starch, and PBS

Accepted Manuscript A trade-off between carbon and water impacts in bio-based box production chains in Thailand: A case study of PS, PLAS, PLAS/starch, and PBS Nitchanan Cheroennet, Shinatiphkorn. Pongpinyopap, Thanawadee Leejarkpai, Unchalee Suwanmanee PII:

S0959-6526(16)32014-5

DOI:

10.1016/j.jclepro.2016.11.152

Reference:

JCLP 8541

To appear in:

Journal of Cleaner Production

Received Date: 13 February 2016 Revised Date:

24 November 2016

Accepted Date: 24 November 2016

Please cite this article as: Cheroennet N, Pongpinyopap S, Leejarkpai T, Suwanmanee U, A trade-off between carbon and water impacts in bio-based box production chains in Thailand: A case study of PS, PLAS, PLAS/starch, and PBS, Journal of Cleaner Production (2016), doi: 10.1016/j.jclepro.2016.11.152. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT A trade-off between carbon and water impacts in bio-based box production chains in Thailand: A case study of PS, PLAS, PLAS/starch, and PBS

Nitchanan Cheroennet1, Shinatiphkorn. Pongpinyopap1, Thanawadee Leejarkpai2, Unchalee Suwanmanee3,*

Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand.

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National Metal and Materials Technology Center, National Science and Technology Development Agency,

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Department of Chemical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakornnayok

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Pathumthani 12120, Thailand.

26120, Thailand.

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Corresponding author*, e-mail: [email protected]

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ACCEPTED MANUSCRIPT Abstract Currently, bio-based plastics are considered the most promising and environmentally friendly alternative to replace petroleum-based plastics to reduce their environmental impacts. The aim of this research work is to assess and compare the life cycle impact of three types of bio-based boxes (namely, polylactic acid from sugarcane, polylactic acid from sugarcane-starch blends and polybutylene succinate from sugarcane and corn)

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and petroleum-based boxes of polystyrene. The locations of the plantation stage are focused in 4 provinces, namely, Kanchanaburi, Sakaeo, Prachinburi, and Chonburi provinces, in Thailand. The total impact using the external environmental cost (unit: THB equivalent) is performed at two impact categories: carbon footprint and fresh water consumption. The results from this study indicate that polybutylene succinate reveals the lowest

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water footprint at 0.38 m3 H2O of all the bio-based boxes and presents the second lowest water deprivation at 0.008 m3 H2O equivalent and the lowest carbon footprint at -0.06 kg CO2 equivalent. The lowest water

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footprints for all bio-based boxes production chains are found in Kanchanaburi and Chonburi provinces because the highest sugarcane and corn yield were observed, respectively, whereas, the minimum of water deprivation for all bio-based boxes production chains are clearly observed in Sakaeo province because of the lowest amount of chemicals used during plantation stage. The total impact on CF decreased by 26−69% for the production of bio-based boxes because CO2 absorption from the photosynthetic reactions during the plantation stages were

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included. In conclusion, for bio-based boxes, the polybutylene succinate box showed the lowest total externality cost of 0.046 THB equivalent on production chain in Sakaeo province. This externality accounts for 64−74% of total cost for freshwater consumption but only accounts for 26−36% of total cost for CF. These results are

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beneficial to supporting the development for establishing bio-plastics industry in aspects of water used and CF. Therefore, the effective strategies for preparing a sufficient supply of irrigation system or efficient water

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management and appropriate performance from agrochemicals used for supporting the feedstocks production to bio-plastics industry production chains should be proposed.

Keywords: Polylactic acid; Polylactic acid-starch blends; Polybutylene succinate; Carbon footprint; Water deprivation; Externalities cost.

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ACCEPTED MANUSCRIPT Highlights

The externality cost of PS, PLAS, PLAS/starch, and PBS boxes was first performed.

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The externality cost is performed in terms of the aspects of water used and carbon footprints.

Geographic Information System in Thailand was applied for the water footprint value.

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PBS box presents the lowest externality environmental cost of the bio-based boxes.

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ACCEPTED MANUSCRIPT 1. Introduction The development of bio-plastics as an alternative plastic has become an issue of increasing importance because of its potential to reduce greenhouse gases (GHGs) and consumption of fossil fuels (Suwanmanee et al., 2013; Papong et al., 2013; Tsiropoulos et al., 2015). The global demand for bio-plastic production in 2018 reached a capacity of 6.731 million tonnes; an approximately 350% increase compared to the demand of the

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year 2012 (European Bioplastics, 2015). Currently, the dominant feedstocks for bio-plastics production, such as polylactic acid (PLA) and polyhydroxyalkanoate (PHA), are derived from renewable resources (cassava, corn, and sugarcane). Moreover, bio-based plastics can be partially made from renewable resources and conventional

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plastics, e.g., polybutylene succinate (PBS), poly (butylene adipate-co-terephthalate) (PBAT), and bio-based PET (Tabone et al., 2010). Among the various types of biodegradable plastics, PBS and PLA are the most popular bio-plastics types, having attracted particularly increasing commercial interest (Liu et al., 2009), and are

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expected to be an alternative of petroleum-based plastics in the future. PBS, which is a biodegradable aliphatic polyester derived from butanediol and succinic acid, has high molecular weights and excellent mechanical properties that are competitive to those of the petroleum-based plastics of polyethylene (PE), polystyrene (PS), and polyethylene terephthalate (PET) (Mizuno et al., 2015). PLA, which is aliphatic polyester, is derived from lactic acid and has desirable processability properties, i.e., mechanical, thermal, and rheological, comparable to

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those of petroleum-based plastics (Hamad et al., 2016). However, its high cost has limited the application of PLA; as a result polymer blending techniques were used. Starch is a potentially useful material for bio-based plastics, has a wide variety of sources and a low price, and is easily biodegradable. Therefore, the PLA/starch

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blend is one of the options to extend the range of applications of PLA, especially when its high price is reduced (Zhang et al., 2013). Moreover, European Bioplastics (2015) reported that 11.3% of bio-plastics were produced

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from starch-based compared to the total bio-plastics used in the packaging sector. The development of bio-plastics in Thailand has been accelerating as a result of “New Wave Industry”

program since 2008 because of the plentiful feedstocks used to produce bio-plastics i.e., lactic acid from sugarcane which was produced in a commercial plant with a capacity of 100,000 tonnes per year in 2011 by PURAC Company in Thailand.

Moreover, a joint venture between PTT Public Company Limited and

Mitsubishi Chemical in Thailand has been planning to build the commercial PBS production plant with a capacity of 20,000 tonnes per year (Plastics Institute of Thailand, 2013).

Thailand is one of the major

agricultural producing countries of the world. In 2012, BOI (2014) reported that the average annual the production of cassava and sugarcane was approximately 26 million tonnes and 98 million tonnes, respectively.

