Biopolymers, Facts and Statistics 2019 – 3 BIOPLASTICS 2 Process Routes Old Economy New Economy

Biopolymers facts and statistics 2019

Production capacities, processing routes, feedstock, land and water use 1 Introduction and background 3 1 Introduction and background 2 Process routes, feedstock, land and water use 5 The IfBB – Institute for Bioplastics and Biocomposites is a re Glossary 6 search institute within the Hochschule Hannover, University of 2.1 Bio-based polyesters 8 Applied Sciences and Arts. The IfBB was established in 2011 af 2.1.1 Polylactic acid (PLA) 8 ter more than a decade of on-going research activities in the field 2.1.2 Polyhydroxybutyrate (PHB) 10 of bioplastics to respond to the growing need for expert knowled ge in this area. With its practice-oriented research and its 2.1.3 Polybutylene succinate (PBS) 12 collaboration with industrial partners, the IfBB is able to shore 2.1.4 Polybutylene succinate adipate (PBSA) 15 up the market for bioplastics and, in addition, foster unbiased 2.1.5 Polytrimethylene terephthalate (PTT) 18 public awareness and understanding of the topic. 2.1.6 Polyethylene terephthalate (Bio-PET) 21 As an independent research-led expert institution for biopla 2.2 Bio-based polyolefins 24 stics, the IfBB is willing to share its expertise, research findings 2.2.1 Polyethylene (Bio-PE) 24 and data with any interested party via online and offline publi cations or at fairs and conferences. In carrying on these efforts, 2.3 Bio-based polyamides (Bio-PA) 26 substantial information regarding market trends, processes and 2.3.1 Homopolyamides 26 resource needs for bioplastics are being presented here in a 2.3.1.1 Bio-PA 6 26 concise format, in addition to the more detailed and compre hensive publication “Engineering Biopolymers”1. If figures or 2.3.1.2 Bio-PA 11 28 data from this or other publication of IfBB is being used, we 2.3.2 Copolyamides 29 kindly ask any person or institution to quote IfBB's authorship. 2.3.2.1 Bio-PA 4.10 – Bio-PA 5.10 – Bio-PA 6.10 29 2.3.2.2 Bio-PA 10.10 30 One of our main concerns is to furnish a more rational basis for discussing bioplastics and use fact-based arguments in the pub 2.4 Polyurethanes 32 lic discourse. Furthermore, “Biopolymers, facts and statistics” 2.5 Polysaccharide polymers 34 aims to easily and quickly provide specific, qualified answers 2.5.1 Cellulose-based polymers 34 for decisionmakers in particular from public administration and the industrial sector. Therefore, this publication is made up like 2.5.1.1 Regenerated cellulose 34 a set of rules and standards and largely foregoes textual detail. 2.5.1.2 Cellulose diacetate 35 It offers extensive market-relevant and technical facts presen 2.5.2 Starch-based polymers 37 ted in graphs and charts, which means that the information is much easier to grasp. The reader can expect comparative 2.5.2.1 Thermoplastic starch (TPS) 37 market figures for various materials, regions, applications, 2.5.2.2 Starch blends 38 process routes, agricultural land use or resource consumption, production capacities, geographic distribution, etc. 3 Market data and land use facts 40 In recent years, many new types of bioplastics have emerged 3.1 New Economy bioplastics global production capacities 42 and innovative polymer materials are pushing into the plastics 3.2 New Economy bioplastics production capacities by material type 43 market. All the same, bioplastics by no means constitute a 3.3 New Economy bioplastics production capacities by region 44 completely new class of materials but rather one that has been rediscovered from among the large group of plastic materials. 3.4 New Economy bioplastics production capacities by market segment 45 3.5 Land use for new Economy bioplastics 2018 and 2023 46 1 Cf. Endres, Hans-Josef; Siebert-Raths, Andrea: Engineering Biopolymers. Markets, Manufacturing, Properties and Applications. München 2011

Biopolymers, facts and statistics 2019 – 3 BIOPLASTICS 2 Process routes Old Economy New Economy

Chemical novel Drop-ins Rubber Process routes depict the manufacturing steps from the raw material PLA Bio-PA Regenerated cellulose PHA Bio-PE to the finished product, specifying the individual process and con Cellulose acetates PEF Bio-PET versation steps, intermediate products, and input-output streams. Linoleum Starch blends Bio-PP So they serve as a guide for all considerations and calculations etc. etc. etc. around the production of bioplastics, in particular also with regard to their resource consumption. The first man-made polymer materials were all based on modi fied natural materials (e.g., casein, gelatine, shellac, celluloid, The following methodical approach was chosen to establish the cellophane, linoleum, rubber, etc.). That means they were process routes: bio-based since petrochemical materials were not yet available at that time. Ever since the middle of the 20th century, these The mass flows were first calculated without assuming allocations early bio-based plastics, with a few exceptions (cellulose and (especially no feedstock allocation) and using a molar method rubber-based materials), have almost been replaced by petro based on the chemical process, with the introduction of known rates chemical materials. and conversion factors. The routes so established were confirmed with polymer manufacturers and the industry. In so far as no loss rates By now, due to ecological concerns, limited petrochemical re due to the chemical processes or the process stages were included, sources and sometimes new property profiles, bioplastics have the calculations were made basically assuming no losses. The mass undergone a remarkable revival and are taken more and more flows show feedstocks and resulting and requirements in hectare into focus by the general public, politics, the industrial sector (ha) or the production of one metric ton (t) of bioplastics. Feedstock and in particular the research community. requirements were calculated for the use of different crops. Yields of the most important crops and renewable raw materials used for Of particular interest today are new types of bioplastics, which feedstocks are shown in the chart below on page 6. were developed in the past 30 years. The publication presen ted here refers to the socalled “New Economy” bioplastics as Please note that the yields in this context refer to the crop itself, opposed to “Old Economy” bioplastics which indicate earlier which contains the raw material for processing, and not to the materials developed before petrochemical bioplastics emerged, harvested whole plant. yet still exist on the market today (e.g., rubber, cellophane, viscose, celluloid, cellulose acetate, linoleum). The conservative calculation used in this publication delivers a resilient approach for adjustments to be made out of invididual “New Economy” bioplastics divide up into two main groups. On needs. the one hand, there are those biopolymers which have a new chemical structure virtually unknown in connection with pla For calculating water use data, information on water use for different stics until a few years ago (e.g. new bio-based polyesters such raw materials originally collected by the ‘Water Footprint Network’ has as PLA), on the other hand socalled “drop-ins”, with the same been used. It is based on FAOSTAT crop definitions (Food and Agricul chemical structure yet bio-based. The most prominent drop- ture Organization of UN) which are also used for land use calculations. ins at this point are bio-based PET (Bio-PET) and bio-based This means, water use is only available from “seed to market place”. polyethylene (Bio-PE). Only water, such as rainwater, irrigation and to somewhat extent pro cess water to clean agricultural products, e.g., used/needed to grow the whole plant is included here. On the other side the water use for the processing like polymerization is neglected. This is part of an ongoing research, but this first simplified approach gives a good indication and delivers first data to the issue of water use of bioplastics.

