Keywords

1 Introduction

The introduction and diffusion of biofuel industry have been promoted in many developed countries including Japan, which has established concrete mandates with numerical targets for both bioethanol and biodiesel. Table 15.1 shows changes to the biofuel introduction targets in Japan. In response to government requests to achieve the GHG emission reduction goals of the Kyoto protocol, the Petroleum Association of Japan has agreed to blend 840,000 kl/year of bio-ETBE (ethyl tertiary-butyl ether), equivalent to 210,000 kl of crude oil, into gasoline starting in fiscal year (FY) 2010. This blended bio-ETBE gasoline has been sold as “biogasoline,” and the number of service stations selling it has increased from 50 in 2007 to 3210 in 2012. On the other hand, Japan’s Ministry of the Environment (MOE) has been promoting a strategy to accelerate the use of biomass energy by supplying E3 gasoline, a blend of gasoline with 3% bioethanol. Demonstration projects for E3 have been conducted in Osaka, Tokyo, and Okinawa, but the amount of E3 gasoline sold in 2010 remained approximately 28,000 kl.

Table 15.1 Changes to biofuel introduction targets in Japan
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A number of studies have evaluated how achieving these mandates can contribute to reductions in GHG emissions and how the expansion of biofuel production can affect food security. However, there are few studies focusing on the interlinkages between different impacts, including trade-offs and synergies among different types of impacts. This chapter quantitatively assesses various environmental impacts by expanding biofuel production and ethanol usage and analyzes the interlinkages among different impacts under several options for introducing biofuel in Japan. We use three indicators for this analysis, life-cycle carbon footprint (LCCO2), water footprint (WF), and ecological footprint (EF), by considering feedstock types, changes in land use, imports, and environmental conditions as well as domestic supply capacity and national mandates. Based on the analysis, we end the discussion with policy implications of moving toward sustainable biofuel.

2 Methods and Materials

Available future scenarios were reviewed for transportation usage of bioethanol and biodiesel. The national targets for bioethanol (Table 15.2) were set on the basis of Public Notice No. 242 issued by the Ministry of Economy, Trade, and Industry (METI) in 2010. The biodiesel targets in Table 15.2 followed the targets set by the MOE in 2006, but we modified them by shifting 5 years ahead from the original targets (i.e., interpreting the 2030 MOE target as the 2035 target for this analysis) because the actual diffusion of biodiesel has been delayed.

Table 15.2 Biofuel diffusion scenario for this study (Crude oil equivalent)
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For analyzing each scenario, five options were prepared by considering the type of biomass, producer country, associated land use changes, competition with respect to food production, supply pattern, and transportation (Figs. 15.1 and 15.2).

Fig. 15.1
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Supply options for bioethanol in Japan

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Fig. 15.2
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Supply options for biodiesel in Japan

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We used three assessment indicators: carbon footprint (CF), WF, and EF. CFs and WFs for biofuel derived from different crops were collected extensively and reviewed to identify differences among biomass sources. The maximum supply capacities of domestic options such as rice straw ethanol and waste cooking oil were calculated on the basis of domestic production and consumption of each biomass source (Table 15.3). Due to the variation in CF and WF values within the same biomass source, we used both upper-end and lower-end values as best case and worst case while calculating EF. Table 15.4 summarizes the domestic biofuel ratio (%) of each case and the target year. Unless Japan cannot expand the maximum supply capacity of the domestic options (Table 15.3), the domestic biofuel ratio will decrease owing to the increase in imported biofuel, which is necessary to fill the gap between domestic production and the targets, as described in Table 15.2.

Table 15.3 Maximum supply capacity of domestic options
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Table 15.4 Domestic biofuel ratio (%) by case and target year
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2.1 Carbon Footprint

CF or LCCO2 is one of the most popular indicators used in many LCA studies. CF can be defined as the total GHG emission due to biomass cultivation, extraction, transportation, the process of conversion to biofuel, and shipping of the biofuel. Today, CF is applied to the product labeling scheme in many countries.

2.2 Water Footprint

Water is needed for several processes in biofuel production. WF can be defined as the total annual volume of fresh water used to produce goods and services for consumption. WF consists of three components: the green WF, blue WF, and gray WF (Worldwatch Institute 2007). The green WF refers to rainwater that evaporates during production, mainly during crop growth. The blue WF is the surface- and groundwater used for irrigation that evaporates during crop growth. The gray WF is the amount of water needed to dilute pollutants discharged into the natural water system to the extent that the quality of the ambient water remains above agreed-upon water quality standards.