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ACCEPTED MANUSCRIPT Life cycle assessment (LCA) is a common tool used to evaluate the environmental performance of a material, product, process, system or service throughout its entire life cycle, i.e., from ‘cradle-to-factory gate’ and ‘cradle-to-grave’, based on international standards (ISO 14040, 2006). Various life cycle analyses of bioplastics studies support the further improvement of their products to certify sustainable resource consumption, particularly in the environmental impacts of fossil depletion and global warming potential (Suwanmanee et al.,

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2013; Tsiropoulos et al., 2015). Recently, LCA studies have emphasized the importance of assessing freshwater by quantifying water consumption of bio-based products such as food, feed, and biofuel (Gheewala et al., 2014; Pongpinyopap and Mungcharoen, 2014). Freshwater is one of the important factors for crop plantation and bio-

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plastics production from renewable resources. The global agricultural area used to grow crops feedstocks for bio-plastics is now in competition with food and feed (European Bioplastics, 2015). However, there is a lack of research focusing on the water footprint impact of bio-based plastics. Our previous studies had evaluated and

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compared global warming, acidification, and photochemical oxidation, of bio-based, PLA from corn (PLAC) and PLAC-starch blends, against petroleum-based, PS, plastics for single use boxes (Suwanmanee et al., 2013). Nevertheless, there is a lack of research focusing on both carbon and water footprint impacts of bio-based plastics, PLA from sugarcane (PLAS) and PLAS starch blends, and a lack of a comparative study on the new bio-based plastic, PBS, produced from renewable resources.

A similar study was conducted by

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Taengwathananukul et al. (2013). They investigated and compared whether there are impact categories of global warming, abiotic depletion, and ecotoxicity impacts of coffee cups made from PLA from cassava and melamine. Their results indicated that the PLA cup led to 4.6 − 4.8 times fewer environmental impacts than the

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melamine cup. Ingrao et al. (2015) assessed and compared the carbon footprint of PLA and PS trays from cradle-to-grave for the packaging of fresh food by considering various transport system scenarios.

The

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comparative assessment showed that the carbon footprint associated with life cycle of a PLA tray was 5.5% slightly lower than that of the life cycle of a PS tray.

Tecchio et al. (2016) studied and examined the

environmental impacts, cumulative energy demand (CED) and greenhouse gas emission (GHG) for the production of 1 kg of PET and PBS granules at different production scales. In all of the various production scales, the results indicated that PBS had higher impact in all environmental categories of 30−45% and 34−60%, respectively. In this context, the study assessed and compared the carbon footprint, the water footprint impact, and the total impacts for quantification of the 100-year global warming potential (GWP), water deprivation potential (WDP), and externality cost (Social cost), all of which are associated with the life cycle of bio-based plastics,

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ACCEPTED MANUSCRIPT including PLA from sugarcane, (PLAS), PLAS-starch blends (PLAS/starch), and polybutylene succinate (PBS), for packaging application of food box container. This study includes the study of Suwanmanee et al. (2013) concerning application of LCA to PS boxes of identical size and production technology compared to those made of PLAS, PLAS/starch, and PBS, which are the objects of this study.

The results are beneficial to the

establishment of the bio-plastics industry, which is promising in the near future in Thailand, in terms of effective

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strategies for preparing a sufficient supply of raw materials for bio-plastics industry production chains.

2. Method

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The study was based on international standards on life cycle assessment (LCA) (ISO 14040 and ISO 14044 standards (see ISO 14040, 2006a; ISO 14044, 2006b)) for quantification and communication focusing on the carbon footprint and the impact of freshwater water consumption of three types of bio-based box and PS box Next, the environmental impact comparisons are included and quantified to estimate the total

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products.

environmental costs by using an external environmental cost.

2.1 Data source, assumption, and limitation

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Primary data on corn, cassava, and sugarcane productions are collected from field interviews with farmers in Thailand within 2014. The secondary data used in this study are important as they are taken from literature, Ecoinvent 2.0, and Intergovernmental Panel on Climate Change or IPCC (2006) method. The assumptions for the LCIA in this study are summarized as follows:

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The average amounts of inputs of raw materials (fuel, fertilizer and agrochemicals) during sugarcane and corn plantations from each province were calculated by weighted-average method based on

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sugarcane and corn planted area in year 2013−2015 (Office of Agricultural Economics, 2014). The percentages of raw materials input during sugarcane plantation from Kanchanaburi, Sakaeo, Prachinburi, and Chonburi provinces were evaluated as 67.7%, 20.9%, 1.2%, and 10.2%, respectively. The percentages of raw materials input during corn plantation were 47.1% in Kanchanaburi, 46.8% in Sakaeo, 5.7% in Prachinburi, and 0.4% in Chonburi provinces.


The data for the production of bio-based boxes forming is retrieved from the manufacturing of

packaging products in Thailand during 2014.

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The road transportation of the PLAS and PBS pellets to the box manufacturing site is

estimated at 185 km. The distance of the road transportation of all the studied boxes to the consumers is 40 km (Department of Highway, 2014).




This study assumes that there was no water consumption or emissions in the use stage by the

2.2 Water footprint and water deprivation assessment

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end users for all types of boxes.

The study was based on the water footprint network (WFN) and international standards ISO 14046

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(2014) for assessing water footprint (WF) and water deprivation potential (WDP) of bio- and petroleum-based

2.2.1 Water footprint assessment

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boxes, respectively.

The water footprint of bio-based boxes was estimated using the CROPWAT model (FAO, 2003) and the Geographic Information System in Thailand (GIS).

Three components of the water footprint to be

considered are green, blue, and grey water footprints. Regarding the methodology of Hoekstra et al. (2011), the

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green water footprint of crop is the ratio of rainwater to crop yield from the field during the growing period and the blue water footprint of crop is the ratio of irrigation water used to crop yield from the field during the growing period. The total crop water requirement was estimated by multiplying the crop coefficient (Kc) by the reference crop evaporation (ETo). The crop coefficients for corn and sugarcane were obtained from the Royal

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Irrigation Department (2009), and that for cassava was available from Kwanyuen et al. (2010). The reference crop ETo represented the Penman-Monteith evaporation and was estimated on the basis of the monthly climatic

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data of 30 years (1981−2010). The grey water footprint of crop is the volume of water required to dilute the pollutant concentration. This water was analyzed by the impact of nitrogen fertilizer application during crop growing which represented the amount of nitrogen leaching, by Roy et al. (2003). The limited of nitrate-NO3 concentration of 35 mg per liter (Pollution Control Department, 1996) was used to calculate the volume of water requirement to dilute the nitrogen leaching concentration. The operational blue water footprint is separated into two types: direct and indirect water used. The direct blue water footprint is evaluated to measure the evaporated water and water used during processing of all the studied boxes. Water used during the production of raw materials, chemicals, fossil fuels was implied as indirect blue water, which was assumed to be 10% of the abstracted water consumed (Flury et al., 2012).

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ACCEPTED MANUSCRIPT 2.2.2 Water deprivation potential assessment The LCA methodology accounted for fresh water consumption or blue WF in different regions to evaluate the impact of water use for the production system of bio-based boxes (ISO 14046, 2014). The application of the water stress index (WSI) value of LCIA is required to identify the place where water consumption occurs. The

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WSI obtained from Pfister et al. (2009) and Gheewala et al. (2014) was used as the characterization factor for calculating the impact of fresh water consumption or called ‘‘water deprivation potential or WDP’’. The water was measured in m3 water equivalents (m3 H2O equivalent). The WDP was calculated using equation (1).

i

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WDP = WFblue, i × WSI

(1)

2.3 Carbon footprint (CF) assessment

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where, WFblue,i is the blue water consumption in specific location i, WSIi is the WSI value in specific location i.