4 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 5 Feedstock Crop Raw Global mean yield * Average content Resulting amount Sample process route material (Crop) of raw material (raw material) Calculations -> x -> = Sugar cane fermt. select desired feedstock/crop, i.e. Sugar cane (without cane 72.7 t/ha 13 % 9.46 t sugar/ha Sugar sugar cane or sugar beet tops) land use for 1 t of Beet fermt. resulting polymer Sugar beet 57.8 t/ha 16 % 9.24 t sugar/ha (without leaves) Sugar 0.09 ha 0.09 ha water usage for Corn Maize kernel Starch 6.7 t/ha 70 % 4.69 t starch/ha 1 387 m³ 711 m³ feedstock/crop amount Potatoes Potato tuber Starch 22.2 t/ha 18 % 4.0 t starch/ha feedstock/crop Sugar Sugar Wheat Wheat grains Starch 3.74 t/ha 46 % 1.72 t starch/ha cane or beet Standing timber, 0.66 t cellulose/ 6.62 t 5.37 t Wood Cellulose 1.64 t atro/ha 40 % residual wood ha

1.28 t seeds/ha 0.51 t oil/ha Castor oil Castor bean Castor oil (given one harvest 40 % (given one plant (seeds) Sugar per year) harvest per year) raw material 0.86 t * Global mean yield over the last 10 years, weighted by respective production amount (based on FAOSTAT 2005 - 2014). (chemical) process

H2O CO2 process inputs Fermentation Glossary Microorg.

H2O Filtration Abbreviations used: Microbial atro = bone dry mass bb = bio-based Succinic BDO = Butanediol intermediate product acid* DMDA = Decamethylene diamine 0.69 t fermt. = fermentable process outputs ha = hectare = 0,01 km2 1,4-BDO H2O HMDA = Hexamethylene diamine Esterification 0.52 t 0.10 t m3 = cubic metres = 1 000 litres resource has petro-based origin H2O MEG = Monoethylene glycol Polycondensation PDO = Propanediol 0.10 t PMDA = Pentamethylene diamine PTA = Purified terephthalic acid PBS SCA = Succinic acid bb SCA resulting polymer t = metric ton = 1 000 kg 1.00 t TMDA = Tetramethylene diamine red coloured resources have a petro-based origin

A large amount of additional information is also available at: www.ifbb-hannover.de.

6 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 7 PLA – Feedstock requirements in t (different feedstocks)

12 11.31

PLA10 – Feedstock requirements in t (different feedstocks)9.19 9.26

8 12 PLA –11.31 Feedstock requirements in t

t biopolymer (diff erent feedstocks) PLA/ –6 Feedstock requirements in t 2.1 Bio-based polyesters 10(different feedstocks) 9.19 9.26 oc k 12 4 11.31 8 3.54 eeds t

t f 2.39 2.1.1 Polylactic acid (PLA) 10 t biopolymer 2 9.19 9.26

/ 6 0.44 ha oc k 8 2 659 m³ 0 4 Sugar Sugar Corn Potato Wheat3.54 eeds t

t biopolymer cane beet / t f 6 2.39 Potato 2 oc k 0.16 ha 0.18 ha 0.37 ha 1.04 ha 4 3.54 9.26 t

eeds t 0 2 370 m³ 1 215 m³ 2 921 m³ 6 468 m³ t f Sugar Sugar Corn2.39 Potato Wheat PLA2 –cane Land usebeet in ha Sugar Sugar (different feedstocks) Corn Wheat 0 cane or beet or Sugar Sugar Corn Potato Wheat 11.31 t 9.19 t 2.39 t 3.54 t cane beet PLA – Land use in ha 1.04 1.0 (different feedstocks) PLA – Land use in ha (diff erent feedstocks) Sugar Starch 0.8 PLA – Land use in ha 1.04 1.47 t 1.67 t 0.6(different feedstocks) 1.0 0.44

H2O CO2 H2O H2O t biopolymer 0.4 0.37 Hydrolysis 0.8 Fermentation 1.04 Microorg. Enzymes Dextrins ha / 1.0 0.2 0.16 0.18 0.6

Lactic * 0.8 0.44 Glucose 0 acid* t biopolymer 0.4 Sugar Sugar Corn0.37 Potato Wheat

ha / cane beet 1.25 t 1.47 t 0.6 0.2 0.16 0.18 0.44

t biopolymer 0.4 0.37 H2O H2O CO2 Dehydration Fermentation 0 Microorg. ha / Sugar Sugar Corn Potato Wheat 0.2PLA –cane0.16 Water usebeet0.18 in m3 (different feedstocks) Lactide Lactic 0 acid* Sugar Sugar Corn3 Potato Wheat PLA – Watercane usebeet in m (diff erent feedstocks) 1.00 t 1.25 t 3 PLA – Water use in m 6 468 (different feedstocks) Catalyst H2O 6 000 Polymerization Dehydration

3 5 000PLA – Water use in m (different feedstocks) 6 468 PLA Lactide 4 000 6 000 1.00 t 1.00 t 3 000 2 921 5 000 2 659 6 468 2 370 t biopolymer / Catalyst 3 6 000 2 000 m * Conversion Conversion rates: rates: Polymerization 4 000 fermt. fermt. Sugar – –Lactic Lactic acid acid 85 % 85 % 1 215 Starch – Glucose 90 % 5 000 1 000 Starch – Glucose 90 % 3 000 2 921 2 659 2 370 t biopolymer