2.3 Ecological Footprint

EF is a tool to measure human demand by comparing with Earth’s ecological capacity to regenerate. It indicates the amount of biologically productive land and sea area needed to regenerate the resources consumed by a human population and to absorb its wastes (Rees 1992; Wackernagel 1994). Conceived in 1990 by Mathis Wackernagel and William Rees at the University of British Columbia, EF has been widely used by scientists, businesses, governments, agencies, individuals, and institutions to monitor ecological resource use and assess our pressure on Earth’s system. The following equation was used to calculate EF in this study. Wackernagel and Rees (1995) selected 6.6 mt as their average value for the total CO2 sequestered by the world’s forests. Therefore, we also used the value of 6.6 Mg/ha for CO2 sequestration. This value would be 3.2 Mg/ha (Greenhouse Gas Inventory Office of Japan 2010) by assuming the offset CO2 emissions from the forests in Japan:

EF(ha) = EFcf + EFharvest + EFwaterwhere

  • EFcf = Forest cover (ha) needed to assimilate CO2 emissions from the biofuel supply (i.e., CF)

  • EFharvest = Farmland cover (ha) needed to harvest crops or vegetables for biofuel

  • EFwater = Water catchment area (ha) needed to collect the total water volume required to grow biofuel crops and vegetables (the blue WF and the green WF)

3 Results

3.1 CF, WF, and EF per Unit Amount

3.1.1 Carbon Footprint

Table 15.5 and Fig. 15.3 summarize the net life-cycle GHG emissions from biofuels derived from different biomass sources. Within the same type of biofuel such as corn ethanol, different studies report different values depending on the researcher, production system, and accounting boundary. Until 2005, most of the studies on corn ethanol showed a corn ethanol CF slightly larger than that of gasoline, but studies after 2006 have demonstrated a 20 % or even greater GHG reduction by gasoline. Sugarcane ethanol has a smaller CF than that of corn ethanol, which is equivalent to one-fifth of the gasoline GHG emission. This relative advantage of sugarcane is because the bagasse—a by-product of the sugarcane plant—can be used as an energy source in ethanol refinery. METI’s Public Notice No.242 (2010) specifies that CF from bioethanol should be less than 50 % of that from gasoline (81.7 g-CO2eq/MJ).

Table 15.5 Life-cycle GHG emissions excluding those due to changes in land usage
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Fig. 15.3
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Life-cycle GHG emissions (carbon footprint) of various biofuels

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CF from soybean biodiesel is reported to be approximately half that of conventional diesel. CF from palm oil biodiesel is even smaller than that of soybean biodiesel if we ignore the methane emissions from the conversion of peatland to oil palm plantations, a common occurrence in Indonesia and Malaysia.

3.1.2 Water Footprint

Table 15.5 summarize WF per unit amount of fuel. Gerbens–Leenes et al. (2009a) report that WF of biodiesel is generally greater than that of bioethanol while using global averages. The global average WF of biodiesel crops ranges from 394 to 574 m3/GJ biodiesel. Jatropha is famous for being tolerant to wasteland, but its requirement for water is greater than many other energy crops, which implies that water availability may be one of the constraints for Jatropha biodiesel supply.

The global average WF of bioethanol crops ranges from 59 to 419 m3/GJ. WFs of sugar beet, potato, and sugarcane are 59, 103, and 108 m3/GJ, respectively, whereas sorghum (419 m3/GJ) has the largest WF of all ethanol crops (Table 15.6).

Table 15.6 Water footprints for ten crops providing ethanol and five crops providing biodiesel (m3/GJ)
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These results suggest that switching to biomass energy may result in an increased demand for fresh water, which eventually will intensify the competition between water usage for food production and energy (Bazilian et al. 2011).

3.1.3 Ecological Footprint per Unit Amount of Biofuel

EFs per unit of biofuel are compared according to cases in Fig.15.4. Producing bioethanol from sorghum and maize results in a larger EF than production from other biomass sources. Using construction waste wood is the best option for minimizing EF (Fig. 15.4a). Biodiesel from Jatropha and soybean yields an EF two to three times greater than other cases, and converting waste cooking oil to BDF is the best among all cases (Fig. 15.4b). Palm oil shows the smallest EF among three cases of imported biodiesel from other countries.

Fig. 15.4
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Ecological footprint per unit of biofuel for five cases each of (a) bioethanol and (b) biodiesel

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3.2 Scenario Analysis

Considering the targets for 2015, 2025, and 2035, different cases to achieve the targets (Figs. 15.1 and 15.2), the maximum supply capacity of each domestic biomass source (Table 15.3), and the domestic biofuel ratio (Table 15.4), we calculated CF, WF, and EF from 2015 to 2023 (Figs. 15.5, 15.6, 15.7, and 15.8). In addition to the five cases for each biofuel described in Figs. 15.1 and 15.2, we prepared a sixth case that maximizes the domestic biomass sources by combining sorghum, construction waste wood, and rice straw for bioethanol and by combining rapeseed and waste cooking oil for biodiesel (Table 15.4).