The carbon footprint (CF) was estimated according to the life cycle assessment concept and ISO/TS 14067: 2013 (ISO, 2013). All greenhouse gases (GHGs) were converted into a CO2 equivalent value using GWP in 100 years timeframe from the IPCC (2007), which has a relative value of 1 for CO2, 298 for N2O, 25 for CH4 and

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22,800 for SF6. The CF was calculated by multiplying the production activity from four types of box packaging by indicating each emission factor using Equation (2).

CF

(

n = ∑ Q k × EF Total k j=1

)

(2)

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where, CFTotal is the total CF from cradle-to-gate of each box production (kg CO2 equivalent/box); Qk is the quantity of the use of raw materials, chemicals, and fossil fuels k from four types of packaging; EFk is the

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emission factor k; and j to n are the set of CF during the productions of crops plantation, plastic pellets, forming, transportation and use.

The general formula for CF from the life cycle of each crop productions (CFCrop) is Equation (3). The total CFCrop of each crop productions was evaluated from three main sources of emissions: CF from photosynthesis reaction, production of input materials, and applied fertilizer N. The amounts of 0.049 kg CO2 per kg of cassava (Liu et al., 2012), 1.46 kg CO2 per kg of corn (Akiyama et al., 2003) and 0.85 kg CO2 per kg of sugarcane (de Figueiredo et al., 2010) are absorbed in the photosynthesis reaction during plant growth and are applied to calculate the amount of CFphotosynthesis of each crop.

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ACCEPTED MANUSCRIPT CFCrop = CFphotosynte sis + CFmat + CFfield

(3)

where, CFphotosynthesis is the CF from photosynthesis reaction during cassava, corn, and sugarcane plantation (kg

agrochemicals, and fuels; CFfield is the CF from applied fertilizer N.

2.4 Externalities costs

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CO2 equivalent/box); CFmat is the CF from the production of input materials, including fertilizers,

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The total environmental impacts using the external environmental cost (unit: THB equivalent) is performed at two impact categories, namely, water and carbon footprints. In this study, for the cost analyses,

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the estimated external cost of water consumption was applied in consideration for the average cost of wastewater treatment from the US Environmental Protection Agency (1992). The costs of the characteristic treatment processes consisted of secondary (aerated activated sludge) and tertiary (nitrification−denitrification) costs. These costs are estimated according to operation, maintenance, and capital recovery stages related to the treatment capacity of 19.3−31.6 million m3 per year. The wastewater quality including the concentration of

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biological oxygen demand (BOD) and nitrate were below the Thailand standard of 60 and 35 mg per liter, respectively (Pollution Control Department, 1996). In addition, the estimated external cost of wastewater treatment in US was adjusted by converting based on US value inflation of 0.028% from 1992 to 2013 (Trading Economics, 2014) and the ratio of US and Thailand gross domestic product (GDP) in year 2013 as

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0.89 THB per m3 (Central Intelligence Agency, 2014). Next, average cost of greenhouse gas emissions (CO2 equivalent) in unit cost was obtained from the social cost of carbon (SCC) study (U.S. Government IWG,

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2013). The estimated external cost of carbon footprint was based on dollar value inflation (0.021% from 2010 to 2013) and the ratio of US and Thailand GDP at purchasing power parity per capita in year 2013 as 0.272 THB per kg CO2 equivalent (Nguyen and Gheewala, 2008).

3. LCA goal, scope and functional unit The materials to be studied in this paper are PS, PLAS, PLAS/starch, and PBS. The functional unit defined for four types of boxes is 1 box of 8.0 × 10.0 × 2.5 cm. of 1 box the carrying capacity of 100 grams (g), including 0.045, 0.213, 0.126, and 0.161 kg of PS, PLAS, PLAS/starch, and PBS, respectively. In the LCA

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ACCEPTED MANUSCRIPT study, the information from cradle-to-gate of the PS was retrieved from Suwanmanee et al. (2013). The LCI in the production process of three bio-based boxes are summarized as follows.

3.1 PLAS boxes The system boundary from cradle-to-gate for PLAS box is shown in Figure 1. The cradle-to-gate LCI

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of the PLAS box consists of seven main stages, including sugarcane plantation, sugar milling production, the production of PLAS pellet, delivery of materials to box manufacturing site, PLAS box forming, delivery of PLAS box to consumers, and usage stage. Data for sugarcane plantation were obtained from the questionnaires

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and field interview with 9 farmers in the year 2014, covering 549 hectares of arable land in Kanchanaburi (96 hectares), Sakaeo (37 hectares), Prachinburi (48 hectares), and Chonburi (368 hectares) provinces, Thailand (see Table 1). Next, the LCI of productions of sugar milling was retrieved from Saibuatrong (2008). The production

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of 1 kg of sugar requires 11.25 kg of sugarcane, 5.65 kg of water, 0.07 kg of calcium hydroxide, 0.17 kWh of electricity, and 4.77 kg of steam. In sugar milling production, 0.05 kWh of electricity and 0.014 kg of steam were generated by burning bagasse. The economic allocation for sugar milling was based on the 2012 prices of sugar (19.59 THB per kg) and molasses (3.02 THB per kg) (Thai Custom Department, 2012). The production of 1 kg of PLAS requires 1.48 kg of sugar, 1.07 kWh of electricity, 2.96 kg of steam, 0.67 kg of lime, 0.74 kg of

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H2SO4, and 0.26 kg KOH (Groot and Boren, 2010). Next, the production of 1 PLAS box (0.21 kg) requires 0.24 kg of PLAS pellet and 0.014 kWh of electricity. The average distance from sugarcane field to sugar factory and

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PLA pellet manufacturing in Rayong province is approximately 198 km.

3.2 PLAS/starch boxes

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The cradle-to-gate system boundary for PLAS/starch box is shown in Figure 2. For the productions of

sugar milling and PLAS pellets, the information on the conversion process was taken from section 3.1. The production of cassava starch is separated into two phases: cassava plantation and cassava starch. In the cassava plantation stage, LCI was obtained from primary data in the year 2014 (see Table 1) based on the questionnaires and field interview with 10 farmers, covering approximately 198 hectares of arable land in Rayong (65 hectares) and Chonburi (133 hectares) provinces, Thailand. The LCI of cassava starch production was collected from Khongsiri (2009). The study assumed that the energy requirement for cassava starch production was from fuel oil (38%), power generation from biogas (61%), and electricity from the national grid. In addition, based on 1 PLAS/starch box (0.126 kg), in the production of PLAS/starch box forming, 0.105 kg of PLAS pellet, 0.03 kg of

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ACCEPTED MANUSCRIPT starch, and 0.01 kWh of electricity were consumed.

The inventory analysis of cassava transportation is

presented in two stages: road transportation of cassava and delivery of cassava starch to the packaging factory. It is assumed that the distance from the cassava field to the cassava starch factory is 20 km. The average distance from cassava starch plants in Rayong and Chonburi province to box manufacturing location is 154 km

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(Department of Highway, 2014).