PLA / 4 000 3 0 2 000 m Sugar Sugar Corn Potato Wheat

1.00 t 3 000 cane 1beet 215 2 921 2 659 1 000 2 370 t biopolymer / 8 – Biopolymers, facts and statistics 2019 3 Biopolymers, facts and statistics 2019 – 9 2 000 m 0 Sugar Sugar1 215 Corn Potato Wheat 1 000 cane beet

0 Sugar Sugar Corn Potato Wheat cane beet PHB– Feedstock requirements in t (differentPHB– Feedstock feedstocks) requirements in t (different feedstocks) 30 2.1.2 Polyhydroxybutyrate (PHB) PHB – 30PHB–Feedstock Feedstock requirements requirements in t in t (diff erent feedstocks) 25(different feedstocks) 25 22.00 0.81 ha 30 22.00 20 17.87 18.00 5 168 m³ 20 17.87 18.00 25 t biopolymer

/ 15 22.00 t biopolymer

/ 15 oc k Potato 20 oc k 10 17.87 18.00

eeds t 10 7.04 t biopolymer

0.30 ha 0.31 ha 0.69 ha 1.88 ha t f eeds t 18.00 t / 15 7.04 5 4.63 4 610 m³ 2 364 m³ 5 655 m³ 12 867 m³ t f oc k 5 4.63 10 0 eeds t Sugar Sugar Corn Potato Wheat7.04 Sugar Sugar t f 0 Corn Wheat Sugar Sugar Corn4.63 Potato Wheat or 5 cane beet cane or beet cane beet

22.00 t 17.87 t 4.63 t 7.04 t 0 Sugar Sugar Corn Potato Wheat cane beet PHB – Land use in ha (differentPHB – Land feedstocks) use in ha Sugar Starch (different feedstocks) PHB –2.4 Land use in ha (diff erent feedstocks) 2.86 t 3.24 t 2.4PHB – Land use in ha 2.0(different feedstocks) 1.88 2.0 1.88 H2O CO2 H2O H2O 2.4 Fermentation Hydrolysis 1.6 Microorg. Enzymes Dextrins 1.6 2.0 1.88 1.2 1.2

Isolation of t biopolymer * 1.6 0.81 biopolymers H2O t biopolymer 0.8 Glucose ha / 0.69 0.81 0.8 Microbial ha / 0.69 1.2 mass 2.86 t 0.4 0.30 0.31 t biopolymer Compounding 0.4 0.30 0.31 0.81 0.8 ha / 0.69 and granulation 0 H2O CO2 Fermentation 0 Sugar Sugar Corn Potato Wheat Microorg. 0.4 Sugarcane0.30 Sugarbeet0.31 Corn Potato Wheat cane beet

PHB* 0 Isolation of Sugar Sugar Corn Potato Wheat H O biopolymers 2 PHB–cane Water usebeet in m3 1.00 t 3 Microbial PHB – (differentPHB–Water Water feedstocks) use use inin m 3m (diff erent feedstocks) mass (different feedstocks) Compounding 3 and granulation PHB– Water use in m 12 867 12 867 12 000(different feedstocks) 12 000

* 10 000 12 867 PHB 10 000 12 000 1.00 t 8 000 8 000 10 000 6 000 5 655 5 168 6 000 5 655 t biopolymer 4 610 5 168 / 8 000 3 t biopolymer 4 000 4 610 m / * Conversion rates: 3 4 000

* Conversion rates: m 2 364 Starch – Glucose 90 % 6 000 5 655 Starch fermt. Sugar – Glucose – PHB 35 90 % % 2 000 2 364 5 168

t biopolymer 4 610 2 000 fermt. Sugar – PHB 35 % / 3 4 000 m 0 0 Sugar Sugar2 364 Corn Potato Wheat 2 000 Sugarcane Sugarbeet Corn Potato Wheat cane beet

0 10 – Biopolymers, facts and statistics 2019 Sugar Sugar Corn Potato BiopolymersWheat , facts and statistics 2019 – 11 cane beet 2.1.3 Polybutylene succinate (PBS) 2.1.3 Polybutylene succinate (PBS) with biobased succinic acid (PBS bb SCA) 100 % biobased (PBS 100)

0.24 ha 0.49 ha 1 548 m³ 3 111 m³

Potato Potato 0.09 ha 0.09 ha 0.21 ha 0.56 ha 0.18 ha 0.19 ha 0.42 ha 1.13 ha 5.43 t 10.83 t 1 387 m³ 711 m³ 1 693 m³ 3 853 m³ 2 757 m³ 1 414 m³ 3 404 m³ 7 746 m³

Sugar Sugar Corn Wheat Sugar Sugar Corn Wheat cane or beet or cane or beet or 6.62 t 5.37 t 1.39 t 2.11 t 13.15 t 10.69 t 2.79 t 4.24 t

Sugar Starch Sugar Starch

0.86 t 0.97 t 1.71 t 1.95 t

H2O CO2 H2O H2O H2O CO2 H2O H2O Fermentation Hydrolysis Fermentation Hydrolysis Microorg. Enzymes Dextrins Microorg. Enzymes Dextrins

H2O H2O Filtration Filtration Microbial Glucose* Microbial * mass mass Glucose

0.86 t 1.71 t Succinic 0.685 t Succinic acid* acid* H O 2 CO2 LiAlH H2O CO2 0.69 t Fermentation 4 Deoxidation 1.37 t Fermentation Microorg. Microorg. H2O

1,4-BDO H2O H2O H2O Esterification Filtration Filtration 0.52 t 0.10 t Microbial 1,4-Bu- 0.685 t Microbial mass tanediol mass H2O Polycondensation 0.52 t 0.10 t Succinic 0.685 t Succinic H2O acid* Esterification acid* 0.10 t 0.69 t LiAlH4 PBS Deoxidation 1.37 t H2O H2O bb SCA Polycondensation 1,4-BDO H2O 0.10 t 1.00 t Esterification 0.52 t 0.10 t 1,4-Bu- 0.685 t tanediol H2O PBS Polycondensation 0.10 t 100 0.52 t H2O 1.00 t Esterification 0.10 t ** Conversion Conversion rates: rates: PBS Starch – Glucose 90 % Starch – Glucose 90 % H2O fermt. Sugar – Succinic acid 80 % bb SCA Polycondensation fermt. Sugar – Succinic acid 80 % 1.00 t 0.10 t

