c

assa

va (i

nter

-cro

p)

from

Fore

st la

nd

Gra

ssla

nd1

Ann

ual c

rop

land

Pe

renn

ial c

rop

land

A

nnua

l cro

p la

nd

Pere

nnia

l cro

p la

nd

Tay

Nin

h

45,6

00

14.1

7 0

5 40

35

5

15

Binh

Phu

oc

23,3

00

7.24

10

10

10

5

20

45

Don

g N

ai

19,3

00

6.01

5

10

30

15

5 35

G

ia L

ai

49,6

00

15.4

0 20

35

10

30

0

5 Ko

m T

um

34,1

00

10.5

9 25

50

15

5

0 5

Dak

Lak

20

,800

6.

45

20

45

20

10

0 5

Dak

Non

g

19,5

00

6.07

15

55

20

0

0 10

Q

uang

Nga

i 19

,300

6.

01

10

75

5 10

0

0 Bi

nh T

huan

24

,400

7.

57

10

20

55

15

0 0

Ngh

e A

n

16,6

00

5.17

0

20

55

25

0 0

Binh

Din

h

13,2

00

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5

30

10

55

0 0

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ng N

am

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00

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5

20

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0 0

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u Ye

n

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25

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5

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25

0 0

0

Viet

nam

%

10

0.00

11

.68

31.1

5 26

.09

18.9

2 2.

46

9.71

ha

94

,086

.05

10,9

88.7

8 29

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.08

24,5

44.6

7 17

,796

.97

2,31

1.09

9,

139.

44

Sour

ce: S

urve

y (2

011)

. N

ote:

1 Barr

en la

nd a

nd d

enud

ed h

ills i

n Vi

etna

m a

re c

lass

ified

as

gras

slan

d.

38

Bi

ofue

l Pro

ducti

on in

Viet

nam

: Cos

t-Effe

ctive

ness,

Ener

gy an

d GHG

Balan

ces

App

endi

x 5.

Cal

cula

tion

of e

nerg

y sa

ving

and

GH

G e

mis

sion

sav

ing

for o

ne h

ecta

re a

nd fo

r the

GoV

’s ta

rget

vol

ume

of e

than

ol

Lite

rs o

f eth

anol

per

ha

(l ha

-1)

5,51

6 (a

) Li

ters

of 6

00 T

t of e

than

ol (

l (60

0 Tt

)-1)

759,

493,

671

(b)

Fuel

con

sum

ptio

n in

tran

spor

t sec

tor i

n 20

09 (1

06 MJ)

43

0,00

0 (c

) Em

issi

ons

from

fuel

pro

duct

ion

and

com

bust

ion

in tr

ansp

ort s

ecto

r in

2009

, Ec

= ((c

) x (f

)x 1

0-6) (

Mt C

O2e)

36

.03

(Ec)

Ener

gy in

put o

f 1 M

J gas

olin

e

1.13

75 (d

) LH

V of

gas

olin

e (M

J l-1

) 32

.17

(e)

Emis

sion

s fr

om g

asol

ine

prod

uctio

n an

d co

mbu

stio

n (g

CO

2e

MJ-1

) 83

.8 (f

) En

ergy

bal

ance

km

l-1

For F

U

For 1

lite

r of e

than

ol

For t

he ta

rget

ed v

olum

e of

600

Tt

Scen

ario

M

J inpu

t km

-1

MJ in

put l

etha

nol-1

l ga

solin

e let

hano

l-1

106

MJ in

put (

600

Tt)-1

10

6 l ga

solin

e (6

00 T

t)-1

106

MJ ou

tput

(600

Tt)-1

%

of (

c)

(g)

(h)

(i)=(

g)x(

h)

(j)=(

i):((d

)x(e

)) (k

)=(i)

x(b)

x10-6

(l)

=(k)

:((d)

x(e)

) (m

)=(k

):(d)

(n

)=((m

):(c)

)x10

0 S1

(E5,

+5%

) 0

.60

30.0

7 17

.97

0.4

9 13

,647

37

3 11

,997

2

.79

S2 (E

5,0%

) 12

.55

-1.3

5 -1

6.88

-0

.46

-12,

823

-350

-1

1,27

3 -2

.62

S3 (E

5,-5

%)

25.7

5 -2

.15

-55.

40

-1.5

1 -4

2,07

9 -1

,150

-3

6,99

1 -8

.60

S4 (E

10,+

5%)

6.5

7 0.

08

0.5

4 0.

01

411

11

362

0.08

S5

(E10

,0%

) 12

.55

-1.3

5 -1

6.88

-0

.46

-12,

823

-350

-1

1,27

3 -2

.62

S6 (E

10,-5

%)

19.1

5 -1

.89

-36.

14

-0.9

9 -2

7,45

1 -7

50

-24,

132

-5.6

1 G

HG

bal

ance

km

l-1

For F

U

For 1

lite

r of e

than

ol

For o

ne h

a Fo

r the

targ

eted

vol

ume

of 6

00 T

t

Scen

ario

g

CO2e

km-1

g

CO2e

l etha

nol-1

l ga

solin

e let

hano

l-1

kg C

O2e

ha-1

t C

O2e

(600

Tt)- 1

%

of E

c (g

) (o

) (p

)=(g

)x(o

) (q

)=(p

):((f)

x(e)

) (s

)= (p

)x(a

)x10

-3

(t)=

(p)x

(b)x

10-6

(u

)= ((

t):(E

c))x

10-4

S1

(E5,

+5%

) 0

.60

1,01

9.60

60

9.18

0.

23

3,36

0 46

2,66

4 1.