3.3 PBS boxes

The production of PBS includes the productions of 1,4 butanediol (BDO) and succinic acid (SA), as

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shown in Figure 3. The production of BDO includes the production of bio-ethanol and SA (Argonne National Laboratory, 2014). The production of 1 kg of BDO requires 0.003 kg of bio-ethanol, 1.3 kg of SA, 0.0003 kWh

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of electricity, and 0.0006 MJ of natural gas. The production of bio-ethanol using sugarcane is separated into three major stages: the sugarcane plantation, the conversion of sugarcane into molasses by sugar milling and refining, and the processing of molasses into bio-ethanol by fermentation. The LCI of the sugarcane plantation, sugar milling and refining are reported in section 3.1. The production of 1 kg of molasses requires 1.52 kg of sugarcane, 0.76 kg of water, and 0.01 kg of calcium hydroxide. Bagasse was burned to generate 0.004 kWh of

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electricity and 0.015 kg of steam for sugar milling production. The production of 1 kg of bio-ethanol requires 3.49 kg of molasses, 0.24 kWh of electricity, 4.49 kg of steam, and 13.46 kg of water (Saibuatrong, 2008). The production of 1 kg of SA requires 0.90 kg of dextrose, 0.06 kg of sodium chloride, and 0.18 kg of carbon dioxide (Glassner and Datta, 1992). The production of 1 kg of dextrose requires 1.34 kg of corn, 21 kg of water,

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0.04 kg of natural gas, and 0.28 kWh of electricity (Vink et al., 2007). In the corn plantation stage, LCI was obtained from primary data in the year 2014 (see Table 1) based on the questionnaires and field interviews with

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12 farmers, covering 75 hectares of arable land in Kanchanaburi (50 hectares), Sakaeo (18 hectares), Prachinburi (2 hectares), and Chonburi (5 hectares) provinces, Thailand. Information of PBS pellet production was available from Nicolas et al. (2011) and then simulated. The simulation was performed by using Aspen Plus® version 8.2. The process was conducted in two stages via the melt polycondensation reaction. In the first stage, PBS oligomer and water were obtained by reacting BDO and SA in condition 225 °C and 2 bar. In the second stage, transesterification of PBS oligomer was heated to 230 °C under 0.7 mbar. The production of 1 kg of PBS pellet requires 0.52 kg of BDO, 0.69 kg of SA, and 0.13 kWh of electricity. The production of 1 PBS box (0.16 kg) requires 0.19 kg of PBS pellets and 0.015 kWh of electricity. It is assumed that the productions of

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ACCEPTED MANUSCRIPT BDO and PBS pellets are processed in the same area in Rayong province. The average distance from corn field to the PBS factory is 204.5 km.

4. Results and Discussion 4.1 Water footprint assessment

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Table 2 shows the results of water accounting phase for the WF studies, evaluated in m3. The highest average water footprint is PLAS (1.11 m3), with a green water contribution of 36.14%, a blue water contribution of 49.82%, and a grey water contribution of 14.05%. Next, the second highest WF belongs to PS (0.70 m3),

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with only blue water contribution of 100%, and the third highest WF is PLAS/starch (0.55 m3), with a green water contribution of 37.05%, a blue water contribution of 48.93%, and a grey water contribution of 14.02%. Last, the smallest water footprint is PBS (0.38 m3), with a green water contribution of 68.05%, a blue water

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contribution of 39.93%, and a grey water contribution of 0.01%. The WF of PLAS/starch is decreased by 0.56 m3 or 50.42% compared to PLAS because, based on 1 kg of materials feedstocks, the WF of cassava starch is lower than PLAS pellet 3 m3, accounting for 66.29%.

The blue WF for PLAS and PLAS/starch were 0.55 and 0.27 m3 per box, respectively, where the main contributors are electricity used in pellet production (73−75%) and irrigation water used in sugarcane production

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(19−22%). The blue WF obtained in this study was from high voltage electricity based on Ecoinvent 2.0 (0.41 m3 per kWh); as a result, the value was moderately higher than the WF obtained in the study by Morera et al. (2016). This difference was observed because freshwater consumption to produce the electricity of each work

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was evaluated by different technologies and estimated on a different basis between the databases of Ecoinvent 3 and 2. For the PBS box, the electricity used in the pellet production and the water consumption for chemicals

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used in corn and sugarcane planation production had a major effect on the total blue WF, contributing 79% and 15%, respectively. The grey WF value was the volume of water used to dilute the concentration of nitrogen leaching during crops plantation stage to be as low as 35 mg per liter according to Thailand standard. It can be observed that the highest grey WF in PLAS (0.15 m3) corresponded to the high consumption of sugarcane 4.06 kg per PLAS box.

The second highest grey WF corresponded to PLAS/starch (0.07 m3), with the low

consumptive of sugarcane and cassava of 1.75 and 0.11 kg, respectively. It can be observed that there is a high reduction of grey WF because of lower weight of PLAS/starch box when cassava starch was applied in PLAS. Although the study investigated green, blue and grey WF, when considering only the green and blue WF, it was found that the WF obtained in this study for sugarcane production (0.13 m3 per kg) was lower than the WF of

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ACCEPTED MANUSCRIPT the green and blue obtained in the study by Gheewala et al. (2014) (0.15−0.17 m3 per kg). The difference was because sugarcane yield obtained from our field study was higher than the study by Gheewala et al. (2014) of 18−42 ton per ha. In addition, the different methods used in this study and in Gheewala et al. (2014) revealed the different WF values during sugarcane production because blue WF value obtained was evaluated according to the CROPWAT model and the blue water availability (irrigation water) in the studied region by applying the

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Geographical information System. Comparing the WF value of cassava production, excluding grey WF, the present study showed a lower WF value (0.12-0.14 m3 per kg) than the WF from the study of Gheewala et al. (2014). Considering cassava yield (44.69 ton per ha) in this work, the WF value was calculated as 0.3 m3 per kg, whereas when considering Gheewala et al. (2014), the WF value was 0.39−0.41 m3 per kg with cassava

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yield approximately 19 ton per ha. The WF per kg corn (0.16 m3 per kg) is lower because the corn yield in this work (6.04 ton per ha) is higher when compared with the study by Gheewala et al. (2014), which has corn yield

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of 3.82 ton per ha. The minimum of WF of sugarcane and cassava starch-based for PLAS and PLAS/starch box production chains is 0.8 m3 and 0.4 m3 per box, respectively, in Kanchanaburi province. Moreover, the minimum of WF of sugarcane and corn-based for PBS production chain is 0.35 m3 per box in Chonburi province. Note that the minimum of WF in bio-based box production chains in the two provinces was due to the

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highest sugarcane and corn yield in Kanchanaburi (109 ton per ha) and Chonburi (6.92 ton per ha), respectively.

4.2 Water deprivation potential and carbon footprint assessment Figure 4 presents the comparisons of water deprivation potential of PS, PLAS, PLAS/starch, and PBS boxes.

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The WSI values of water consumption for each place are summarized in Table 3. The water deprivation of a PLAS box is 0.016 m3 H2O equivalent, which is 48%, 51%, and 93% greater than that of PBS, PLAS/starch, and

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PS boxes, respectively. The impact of water used was also found to be at a significant level during crops plantation and pellet production stages of all bio-based boxes as 31−53% and 30−57% of total impact, respectively. The minimum water impacts for PLA/starch (sugarcane and cassava-based), PBS (sugarcane and corn-based), and PLAS (sugarcane-based) production chains are 0.0064, 0.0048, and 0.013 m3 H2O equivalent, respectively, in Sakaeo province because the impact of indirect blue water consumption from chemicals used (pesticides) during plantation stage in Sakaeo province was lower than in Kanchanburi, Prachinburi, and Chonburi provinces (see Figure 4). Note that the ratio of blue and green WF were 29−34% and 51−71% in Sakaeo province, whereas the main WF in Kanchanburi is blue WF up to 35−59% of the total WF (see Table 2).