* Conversion *rates: Conversion rates: Starch – Glucose 90 % PBS fermt. Sugar – StarchSuccinic – acid Glucose 80 % 90 % 100 fermt. Sugar – Succinic acid 80 % 1.00 t

12 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 13 PBS variations – Feedstock requirements in t (different feedstocks)

PBS 100 PBS variations14PBS variations – Feedstock Feedstock requirements requirements in t in t (diff erent feedstocks) 2.1.4 Polybutylene succinate adipate (PBSA) PBS variations – Feedstock requirements in13.15 t (different feedstocks) (different feedstocks) 12 with biobased succinic acid (PBSA bb SCA) 10.83 14 10.69 PBS 100 14 13.15 PBS 100 10 13.15 0.14 ha PBS bb SCA 12 878 m³ 12 8 10.69 10.83 10.69 10.83 10 6.62

t biopolymer 10

/ 6 PBS bb SCA Potato 5.37 PBS bb SCA 5.43 oc k 8 4.24 0.05 ha 0.05 ha 0.12 ha 0.32 ha 8 4 3.06 t 6.62 790 m³ 405 m³ 960 m³ 2 185 m³

eeds t 6.62 t biopolymer 2.79

/ 6 t f t biopolymer 5.37 5.43 2.11

/ 6 2 5.37 5.43

oc k 1.39 4.24

oc k Sugar Sugar 4 4.24 Corn Wheat 4 cane or beet or eeds t 0 2.79 eeds t t f Sugar Sugar Corn Potato Wheat2.11 Sugar Sugar Corn2.79 Potato Wheat 3.77 t 3.06 t 0.79 t 1.20 t t f 2 cane beet 1.39 2.11 cane beet 2 1.39

0 PBS0 Sugar variationsSugar – CornLand usePotato in haWheat Sugar Sugar Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat (differentcane feedstocks)beet cane beet Sugar Starch

0.49 t 0.55 t PBS variations – Land use in ha PBS 100 PBS variationsPBS variations – Land Land use inuse ha in ha (diff erent feedstocks) 1.2(different feedstocks) 1.13 H2O CO2 H2O H2O (different feedstocks) Fermentation Hydrolysis Microorg. Enzymes Dextrins 1.0 PBS 100 PBS 100 H2O 1.2 Filtration 1.2 1.13 0.8 1.13 Microbial Glucose* PBS bb SCA mass 1.0 1.0 0.56 0.6 0.49 t 0.49 Succinic t biopolymer 0.8 0.42 * 0.8 acid 0.4 ha / PBS bb SCA H2O CO2 PBS bb SCA 0.56 0.39 t Fermentation 0.6 0.24 0.56 Microorg. 0.6 0.21 0.19 0.2 0.18 0.49 t biopolymer 0.42 0.49 t biopolymer 0.09 0.09 1,4-BDO: 0.30 t H2O H2O 0.4 0.42 ha / Esterification Filtration 0.4 ha / Adipic acid: 0.48 t 0.12 t 0 0.24 Microbial Sugar Sugar Corn0.21 Potato0.24 Wheat Sugar Sugar0.19 Corn Potato Wheat mass 0.2 0.18 cane beet 0.21 cane0.18 beet0.19 H2O 0.2 0.09 0.09 Polycondensation 0.09 0.09 0.06 t Succinic 0 * 0 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat acid Sugarcane Sugarbeet Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat 0.39 t PHB–cane Waterbeet use in m3 cane beet PBSA (different feedstocks) bb SCA 1,4-BDO: 0.30 t H2O 1.00 t Esterification Adipic acid: 0.48 t 0.12 t 12 000PHB– Water use in m3 3 PBS variationsPHB– Water use – in Water m3 use in m (diff erent feedstocks) (different feedstocks) H2O (different feedstocks) Polycondensation 10 000 0.06 t 12 000 PBS 100 12 000 7 746 8 000 * Conversion* Conversion rates: rates: Starch – Glucose 90 % PBSA 10 000 Starch – Glucose 90 % 10 000 fermt. Sugar – Succinic acid 80 % bb SCA 6 000 fermt. Sugar – Succinic acid 80 % PBS bb SCA PBS 100 1.00 t t biopolymer 8 000 PBS 100 7 746 /

3 8 000 3 853 7 746 4 000 m 3 404 3 111 2 757 6 000 6 000 2 000 PBS1 bb 693 SCA1 548 t biopolymer 1 387 1 414

/ PBS bb SCA t biopolymer 3 711 3 853 / 4 000 m 3 3 853 3 404 4 000 3 111 m 3 404 0 2 757 3 111 Sugar Sugar Corn Potato Wheat Sugar2 757 Sugar Corn Potato Wheat 1 693 2 000 cane1 387 beet 1 548 cane 1beet 414 2 000 1 693 1 548 1 387 711 1 414 711 14 – Biopolymers0 , facts and statistics 2019 Biopolymers, facts and statistics 2019 – 15 0 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat cane beet cane beet PBSA variations – Feedstock requirements in t (different feedstocks)

PBSA bb SCA/BDO

8 7.54 PBSA variationsPBSA – Feedstockbb SCA requirements in t (different feedstocks) 6.13 6.17 6

t biopolymer 3.77 / PBSA4 variations – Feedstock requirements in t 3.06 3.06 PBSA bb SCA/BDO 2.1.4 Polybutylene succinate adipate (PBSA) PBSAoc k variations(different feedstocks) – Feedstock requirements in t (diff2.41 erent feedstocks) 82 7.54 1.59 1.20 eeds t PBSA0.79 bb SCA

with biobased succinic acid and 1,4butanediol (PBSA bb SCA/BDO) t f 6.13 6.17 60 Sugar Sugar Corn Potato Wheat Sugar SugarPBSA bbCorn SCA/BDOPotato Wheat cane beet cane beet 0.28 ha t biopolymer 8 3.77 7.54 / 4 3.06 1 771 m³ PBSA bb SCA3.06 oc k 6.13 6.17 2.41 6 2 1.59 1.20

eeds t 0.79 t biopolymer t f 3.77 Potato / 4 0 Sugar Sugar3.06 Corn Potato3.06 Wheat Sugar Sugar Corn Potato Wheat 0.10 ha 0.11 ha 0.24 ha 0.64 ha oc k 2.41 6.17 t PBSA2 cane variationsbeet – Land use in ha cane beet 1.59 1 580 m³ 810 m³ 1 938 m³ 4 409 m³ 1.20 eeds t (different feedstocks)0.79 t f 0 Sugar Sugar Corn Wheat Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat cane beet or cane beet cane beet or PBSA bb SCA/BDO 7.54 t 6.13 t 1.59 t 2.41 t 0.8 PBSA variationsPBSA – Landbb SCA use in ha 0.64 PBSA 0.6variations(different feedstocks) – Land use in ha (diff erent feedstocks)