Fig. 15.5
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Carbon footprints of six bioethanol supply cases from 2015 to 2035

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Fig. 15.6
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Carbon footprints of six biodiesel supply cases from 2015 to 2035

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Fig. 15.7
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Water footprints of six supply cases from 2015 to 2035. (a) Bioethanol (b) Biodiesel

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Fig. 15.8
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Ecological footprints of six bioethanol supply cases from 2015 to 2035

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In terms of GHG emissions (CF), imported maize bioethanol shows the worst performance of the six cases, whereas bioethanol from sweet sorghum and construction waste wood shows better performances (Fig. 15.5). Bioethanol from rice straw emits more GHGs than other domestic cases (cases 3, 4, and 6). The difference between sugarcane ethanol imported from Brazil (case 2) and ethanol from domestic construction waste wood (case 4) is reduced in 2035 because imports of complementary bioethanol are increased to achieve the target.

GHG emissions from the domestic biodiesel cases (cases 4–6) tend to be lower than the importing cases, but the differences are not as significant as those in the bioethanol cases (Fig. 15.6). The combination of all domestic BDFs (case 6) gives the best result of all the cases.

Among the bioethanol WFs from the six cases, sweet sorghum (case 3) shows the largest WF (Fig. 15.7a). Therefore, case 6, which maximizes domestic biodiesel, indicates a larger WF than that of construction waste wood (case 4) and rice straw (case 5). Jatropha (case 2) requires the maximum amount of water out of any of the other cases investigated in this study (Fig. 15.7b). Palm oil (case 1) and domestic rapeseed (case 4) show similar WF performances. Waste cooking oil (case 5) is the best option in terms of WF, even considering the complementary import of biodiesel (palm oil) to fill the gap between the maximum supply capacity of waste cooking oil and the national target.

Figure 15.8 summarizes EFs of all bioethanol cases from 2015 to 2035. Construction waste wood shows the smallest EF out of all the cases, whereas maize ethanol is calculated to have the largest EF. In 2035, maximizing the domestic sources (case 6) would not be the best option because the performance of bioethanol is almost similar to that of sugarcane (case 2) and rice straw (case 5), which suggests that care should be taken while selecting combinations of available options to minimize EF in longer term.

Jatropha has the largest EF of all the cases, with soybean coming in the second place (Fig. 15.9) because of the large land area required to harvest it (EFharvest) and the catchment area required for water (EFwater). EF of waste cooking oil (case 5) was the smallest of all the cases, but the EFs of palm oil (case 1), rapeseed (case 4), and the combination of domestically produced biodiesel (case 6) were all less than 2 million ha. The results demonstrate that importing biodiesel produced from Jatropha and soybean does not make sense in terms of EF because their EFs are three to four times larger than those of other cases.

Fig. 15.9
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Ecological footprints of six biodiesel supply cases from 2015 to 2035

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4 Discussion and Conclusion

An integrated sustainability assessment model of biofuel that uses several biomass sources was developed in this chapter. Figure 15.10 summarizes the results of the scenario analysis, which uses six different cases to achieve Japan’s national target for bioethanol and biodiesel. This figure suggests that Japan needs to import more than 40 % of its bioethanol to achieve the national target in 2035, except in case 6 (maximizing domestically produced bioethanol) (Fig. 15.10a). Similarly, Japan needs to import at least 59 % of its total biodiesel to achieve the 2035 target (Fig. 15.10b). In general, a dependency on the imported biofuel or a self-sufficiency in biofuel production has an influence on the level of EFtotal.

Fig. 15.10
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EFtotal and domestic biofuel ratio by case. (a) Bioethanol (b) Biodiesel

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This assessment model can provide not only the overall ecological footprint for each case but also a detailed breakdown of EFcf, EFharvest, and EFwater. This allows us to identify relationships across these indicators. For example, Fig. 15.11 indicates the linkage between EFcf and EFharvest in six bioethanol cases, which suggests that EFcf in general increases EFharvest, but we can find different paths (regression lines) with steeper slopes, such as case 6, and those with moderate slopes, such as cases 1, 2, and 4. This means that the same reduction in GHG emission results in different levels of EFharvest depending on the case chosen by the government. It is highly recommended that the government applies multi-criteria sustainability assessment as demonstrated by this chapter in addition to conventional cost-benefit analysis prior to making a policy decision to expand biofuel production and import.

Fig. 15.11
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Relationships between EFcf and EFharvest in six bioethanol cases

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