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ACCEPTED MANUSCRIPT No significant effects from WSI values were observed, as there was a slight difference in WSI values between Kanchanburi (0.018) and Sakaeo (0.022) provinces. All equations and assumption used for estimating of CF of bio-based plastics from cradle-to-gate are summarized in Table 4. Based on 1 box, 0.053 kg of PS pellet, 0.243 kg of PLAS pellet, 0.105 kg of PLAS pellet and 0.032 kg of cassava starch, and 0.194 kg of PBS are required for the production of PS, PLAS,

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PLAS/starch, and PBS boxes, respectively. Figure 5 presents the CF comparisons of PS, PLAS, PLAS/starch, and PBS boxes. The PBS box is found to have the lowest CF at -0.207 kg CO2 equivalent, followed by PS at 0.05 kg CO2 equivalent, PLAS/starch at 0.303 kg CO2 equivalent, and PLAS (which contributes the highest

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impact) at 0.689 kg CO2 equivalent. Similarly, the CF of PLAS/starch is decreased by 0.372 kg CO2 equivalent or 55.27% relative to PLAS because the CF of PLAS pellet 1 kg is higher than cassava starch 1 kg of 99.7%. Moreover, the result is consistent with Suwanmanee et al. (2013). The CF during the production of pellet is the

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primary cause of the total impact, accounting for 62−63% of the total impact for PLAS and PLAS/starch boxes and 21% for PBS box. Interestingly, the total impact on CF decreased moderately by 26% for the production of PLAS and PLAS/starch boxes and decreased extremely by 69% for the production of PBS because CO2 absorptions from the photosynthetic reactions during the plantation stages were included. Note that the CF of sugarcane per kg was consistent with the results of Nguyen et al. (2012) and Amores et al. (2013), who reported

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that the CF of sugarcane were 0.018 and 0.019 kg CO2 equivalent, respectively, excluding photosynthetic reactions during the plantation stage. The comparison with other studies revealed that CF of cassava per kg obtained in this study (0.008 kg CO2 equivalent) was low because of the lower consumption of chemicals than

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the study of Nguyen et al. (2007). The CF for 1 kg corn was -1.28 kg CO2 equivalent, which was derived from raw materials (0.136 kg CO2 equivalent) and CO2 absorption from the photosynthetic reactions (-1.416 kg CO2

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equivalent). This result agreed with the CF for the production of 1 kg corn in Pelletier et al. (2008) and Yousefi et al. (2014), who reported the value of CF of -1.16 and -2.16 kg CO2 equivalent, respectively, when the amount of CO2 absorption during photosynthetic reactions was considered to be -1.416 kg CO2 equivalent.

4.2 Externality comparison Figure 6 demonstrates that a PLAS box has a greater externality cost of water used than PLAS/starch, PBS, and PS boxes, by 51.31%, 78.14% and 97.18%, respectively. From the results, the most important contributions from PLAS, PLAS/starch, and PBS boxes are the plantation and pellet production stages during cradle-to-gate representing 15−24% and 73−79% of the total externality cost of water, respectively. Figure 7

14

ACCEPTED MANUSCRIPT illustrates the comparison of externality cost of CF of PS, PLAS, PLAS/starch, and PBS boxes. A PBS box has the lowest externality cost at -0.06 THB equivalent, followed by PS at 0.021 THB equivalent, PLAS/starch at 0.086 THB equivalent, and PLAS at 0.98 THB equivalent. Similarly, the total externality cost of CF was obtained in significant level during the pellet production stages of 21−62% of all bio-based boxes. All bio-based boxes exhibit the highest externality cost of CF at 26−68% and 21−63% during plantation and

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pellet production stages, respectively. The total externality cost of PLAS box generates overall impact that is 37%, 83%, and 144% higher than PLAS/starch, PS and PBS boxes, respectively. For all bio-based boxes, there was CO2 absorption from the photosynthesis reaction during plant growth, which helped reduce the total

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externality cost in the cradle-to-gate study by 3-40% of total externality cost. Therefore, the PBS box presents the lowest total externality cost of WF and CF at -0.06 THB equivalent compared to all studied boxes. The

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externality cost of 50−90% from electricity used in pellet production and 3−40% from the plantation stage extremely affect the total externality cost of all the studied bio-based boxes (see Figure 8(a)). This externality accounts for 64−74% of the total cost for freshwater consumption, but it only accounts for 26−36% of the total cost for CF (see Figure 8(b)). The discussion of the high rationality impact of freshwater used was evaluated and proposed with further respect to bio-plastics sustainable development, and provided sufficient water for the It is highly recommended that the agricultural

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operational feedstocks of bio-plastics production chains.

practices particularly affect crops yield and play a significant role in total externality cost during plantation stage, such as the high amount of agrochemicals (pesticide) used and the water supply from the irrigation system. Therefore, the government should have experts in the agricultural field promote the manual practices to

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farmer to ensure appropriate performance from agrochemicals use and prepare an efficient water supply near farms to support important agricultural areas by expanding the irrigation system or improving efficient water

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management.

5. Conclusions

The WF of the PLAS box (1.11 m3) was found to be the highest, followed by those of the PS box (0.70 m3t), PLAS/starch box (0.55 m3), and PBS box (0.38 m3). The green and blue WF during cradle-to-gate studies of all the studied bio-based boxes showed significant contributions of 36−68% and 32−68% of the total WF. In addition, the WF during the plantation and electricity used in pellet productions are the main causes of WF, for all the studied bio-based boxes. The minimum values of WF for the PLAS and PLAS/starch box production chains were found in Kanchanaburi province, and the minimum value of WF for the PBS production chain was

15

ACCEPTED MANUSCRIPT observed in Chonburi province because the highest sugarcane and corn yields in Kanchanaburi and Chonburi provinces. In addition, the PLAS box revealed the highest water deprivation (0.016 m3 H2O equivalent), followed by PBS, PLAS/starch, and PS boxes. The minimum values of water deprivation for all bio-based box production chains are in Sakaeo province because the impact of indirect blue water consumption from the chemicals used during the plantation stage in Sakaeo province presents the lowest value. Next, the PBS box

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presents the lowest CF, followed by PS, PLAS/starch, and PLAS boxes. For bio-based boxes, CO2 absorption from the photosynthesis reaction occurred during plant growth, which helped reduce the CF by 26−69% for the cradle-to-gate production chains. Finally, it can be concluded that PBS box is the best option for production

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chains in Sakaeo province when compared to all the studied boxes because it has the low externality cost of CF and WF aspects on the overall environment.

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7. Acknowledgements

This work has been supported by the Thai Research Fund (TRG5780114), Srinakharinwirot University Fund (SWU 167/2558) and National Science and Technology Development Agency Fund (P12−02213).

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8. References

Akiyama, M., Tsuge, T., Doi, Y., 2003.

Environmental life cycle comparison of polyhydroxyalkanoates

produced from renewable carbon resources by bacterial fermentation. Polym. Degrad. Stab. 80, 183−194.