Sugar Starch 0.4 0.32 PBSA variations – Land use in ha 0.28 t biopolymer PBSA bb0.24 SCA/BDO 0.98 t 1.11 t 0.80.2(different feedstocks) ha / 0.14 0.12 0.10 0.11 0.05 0.05PBSA bb SCA 0.64 H2O CO2 H2O H2O Fermentation Hydrolysis 0.60 Microorg. Enzymes Dextrins Sugar Sugar Corn Potato Wheat Sugar SugarPBSA bbCorn SCA/BDOPotato Wheat 0.8 cane beet cane beet 0.4 H2O 0.32 Filtration 0.28 0.64 t biopolymer PBSA bb SCA 0.24 Microbial * 0.6 Glucose 0.2 mass ha / 0.14 0.12 0.10 0.11 0.05 0.05 0.89 t 0.4 Succinic 0 0.32 0.39 t Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato0.28 Wheat acid* t biopolymer 0.24 H2O 0.2 cane beet cane beet LiAlH CO2 ha / 0.14 4 Deoxidation 0.78 t Fermentation 0.12 0.10 0.11 Microorg. 0.05 0.05 H2O PBSA variation – Water use in m3 0 H2O (differentSugar feedstocks)Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat Filtration cane beet cane beet 1,4-Bu- 0.39 t Microbial PBSA variations – Water use in m3 (diff erent feedstocks) tanediol mass 0.30 t 0.39 t Succinic H2O acid* 3 PBSA bb SCA/BDO Esterification 5 000PBSA variation – Water use in m Adipic acid: LiAlH 0.12 t 4 0.78 t (different feedstocks) 4 409 0.49 t Deoxidation H2O H2O 4 000 Polycondensation 0.06 t PBSA variationPBSA – Water bb SCA use in m3 1,4-Bu- 0.39 t 3 000(different feedstocks) tanediol 2 185 PBSA bb SCA/BDO PBSA bb 1 938 0.30 t t biopolymer 52 000 1 771 / 1 580 SCA/BDO 3 4 409 H2O m 1.00 t Esterification 960 878 Adipic acid: 41 000 790 810 0.12 t 405 0.49 t PBSA bb SCA PBSA bb SCA/BDO 5 000 H2O 3 0000 Polycondensation Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat4 409 0.06 t cane beet 2 185 cane beet 4 000 1 938

t biopolymer 2 000 1 771

/ 1 580 * Conversion rates: 3 PBSA bb SCA m Starch – Glucose 90 % PBSA bb 3 000 960 * Conversion rates: 1 000 790 878 810 fermt. Sugar – Succinic acid 80 % Starch – Glucose 90 % SCA/BDO 405 2 185 1 938 fermt. Sugar – Succinic acid 80 % 1.00 t t biopolymer 2 000 1 771 / 0 1 580 3 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat m 960 1 000 cane790 beet 878 cane beet810 405

16 – Biopolymers, facts and statistics 2019 0 Biopolymers, facts and statistics 2019 – 17 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat cane beet cane beet 2.1.5 Polytrimethylene terephthalate (PTT) 2.1.5 Polytrimethylene terephthalate (PTT) with biobased 1,3propanediol (PTT bb PDO) 100 % biobased (PTT 100) 0.26 ha 0.81 ha 5 185 m³ 1 659 m³ Potato 0.30 ha 0.31 ha 0.69 ha 1.89 ha 18.06 t Potato 4 612 m³ 2 364 m³ 5 673 m³ 12 908 m³

0.10 ha 0.10 ha 0.22 ha 0.60 ha Sugar Sugar Corn Wheat 5.78 t cane or beet or 1 483 m³ 761 m³ 1 816 m³ 4 131 m³ 22.00 t 17.88 t 4.64 t 7.07 t fermt. * Starch Sugar Sugar Sugar Corn Wheat 2.86 t 3.25 t cane or beet or H2O H2O Hydrolysis 7.08 t 5.75 t 1.49 t 2.26 t Enzymes Dextrins

Glucose*

2.86 t

Sugar Starch or 0.92 t 1.94 t 0.92 t 1.04 t H2O CO2 H2O CO2 Fermentation Fermentation Microorg. Microorg.

H2O CO2 H2O H2O H2O H2O Fermentation Hydrolysis Filtration Filtration Stillage Stillage Microorg. Enzymes Dextrins

Iso- H2O Filtration butanol* Stillage Glucose* 0.76 t

H2O: 0.18 t Dehydration Other: 0.04 t 1,3-Pro- 0.92 t panediol* Iso- H2O CO2 butene 0.37 t Fermentation Microorg. 0.54 t

Dimerization PTA H2O H2O Esterification Filtration 0.80 t 0.09 t Stillage Iso- H2O octene Polycondensation 0.54 t 0.09 t 1,3-Pro-

* H2SO4 H2O: 0.26 t panediol Dehydrogenation 1.43 t H2SO4: 1.26 t PTT 0.37 t Para- bb PDO Xylene1 PTA H2O 1.00 t Esterification 0.51 t 0.80 t 0.09 t KMnO4 KOH: 1.09 t Oxidation 3.07 t MnO : 1.69 t H2O 2 Polycondensation 0.09 t 1,3-Pro- Bio-PTA panediol*

0.37 t H2O 0.81 t * Conversion rates: Esterification * Conversion rates: 0.09 t Starch – Glucose 90 % PTT Starch – Glucose 90 % H2O fermt. Sugar – 1,3Propanediol 40 % bb PDO Polycondensation fermt. Sugar – 1,3-Propanediol 40 % 1 GEVO-Process 0.09 t 1.00 t * Conversion rates: Starch – Glucose* Conversion 90 % rates: fermt. Sugar Starch– 1,3Propanediol – Glucose 90 % 40 % fermt. Sugar – 1,3-Propanediol 40 % PTT 100 Glucose – IsobutanolGlucose – Isobutanol39 % 39 % 1.00 t