28

S2 (E

5,0%

) 12

.55

-156

.08

-1,9

58

-0.7

3 -1

0,80

1 -1

,487

,314

-4

.13

S3 (E

5,-5

%)

25.7

5 -1

86.2

2 -4

,796

-1

.78

-26,

454

-3,6

42,5

54

-10.

11

S4 (E

10, +

5%)

6.57

-1

02.6

4 -6

75

-0.2

5 -3

,721

-5

12,3

25

-1.4

2 S5

(E10

,0%

) 12

.55

-156

.08

-1,9

58

-0.7

3 -1

0,80

1 -1

,487

,314

-4

.13

S6 (E

10,-5

%)

19.1

5 -1

76.3

5 -3

,377

-1

.25

-18,

628

-2,5

64,9

34

-7.1

2

Cost

-effe

ctiv

enes

s km

MJ-1

∆

cos

t VN

D k

m-1

VN

D M

J-1

VND

l-1

For t

arge

ted

volu

me

of 6

00 T

t (1

09 VN

D (6

00 T

t)- 1

) Sc

enar

io

(v)

(w)

(x)=

(v)x

(w)

(y)=

(x)x

LHV

(z)=

(y)x

(b)

S1 (E

5, +

5%)

0.02

83

14,1

04.7

6 39

9.39

8,

427

6,40

0 S2

(E5,

0%)

0.59

46

-250

.88

-149

.18

-3,1

48

-2,3

91

S3 (E

5,-5

%)

1.22

06

-618

.97

-755

.50

-15,

941

-12,

107

S4 (E

10, +

5%)

0.31

15

401.

65

125.

10

2,64

0 2,

005

S5 (E

10,0

%)

0.59

46

-250

.88

-149

.18

-3,1

48

-2,3

91

S6 (E

10,-5

%)

0.90

76

-498

.39

-452

.34

-9,5

44

-7,2

49

Sour

ces:

(b):

Gov

ernm

ent o

f Vie

tnam

200

7, (c

): A

PEC

2011

, (d)

: GRE

ET 2

011,

(e):

Dav

is e

t al.

2010

, (f):

Bio

grac

e 20

11, a

nd a

utho

r’s c

alcu

latio

ns.

Not

e: A

min

us si

gn m

eans

an

ener

gy s

avin

g fo

r ene

rgy

bala

nce

or a

GH

G e

mis

sion

savi

ng fo

r GH

G b

alan

ce.


Appendix 6. Cost-effectiveness of ethanol and gasoline at the discount rates of 8% and 10%

VND MJoutput

-1 MJoutput km-1

VND km-1 Cost difference Ethanol Gasoline VND km-1 %

(1) (2) (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=(5)x100/(4) At the discount rate of 8% Gasoline 377.67 2.564 968.34 Ethanol

S1 (E5, +5%) 411.06 35.32 14,517.01 968.34 13,548.67 1,399.16 S2 (E5, 0%) 411.06 1.68 691.29 968.34 -277.06 -28.61 S3 (E5, -5%) 411.06 0.82 336.78 968.34 -631.56 -65.22 S4 (E10, +5%) 411.06 3.21 1,319.73 968.34 351.39 36.29 S5 (E10, 0%) 411.06 1.68 691.29 968.34 -277.06 -28.61 S6 (E10, -5%) 411.06 1.10 452.91 968.34 -515.43 -53.23

At the discount rate of 10% Gasoline 377.79 2.564 968.66 Ethanol

S1 (E5, +5%) 426.82 35.32 15,073.43 968.66 14,104.76 1,456.11 S2 (E5, 0%) 426.82 1.68 717.78 968.66 -250.88 -25.90 S3 (E5, -5%) 426.82 0.82 349.69 968.66 -618.97 -63.90 S4 (E10, +5%) 426.82 3.21 1,370.31 968.66 401.65 41.46 S5 (E10, 0%) 426.82 1.68 717.78 968.66 -250.88 -25.90 S6 (E10, -5%) 426.82 1.10 470.27 968.66 -498.39 -51.45

Source: Author’s calculations. Appendix 7. Cost-effectiveness of biodiesel and diesel at the discount rates of 8% and 10%

Scenarios VND MJoutput

-1 MJoutput km-1

VND km-1 Cost difference Biodiesel Diesel VND km-1 %

(1) (2)1 (3)=(1)x(2) (4)=(1)x(2) (5)=(3)-(4) (6)=(5)x100/(4) At the discount rate of 8% Diesel 363.4 1.9 704.0 Biodiesel

S7 (B5, 0%) 540.7 1.8 953.1 704.0 249.1 35.4 S8 (B5,+5%) 540.7 37.0 20,014.6 704.0 19,310.7 2,743.2 S9 (B10,0%) 540.7 1.8 953.1 704.0 249.1 35.4 S10 (B10,+5%) 540.7 3.4 1,819.5 704.0 1,115.6 158.5

At the discount rate of 10% Diesel 363.8 1.9 704.6 Biodiesel

S7 (B5, 0%) 554.6 1.8 977.4 704.6 272.8 38.7 S8 (B5,+5%) 554.6 37.0 20,526.2 704.6 19,821.6 2,813.2 S9 (B10,0%) 554.6 1.8 977.4 704.6 272.8 38.7 S10 (B10,+5%) 554.6 3.4 1,866.0 704.6 1,162.4 164.8

Source: Author’s calculations. Notes: (1) 1See Table 3. (2) A plus sign means cost-ineffectiveness. (3) The high cost-ineffectiveness in S8 is due to the low contribution of biodiesel to blend B5.

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