EP

Amores, M.J., Mele, F.D., Jimenez, L., Castells, F., 2013. Life cycle assessment of fuel ethanol from sugarcane in Argentina. Int. J. Life Cycle Assess. 18, 1344−1357.

AC C

BOI., 2014. Thailand bio-plastics industry. Available: http://www.boi.go.th.

de Figueiredo, E.B., Panosso, A.R., Romao, R., Jr, N.L.S., 2010. Greenhouse gas emission associated with sugar production in southern Brazil. Carbon. Balance. Manage. 5(3), 1−7.

Central Intelligence Agency (CIA)., 2014. GDP – per capita (PPP). Available: https://www.cia.gov.

Department of Highway., 2014.

Distance between provinces in Thailand.

Available: http://map-

server.doh.go.th.

European Bioplastics., 2015. Bioplastics facts and figures. Available: www.european-bioplastics.org.

16

ACCEPTED MANUSCRIPT FAO., 2003. Agriculture, food and water (A contribution to world water development report).

Flury, K., Jungbluth, N., Frischknecht, R., Munoz, I., 2012. Recommendation for life cycle inventory analysis for water use and consumption. Available: http://www.esu-services.ac.th.

Gheewala, S.H., Silalertruksa, T., Nilsalab, P., Mungkung, R., Perret, S.R., Chaiyawannakarn, N., 2014. Water

RI PT

footprint and impact of water consumption for food, feed, fuel crops production in Thailand. Water: 6, 1698−1718.

Glassner, D.A., Datta, R., 1992. Process for the production and purification of succinic acid. US Patent

SC

5143834.

Groot, W.M., Boren, T., 2010. Life cycle assessment of manufacture of lactide and PLA biopolymer from

M AN U

sugarcane Thailand. Int. J. Life Cycle Assess. 15, 284−293.

Hamad, K., Kaseem, M., Deri, F., Ko, Y.G., 2016. Mechanical properties and compatibility of polylactic acid/polystyrene polymer blend. Mater. Lett. 164, 409−412.

Hoeksta, A.Y., Chapagain, A.K., Aldaya, M.M. Mekonnen, M.M., 2011. The water footprint assessment manual: setting the global standard. Earthscan, London, UK.

TE D

Ingrao, C., Tricase, C., Cholewa-Wojcik, A., Kawecka, A., Rana, R., Siracusa, V., 2015. Polylactic acid trays for fresh-food packaging: A carbon footprint assessment. Sci. Total Environ. 537, 385−398.

ISO 14040.,

2006.

Environmental management − Life cycle assessment − Principles and framework,

EP

International Organization for Standardization. Geneva, Switzerland.

ISO 14044., 2006. Environmental management − Life cycle assessment − Requirement and guidelines,

AC C

International Organization for Standardization. Geneva, Switzerland. ISO 14046., 2014. Environmental management water footprint − principles, requirements and guidelines.

ISO 14067., 2013. Greenhouse gas−Carbon footprint of products−Requirements and guidelines for quantification and communication. International Organization for Standardization.

IPCC (Intergovernmental Panel on Climate Change)., 2006. IPCC guidelines for national greenhouse gas inventories. In: Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. Agriculture, Forestry and Other Land Use, Prepared by the National Greenhouse Gas Inventories Programme, 4. Institute for Global Environmental Strategies (IGES), Japan.

17

ACCEPTED MANUSCRIPT IPCC., 2007. Fourth assessment report: climate change 2007 (AR4). Geneva, Switzerland.

Khongsiri, S., 2009. Life cycle assessment of cassava root and cassava starch.

M.S. Thesis, Kasetsart

University, Thailand. Kwanyuen, B., Namkang, P., Phuthongsook, W., Tanwiboonsak, S., 2010. Crop coefficient for cassava. The 11th

RI PT

Thai Society of Agricultural Engineering International Conference on Innovation in Agricultural Engineering for Sufficiency Economy and Empowered Communities. 6–7 May 2010 at Kasetsart University, Kamphaengsaen Campus, Nakhonpathom, Thailand.

Liu, L., Zhuang, D., Jiang, D., Huang, Y., 2012. Assessing the potential of the cultivation area and greenhouse gas (GHG) emission reduction of cassava based fuel ethanol on marginal land in Southwest China. J. Agric.

SC

Res. 41, 5594−5603.

M AN U

Mizuno, S., Maeda, T., Kanemura, C., Hotta, A., 2015. Biodegradability, reprocessability, and mechanical properties of polybutylene succinate (PBS) photografted by hydrophilic or hydrophobic membranes. 117, 58−65.

Morera, S., Corominas, L.L., Poch, M., Aldaya, M.M., Comas, J., 2016.

Water footprint assessment in

wastewater treatment plants. J. Clean. Prod. 112, 4741−4748.

TE D

MTEC., 2009. Thai national life cycle inventory database.

Nicolas, J., Floriane, F., Françoise, F., Alain, R., Jean, P.P., Patrick, F., Rene, S.L., 2011. Synthesis and properties of poly(butylene succinate): efficiency of different transesterification catalysts. J. Polym. Sci. Pol.

EP

Chem. 49, 5301–5312.

Nguyen, T.L.T., 2007. Life cycle assessment of bio-ethanol as an alternative transportation fuel in Thailand. The Joint Graduate School of Energy and Environment. King Mongkut’s University of

AC C

PhD. Thesis.

Technology Thonburi, Thailand.

Nguyen, T.L.T., Gheewala, S.H., 2008. Fossil energy, environmental and cost performance of ethanol in Thailand. J. Clean. Prod. 16, 1814−1821.

Nguyen, T.L.T., Hermansen, J.E., 2012. System expansion for handling co-product in LCA of sugar cane bioenergy system: GHG consequences of using molasses for ethanol production. Appl. Energ. 89, 254−261.

Office of Agricultural Economics (OEA)., 2014.

Agricultural Statistics of Thailand 2014.

Available:

http://www.oae.go.th.

18

ACCEPTED MANUSCRIPT Pelletier, N., Arsenault, N., Tyedmers, P., 2008. Scenario modelling potential eco-efficiency gains from a transition to organic agriculture: life cycle perspectives on Canadian Conola, corn, soy, and wheat production. Environ. Manage. 42, 989−1001.

Pfister, S., Koehler, A., Hellweg, S., 2009. Assessing the environmental impacts of freshwater consumption in

Pongpinyopap, S., Mungcharoen, T., 2015.

RI PT

LCA. Environ. Sci. Technol. 43(11), 4098−4104.

Bioethanol water footprint: life cycle optimization for water

reduction. Wa. Sci. Technol. 15(2), 395−403.

Papong, S., Malakul, P., Trungkavashirakun, R., Wenunun, P., Chom-in, T., Nithitanakul, M., Sarobol, E., 2013.

SC

Comparative assessment of the environmental profile of PLA and PET drinking water bottles from a life cycle perspective. J. Clean. Prod. 65, 539−550.

Pollution Control Department., 1996. http://www.pcd.go.th.

M AN U

Plastics Institute of Thailand., 2013. Plastics foresight. Available: www.thaiplastics.org.

Information of wastewater treatment in Thailand.

Available:

Roy, J.L., McGill, W.B., Lowen, H.A., Johnson, R.L., 2003. Relationship between water repellency and native

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and petroleum-derived organic carbon in soil. J. Environ. Qual. 32(2), 5 83−590.