18 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 19 PTT variations – Feedstock requirements in t (different feedstocks)

PTT 100 25 22.00

20 PTT variations – Feedstock requirements in t 17.88 18.06 (different feedstocks) PTT variations15PTT variations –– FeedstockFeedstock requirements requirements in t in t (diff erent feedstocks) 2.1.6 Polyethylene terephthalate (Bio-PET) t biopolymer PTT bb PDO / (different feedstocks) PTT 100 10 with biobased ethanol (BioPET 30) oc k 25 7.08 22.00 7.07 5.75 5.78 PTT 100 0.21 ha

eeds t 25 4.64 205 1 356 m³ t f 2.26 22.00 17.88 18.06 1.49 20 Potato 150 17.88 18.06 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0.08 ha 0.08 ha 0.18 ha 0.49 ha t biopolymer PTT bb PDO 4.72 t / 15 cane beet cane beet 1 193 m³ 612 m³ 1 484 m³ 3 376 m³ 10 oc k t biopolymer PTT bb PDO / 7.08 7.07 Sugar Sugar Corn Wheat 10 5.75 5.78 cane or beet or oc k eeds t 5 4.64

t f 7.08 7.07 5.69 t 4.63 t 1.21 t 1.85 t 2.26 5.75 1.49 5.78 eeds t PTT5 variations – Land use in ha 4.64 t f 0 2.26 (differentSugar feedstocks)Sugar Corn1.49 Potato Wheat Sugar Sugar Corn Potato Wheat Sugar Starch 0 cane beet cane beet Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0.74 t 0.85 t cane beet cane beet PTT 100

H2O CO2 H2O H2O 2.0 Fermentation Hydrolysis 1.89 Yeast Enzymes Dextrins

H2O PTT variations – Land use in ha Filtration PTT variations1.6 – Land use in ha (diff erent feedstocks) Stillage * (different feedstocks) Glucose PTT variationsPTT – Land bb PDO use in ha 0.75 t 1.2(different feedstocks) Ethanol*

PTT 100 H2O CO2 0.81 0.36 t Fermentation Yeast

t biopolymer 0.8 0.69 2.0 0.60 PTT 100 1.89 H2O H2O

ha / Dehydration Filtration 2.0 1.89 0.11 t Stillage 0.41.6 0.30 0.31 0.22 0.26 PTT bb PDO 0.10 0.10 * * 1.6 Ethene Ethanol 0 1.2 PTT bb PDO Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0.17 t 0.36 t 1.2 cane beet cane beet 0.81

t biopolymer 0.8 0.69 H2O 0.60 O2 Catalytic CO2: 0.03 t Dehydration 0.81 oxidation 0.11 t ha / 0.10 t H2O: 0.01 t

t biopolymer 0.8 0.69 0.4 0.60 0.30 0.31 0.22 0.26 ha / Ethene* 0.10 0.10 0.4 0.31 Ethene- 0.26 0.30 * 0 0.22 oxide 0.17 t Sugar0.10 Sugar0.10 Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0.23 t 0 cane beet cane beet O2 Catalytic CO2: 0.03 t Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat O2 oxidation Reaction 0.10 t H2O: 0.01 t cane beet cane beet 0.23 t 3 PTT PTTvariations variations – Water – Water use in m use3 in m (diff erent feedstocks) Ethene- Ethene- carbonate oxide* (different feedstocks) 0.46 t 0.23 t

H2O CO2 PTT 100 Reaction O2 0.09 t 0.23 t Reaction 12 908 0.23 t

12 000 Ethene- MEG1 PTT variations – Water use in m3 carbonate 0.32 t 0.46 t (different10 000 feedstocks) PTT variations – Water use in m3 PTA H2O H2O CO2 Esterification Reaction (different8 000 feedstocks) PTT 100 0.87 t 0.095 t 0.09 t 0.23 t

12 908 H2O Polycondensation PTT 100 0.09 t 5 673 1 126 000 12 908 MEG PTT bb PDO 5 185

t biopolymer 4 612 0.32 t

/ 4 131 Bio-PET 3 12 000 4 000 m 10 000 30 PTA H2O 1.00 t Esterification 2 364 0.87 t 0.095 t 10 000 2 000 1 816 1 659 8 000 1 483 1 Omega-Process (Shell) H2O 1 OmegaProcess (Shell) Polycondensation 761 0.09 t 8 000 ** Conversion Conversion rates: rates: 6 0000 5 673 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato5 185 Wheat Starch Starch – Glucose – Glucose 90 % 90 % PTT bb PDO Glucose – Ethanol 48 % Bio-PET t biopolymer cane beet cane4 612 beet Glucose – Ethanol 48 % / 6 000 4 131 5 673 Ethanol – Ethene 48 % 30 3 Ethanol – Ethene 48 % 4 000 5 185

m 1.00 t PTT bb PDO Ethene Ethene – Etheneoxide – Etheneoxide 85 % 85 % t biopolymer 4 612

/ 4 131 3 4 000 2 364 20 – Biopolymersm , facts and statistics1 816 2019 Biopolymers, facts and statistics 2019 – 21 2 000 1 483 1 659 761 2 364 1 816 2 000 1 483 1 659 0 Sugar Sugar761 Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0 cane beet cane beet Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat cane beet cane beet Bio-PET variations – Feedstock requirements in t (different feedstocks) Bio-PET variations – Feedstock requirements in t Bio-PET variations – Feedstock requirements in t Bio-PET 100 (diff erent feedstocks) 2.1.6 Polyethylene terephthalate (Bio-PET) Bio-PET25(different variations feedstocks) – Feedstock requirements in t (different feedstocks) 21.69 100 % biobased (BioPET 100) Bio-PET 100 0.80 ha 2520 17.63Bio-PET 10017.83 5 122 m³ 25 21.69

2015 21.69 17.83 Potato t biopolymer Bio-PET bb EtOH 17.63

/ 20 0.30 ha 0.31 ha 0.68 ha 1.86 ha 17.63 17.83 17.83 t 10 4 547 m³ 2 331 m³ 5 604 m³ 12 751 m³ oc k 15

t biopolymer Bio-PET bb EtOH 6.98

/ 15 5.69 t biopolymer Sugar Sugar eeds t 4.63Bio-PET bb EtOH4.72 4.59 Corn Wheat / 105 oc k cane or beet or t f 10 1.85 6.98 21.69 t 17.63 t 4.59 t 6.98 t oc k 5.69 1.21

eeds t 4.63 50 4.72 4.59 6.98 t f Sugar5.69 Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat eeds t 4.63 4.72 4.59 fermt. 5 1.21 1.85 Starch t f Sugar* cane beet cane beet 0 1.21 1.85 2.82 t 3.21 t Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0 cane beet cane beet H2O H2O Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat Hydrolysis Enzymes Dextrins cane beet cane beet Bio-PET variations – Land use in ha Glucose* (different feedstocks) 2.82 t Bio-PETBio-PET variations variations – Land– Land use in ha use in ha (diff erent feedstocks) or (differentBio-PET variations feedstocks) – Land use in ha Bio-PET 100 0.74 t 2.08 t (different feedstocks) 2.0 H2O CO2 H2O CO2 1.86 Fermentation Fermentation Bio-PET 100 Microorg. Yeast Bio-PET 100 2.01.6 H2O H2O Filtration Filtration Bio-PET bb EtOH 1.86 Stillage 2.0 Stillage 1.86 1.61.2 Iso- * 1.6 Bio-PET bb EtOH Ethanol * butanol Bio-PET bb EtOH 0.80

t biopolymer 0.8 0.36 t 0.81 t 1.2 0.68 1.2 0.49 ha / H O: 0.11 t H2O: 0.19 t 0.80 2 Dehydration 0.4 Dehydration t biopolymer 0.8 Other: 0.04 t 0.30 0.31 0.68 EtOH: 0.08 t 0.18 0.21 0.80

t biopolymer 0.8 0.49 ha / 0.08 0.08 0.68 0 0.49 * Iso- ha / 0.4 0.30 0.31 Ethene 0.21 butene Sugar Sugar Corn0.18 Potato Wheat Sugar Sugar Corn Potato Wheat 0.4 0.08 0.08 0.30 0.31 0.17 t 0.58 t cane beet 0.18 0.21 cane beet 0 0.08 0.08 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0 O2 Catalytic CO2: 0.03 t Dimerization Sugarcane Sugarbeet Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat oxidation 0.10 t H2O: 0.01 t cane beet cane beet Bio-PET variations – Water use in m3 Ethene- Iso- (different feedstocks) 3 oxide* octene Bio-PET variations – Water use in m (diff erent feedstocks) 3 0.23 t 0.58 t Bio-PET variations – Water use in m Bio-PET(different variations feedstocks) – Water use in m3 CO2 H2SO4 H2O: 0.28 t Reaction Dehydrogenation Bio-PET 100 0.23 t (different feedstocks) 1.53 t H2SO4: 1.28 t 12 751

Ethene- Para- 12 000 Bio-PET 100 1 Omega-Process (Shell) 2 carbonate xylene Bio-PET 100 12 751 0.46 t 0.55 t 12 751 1210 000

H2O CO2 KMnO4 MnO2: 1.81 t 12 000 Reaction Oxidation 0.09 t 0.23 t 3.29 t KOH: 1.16 t 108 000 10 000 1 Omega-Process (Shell) MEG1 PTA 86 000 5 604 5 122 0.32 t 0.87 t Bio-PET bb EtOH t biopolymer 8 000 4 547 / 3 1 PTA H2O 4 000 m 6 000 5 604 OmegaProcess (Shell) Esterification 3 376 2 5 122 GEVOProcess 0.87 t 0.095 t 6 000 Bio-PET bb EtOH t biopolymer 4 547 2 331 5 604 1 / 5 122 * Conversion Omega-Process rates: (Shell) 3 Bio-PET bb EtOH 2 H2O t biopolymer 42 000 4 547 m 1 484 1 356 GEVO-Process Polycondensation / 1 193 3 376 Starch – Glucose 90 % 3 0.01 t 4 000 612 Glucose* – Conversion Ethanol 48rates: % m 3 376 2 331 Starch – Glucose 90 % Glucose – Isobutanol 39 % 2 0000 1 484 Glucose – Ethanol 48 % 1 193 1 356 2 331 Ethanol – Ethene 48 % Bio-PET Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat Glucose – Isobutanol 39 % 2 000 612 1 484 1 356 Ethene – EthanolEtheneoxide – Ethene 85 48 % 100 cane1 193 beet cane beet Ethene – Etheneoxide 85 % 1.00 t 0 612 Sugar Sugar Corn Potato Wheat Sugar Sugar Corn Potato Wheat 0 22 – Biopolymers, facts and statistics 2019 Sugarcane Sugarbeet Corn Potato Wheat Sugarcane SugarbeetBiopolymersCorn Potato, factsWheat and statistics 2019 – 23 cane beet cane beet Bio-PE– Feedstock requirements in t (different feedstocks)

35 33.55 Bio-PE– Feedstock requirements in t 30Bio-PE–(different Feedstock feedstocks) requirements in t 2.2 Bio-based polyolefi ns Bio-PE –(different Feedstock feedstocks)27.26 requirements27.50 in t (diff erent feedstocks) 25 35 33.55 35 33.55 20 30 t biopolymer

2.2.1 Polyethylene (Bio-PE) / 30 27.26 27.50 27.26 27.50 15 1.24 ha oc k 25 25 7 899 m³ 10.76 eeds t 10

t f 20 7.07 t biopolymer 20 / t biopolymer

/ 5 Potato 15 oc k 15 0.46 ha 0.47 ha 1.06 ha 2.88 ha oc k 27.50 t 10.76

eeds t 0 7 031m³ 3 605 m³ 8 642 m³ 19 663 m³ 10 10.76 t f

eeds t Sugar Sugar Corn Potato Wheat 10 7.07 t f cane beet 7.07 Sugar Sugar 5 Corn Wheat 5 cane or beet or 0 33.55 t 27.26 t 7.07 t 10.76 t Bio-PE0 Sugar – LandSugar use in haCorn Potato Wheat (differentSugarcane feedstocks)Sugarbeet Corn Potato Wheat cane beet