Royal Irrigation Department., 2009. Crop coefficient (Kc). Available: http://water.rid.go.th.

Saibuatrong, W., 2008. A comparative study on net energy gain and life cycle environmental impact of main

EP

raw materials for ethanol production. Master Thesis, Kasetsart University, Thailand.

Suwanmanee, U., Varabuntoonvit, V., Chaiwutthinan, P., Tajan, M., Muncharoen, T., Leejarkpai, T., 2013. Life

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cycle assessment of single use thermoform boxes made from polystyrene (PS), polylactic acid (PLA) and PLA/starch: cradle to consumer gate. Int. J. Life Cycle Assess. 18(2), 401−417.

Tabone, M.D., Cregg, J.J., Beckman, E.J., Landis, A.E., 2010. Sustainability metrics: life cycle assessment and green design in polymers. Environ. Sci. Technol. 44, 8264−8269.

Tecchio, P., Freni, P., Benedetti, B.D., Fenouillot, F., 2016. Ex-ante life cycle assessment approach developed for a case study on bio-based polybutylene succinate. J. Clean. Prod. 112, 316−325.

Tsiropoulos, T., Faajj, A.P.C., Lundquist, L., Schenker, U., Briois, J.F., M.K., Patel., 2015. Life cycle impact assessment of bio-based plastics from sugarcane ethanol. J. Clean. Prod. 90, 114−127.

19

ACCEPTED MANUSCRIPT Taengwathananukul, S., Chidthaisong, A., Gheewala, S.H., Chiarakorn, S., Theinsathid, P., 2013. Environmental impact assessment of bioplastic and melamine-based coffee cup production. J. Sustain. Energ. Environ. 4, 103−111.

Thai Custom Department., 2012. Custom report. Available: http://www.customs.go.th/.

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Trading Economics., 2014. Inflation Rate. Available: http://www.tradingeconomics.com.

Vink, E.T.H., Glassner, D.A., Kolstad, J.J., Wooley, R.J. O’Connor, R.P., 2007. The eco-profile for current and near-future NatureWorks® polylactide (PLA) production. Ind. Biotechnol. 3(1), 58−81.

SC

US Environmental Protection Agency., 1992. Manual, guideline for water reuse. US Environmental Protection

M AN U

Agency. Washington, DC.

U.S. Government, Interagency Working Group on Social Cost of Carbon., 2013. Technical Support Document: − Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis − under Executive Order 12866. Available: http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcostof-carbon-for-regulator-impact-analysis.pdf.

Zhang, S., Feng X., Zhu, S., Huan, Q., Han, K., Ma, .Y, Yu, M., 2013. Novel toughening mechanism for

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Mater. Lett. 164, 238−241.

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polylactics acid (PLA)/starch blends with layer-like microstructure via pressure-induced flow (PIF) processing.

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ACCEPTED MANUSCRIPT Table 1: Materials including fertilizers, agrochemicals, and fuels input in sugarcane, corn, and cassava productions. Table 2: Water footprint results for water accounting stage in m3 per box. Table 3: Water Stress Index values used in LCA studies.

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Table 4: Assumptions used for calculation of CF emissions.

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Table 1: Materials including fertilizers, agrochemicals, and fuels input in sugarcane, corn, and cassava productions Sugarcane plantation

Corn plantation

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Items

Cassava plantation

Kanchanaburi

Sakaeo

Prachinburi

Chonburi

Kanchanaburi

Sakaeo

Prachinburi

Chonburi

Chonburi

Rayong

province

province

province

province

province

province

province

province

province

province

Fertilizer N

2.14×10

-3

8.13×10

-4

1.00×10

-3

5.13×10

-4

6.02×10-3

3.91×10-3

9.38×10-3

1.21×10-2

1.79×10

-2

3.30×10

-3

Fertilizer P

6.80×10

-4

8.67×10

-4

2.80×10

-4

4.67×10

-4

7.00×10-3

3.91×10-3

9.38×10-3

6.43×10-3

1.01×10

-3

1.40×10

-3

Fertilizer K

9.60×10

-4

6.00×10

-4

2.80×10

-4

6.02×10

-4

4.50×10

-3

1.63×10

-1

1.52×10

-4

1.43×10

-4

9.45×10

-5

-7

6.40×10

-5

1.35×10

-5

Diuron

−

9.52×10

Glyphosate

−

−

3.48×10-3

7.22×10

109.38

94.2

Diesel Yield (ton/ha)

− -4

2.83×10

-3

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-4

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1.41×10

−

78.13

3.91×10-3

9.38×10-3

6.43×10-3

1.01×10

-3

1.40×10

-3

5.00×10-1

−

−

−

2.94×10

-1

1.11×10

-1

1.25×10-3

8.79×10-4

2.55×10-4

−

2.09×10

-3

3.38×10

-3

−

−

−

−

−

1.54×10

-4

8.84×10

-5

7.12×10

-4

6.41×10

-5

5.83×10-3

−

−

−

−

1.41×10

-3

1.92×10-2

1.05×10-2

1.44×10-2

7.33×10-3

2.12×10

6.25

6.00

5.00

6.92

78.13

EP

Alachlor

−

3.00×10-3

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Poultry manure

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Input (kg/kg crop)

2

34.68

-3

54.68

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Table 2: Water footprint results for water accounting stage in m3 per box. PS

PLAS

PLAS/starch

PBS

Green WF

Blue WF

Grey WF

Green WF

Blue WF

Grey WF

Green WF

Blue WF

Grey WF

Direct (Kanchanaburi )

-

2.83×10-1

1.79×10-1

1.41×10-1

1.53×10-1

7.77×10-2

7.076×10-2

2.57×10-1

5.25×10-4

7.46×10-5

Indirect (Kanchanaburi )

-

-

7.65×10-3

-

-

6.77×10-1

0

-

-

4.85×10-3

-

6.82×10-1

0

-

-

7.37×10-3

-

5.92×10-1

-

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Blue WF

Direct (Chonburi) Indirect (Chonburi) Direct (Gulf of Thailand) Indirect (Gulf of Thailand)

-

3.50×10-2

-

1.99×10-1

3.23×10-1

0

9.57×10-2

2.54×10-1

0

1.22×10-5

-

-

4.65×10-3

-

-

2.26×10-3

-

1.98×10-1

3.25×10-1

0

9.53×10-2

3.01×10-1

0

2.05×10-5

-

-

5.74×10-3

-

-

1.02×10-2

-

0

1.64×10-1

2.86×10-1

0

8.05×10-2

2.48×10-1

0

1.59×10-5

-

7.88×10-2

-

-

3.66×10-2

-

-

2.85×10-3

-

-

-

-

-

-

-

-

-

-

-

6.90×10-3

-

-

-

-

-

-

-

-

-

-

3.52×10-3

-

-

2.41×10-5

-

-

3.95×10-4

-

-

3.26×10-3

-

-

1.13×10-3

-

-

2.66×10-4

-

-

-

3.03×10-3

-

-

1.52×10-3

-

-

2.13×10-4

-

-

-

1.13×10-3

-

-

1.41×10-3

-

-

8.88×10-5

-

2) Trans to pellet plant Indirect (Kanchanaburi ) Indirect (Sakaeo) Indirect (Prachinburi) Indirect Chonburi)

-

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Indirect (Prachinburi)

-

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Direct (Prachinburi)

5.86×10-3

EP

Indirect (Sakaeo)

-

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Direct (Sakaeo)

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1) Plantation and oil extraction

3

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3) Pellet production

Indirect

-

2.04×10-1

-3

-

2.05×10

-1

3.34×10-5

-

2.38×10-3 6.08×10-6

6.28×10

-

8.85×10-2

-

-

-1

1.53×10-4

-

-

-

5.72×10-3

-

-

2.90×10-5

-

4) Transportation to box forming

Indirect

Indirect

-

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Indirect

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6) Transportation to consumer

-

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5) Box forming manufacturing

4

8.08×10-4

-

-

-

9.51×10

0

-

-

-

1.22×10-4

-

4.17×10-3

-

-

6.35×10-3

-

1.71×10-5

-

-

2.18×10-5

-

1.15×10

9.02×10-5

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manufacturing

-

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Direct

-

-

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Table 3: Water Stress Index values used in LCA studies. Productions/Activities

Energy and chemicals used

Region/Related watershed

Cassava plantation

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Direct blue water during cassava, corn, and sugarcane plantation -

Corn and sugarcane plantations

-

Direct and indirect blue water from cradle-to-gate

Fertilizers, pesticide

Sulphur, water, and heavy oil Electricity

Water and steam

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Sugar production

EP

Starch production

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Diesel and poultry manure

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-

Plantation

Electricity Ca(OH)2 Bio-ethanol

Mae Klong

0.018a

Prachin Buri

0.022a

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-

Electricity Water and steam

5

WSI

Mae Klong

0.018a

Prachin Buri

0.022a

Bangpakong

0.026a

East-Coast Gulf

0.015a

East-Coast Gulf

0.015a

China

0.478b

East-Coast Gulf

0.015a

Pasak

0.050a

East-Coast Gulf

0.015a

Bangpakong

0.026a

Pasak

0.050a

Bangpakong

0.026a

East-Coast Gulf

0.015a

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PLA pellet production

NH4SO3

China

0.478b

Lime

Pasak

0.050a

East-Coast Gulf

0.015a

Bangpakong

0.026a

East-Coast Gulf

0.015a

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H2SO4, KOH Electricity

PBS pellet production

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Steam Natural gas, NaCl, water, and CO2

Gheewala et al. (2014) and bPfister et al. (2009)

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a

Diesel

EP

Transportation

Electricity

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Box forming

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Electricity

6

East-Coast Gulf

0.015a

Bangpakong

0.026a

Chao Phraya

0.339a

Bangpakong

0.026a

East-Coast Gulf

0.015a

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Table 4: Assumptions used for calculation of CF emissions. Emission source

Emission calculation

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GHG emission from crops

Reference

0.049 [kg CO2-eq./kg cassava] × kg cassava

Photosynthesis reaction during cassava plantation

1.416 [kg CO2-eq./kg corn] × kg corn

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Photosynthesis reaction during corn plantation

0.85 [kg CO2-eq./kg cassava] × kg sugarcane

Photosynthesis reaction during sugarcane plantation

Liu et al. (2012) Akiyama et al. (2003) De Figueiredo et al. (2010)

3.27 [kg CO2-eq./kg N] × kg fertilizer N

Ecoinvent 2.0

Fertilizer P

1.37 [kg CO2-eq./kg P] × kg fertilizer P

Ecoinvent 2.0

Fertilizer K

0.852 [kg CO2-eq./kg K] × kg fertilizer K

Ecoinvent 2.0

0.106 [kg CO2-eq./kg p.m.] × kg p.m.

Ecoinvent 2.0

7.39 [kg CO2-eq./kg a.l.] × kg a.l.

Ecoinvent 2.0

6.62 [kg CO2-eq./kg d.i.] × kg d.i.

Ecoinvent 2.0

13.2 [kg CO2-eq./kg g.l.] × kg g.l.

Ecoinvent 2.0

0.328 [kg CO2-eq./kg fuel] × kg fuel (production)

MTEC (2009)

3.224 [kg CO2-eq./kg fuel] × kg fuel (mobile)

IPCC (2006)

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Fertilizer N

Glyphosate (g.l.) Diesel

N2O−N GHG emission from intermediate production Electricity

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Diuron (d.i.)

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Alachlor (a.l.)

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Poultry manure (p.m.)

0.01 [kg N2O-N/kg N] × kg fertilizer-N × 298 [kg CO2-eq./ kg N2O-N]

0.561 [kg CO2-eq./kWh] × kWh

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IPCC (2006); IPCC (2007)

MTEC (2009)

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0.00016 [kg CO2-eq./kg fuel] × kg fuel (production)

Natural gas

Varabuntoonvit (2008) IPCC (2006)

Steam

0.233 [kg CO2-eq./kg steam] × kg steam

Ecoinvent 2.0

Water

0.0003 [kg CO2-eq./kg water] × kg water

Ecoinvent 2.0

H2SO4

0.12 [kg CO2-eq./kg H2SO4] × kg H2SO4

Ecoinvent 2.0

KOH

0.852 [kg CO2-eq./kg KOH] × kg KOH

Ecoinvent 2.0

Lime

0.83 [kg CO2-eq./kg Lime] × kg Lime

Ecoinvent 2.0

2.66 [kg CO2-eq./kg NH4SO3] × kg NH4SO3

Ecoinvent 2.0

NaCl

0.167 [kg CO2-eq./kg NaCl] × kg NaCl

Ecoinvent 2.0

CO2

0.768 [kg CO2-eq./kg CO2] × kg CO2

Ecoinvent 2.0

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3.0324 [kg CO2-eq./kg fuel] × kg fuel (mobile)

AC C

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TE D

NH4SO3

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Figure captions Figure 1: The system boundary for a PLAS box.

Figure 3: The system boundary for a PBS box. Figure 4: The water deprivation of PS, PLAS, PLAS/starch, and PBS boxes. Figure 5: The CF of PS, PLAS, PLAS/starch, and PBS boxes.

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Figure 2: The system boundary for a PLAS/starch box.

Figure 6: The externality cost of water footprint for PS, PLAS, PLAS/starch, and PBS boxes.

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Figure 7: The externality cost of carbon footprint for PS, PLAS, PLAS/starch, and PBS boxes.

Figure 8: The total externality cost for PS, PLAS, PLAS/starch, and PBS boxes: (a) the total externality cost of the

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contribution of each process and (b) the total externality cost of the contribution of carbon and water footprints

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Figure 1: The system boundary for a PLAS box.

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Figure 2: The system boundary for a PLAS/starch box.

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Figure 3: The system boundary for a PBS box.

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Figure 4: The water deprivation potential of PS, PLAS, PLAS/starch, and PBS boxes.

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Figure 5: The CF of PS, PLAS, PLAS/starch, and PBS boxes.

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Figure 6: The externality cost of water footprint for PS, PLAS, PLAS/starch, and PBS boxes.

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Figure 7: The externality cost of carbon footprint for PS, PLAS, PLAS/starch, and PBS boxes.

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(a)

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(b)

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Figure 8: The total externality cost for PS, PLAS, PLAS/starch, and PBS boxes: (a) the total externality cost of the

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contribution of each process and (b) the total externality cost of the contribution of carbon and water footprints.

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