3.0 Sugar Starch Bio-PE – Land use in ha 2.88 Bio-PE –Bio-PE(different Land – Landfeedstocks) use use in in haha (diff erent feedstocks) 4.36 t 4.95 t 2.5(different feedstocks)

2.0 H2O CO2 H2O H2O 3.0 2.88 Fermentation Hydrolysis 3.0 Yeast Enzymes Dextrins 2.88 1.5 2.5 H O 1.24

2 t biopolymer 2.5 Rectification 1.06 Stillage 1.0

* ha / Glucose 2.0 2.0 0.46 0.47 4.36 t 0.5 Bio- 1.5 1.5 1.24

* t biopolymer Ethanol 1.06 1.24

t biopolymer 0 H2O CO2 1.0 1.06 Fermentation ha / Sugar Sugar Corn Potato Wheat 2.08 t 1.0 Yeast ha / cane beet 0.46 0.47 0.5 0.46 0.47 H2O: 0.64 t H2O 0.5 Dehydration Rectification Stillage EtOH: 0.44 t 0 0 Sugar Sugar Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat cane beet Ethene* Bio- Ethanol* 1.00 t 2.08 t 3 Bio-PE – Water use in m 19 663

Catalyst H2O: 0.64 t (diff erent feedstocks) Polymerization Dehydration EtOH: 0.44 t

10 000 19 663 Bio-PE Ethene* 8 642 19 663 7 899 8 000 7 031 1.00 t 1.00 t 6 000 10 000 10 000 Catalyst 8 642 * Conversion rates: Polymerization * Conversion rates: t biopolymer 4 000 3 605 8 642 7 899

/ 8 000 Starch – Glucose 90 % 3 7 899 Starch – Glucose 90 % 8 000 7 031 fermt. Sugar – Ethanol 48 % m 7 031 fermt. Ethanol Sugar – Ethene – Ethanol 48 % 48 % 2 000 6 000 Ethanol (conventional – Ethene technology) 48 % Bio-PE 6 000 (conventional technology) 0

t biopolymer 4 000 3 605

/ Sugar Sugar Corn Potato Wheat 3 1.00 t t biopolymer 4 000 3 605 / m cane beet 3

m 2 000 24 – Biopolymers, facts and statistics 2019 2 000 Biopolymers, facts and statistics 2019 – 25

0 0 Sugar Sugar Corn Potato Wheat Sugarcane Sugarbeet Corn Potato Wheat cane beet Bio-PA 6 – Feedstock requirements in t (different feedstocks)

Bio-PA 6 – Feedstock requirements in t 25 23.61 Bio-PA(different 6 – feedstocks) Feedstock requirements in t Bio-PA(different 6 – Feedstock feedstocks) requirements in t (diff erent feedstocks) 2.3 Bio-based polyamides (Bio-PA) 20 19.19 19.37

25 23.61 2515 23.61 t biopolymer 2.3.1 Homopolyamides 0.91 ha / 20 19.19 19.37 2010 19.19 19.37 5 569 m³ oc k 7.64 2.3.1.1 BioPA 6 15 4.99 eeds t t biopolymer 155 / t f

t biopolymer

/ 10 Potato oc k 7.64 100 oc k 0.32 ha 0.33 ha 0.77 ha 2.14 ha Sugar Sugar Corn4.99 Potato Wheat7.64 19.39 t eeds t 5 t f cane beet 4.99 4 950 m³ 2 538 m³ 6 093 m³ 13 864 m³ eeds t 5 t f 0 Sugar Sugar Corn Potato Wheat Sugar Sugar 0 Corn Wheat Sugarcane Sugarbeet Corn Potato Wheat cane or beet or Bio-PAcane 6 – Landbeet use in ha 23.62 t 19.19 t 4.99 t 7.59 t (different feedstocks)

2.4 Bio-PABio-PA 6 – 6Land – Land useuse in hain ha (diff 2.18erent feedstocks) Bio-PA(different 6 – feedstocks) Land use in ha Sugar Starch 2.0(different feedstocks) 2.4 3.07 t 3.49 t 2.41.6 2.18 2.18 2.0 2.01.2 H2O CO2, H2O H2O H2O

Fermentation Hydrolysis t biopolymer 1.6 0.92 Microorg. Microbial Enzymes Dextrins 1.60.8 0.77 mass ha / 1.2 1.20.4 0.34 0.37

t biopolymer 0.92 * * Lysine Glucose 0.77 t biopolymer 0.8 0.92 ha / 0.80 0.77 ha / Sugar Sugar Corn Potato Wheat 2.15 t 3.07 t 0.37 0.4 cane0.34 beet 0.4 0.34 0.37 H O CO H O H2O CO2, H2O 2 2, 2 0 Fermentation Fermentation Sugar Sugar Corn Potato Wheat Microorg. Microbial 0 Microorg. Microbial cane beet mass mass Sugar Sugar Corn Potato Wheat Bio-PAcane 6 – Waterbeet use in m3 (different feedstocks) Capro- Lysine* 3 lactam* Bio-PA 6 – Water use in m (diff erent feedstocks) 3 1.00 t 2.15 t Bio-PA 6 – Water use in m 3 Bio-PA(different 6 – feedstocks) Water use in m 13 959 (different feedstocks) H2O CO2, H2O Catalyst Ring-opening Fermentation 12 000 Microorg. polymerization Microbial 13 959 mass 13 959 10 000

Capro- 12 000 * 128 000 Bio-PA 6 lactam 10 000 1.00 t 6 098 106 000 5 563 1.00 t 4 948

t biopolymer 8 000 / 3 48 000 Catalyst Ring-opening m * Conversion Conversion rates: rates: 6 098 Starch – Glucose 90 % polymerization 6 000 2 538 5 563 Starch – Glucose 90 % 6 098 fermt. Sugar – Lysine 70 % 26 000 4 948

t biopolymer 5 563

fermt. Sugar – Lysine 70 % /

Lysine – Caprolactam 47 % 3 4 948

t biopolymer 4 000 m Lysine – Caprolactam 47 % / 3 4 0000 m Sugar Sugar2 538 Corn Potato Wheat Bio-PA 6 2 000 cane 2beet 538 2 000

1.00 t 0 Sugar Sugar Corn Potato Wheat 0 26 – Biopolymers, facts and statistics 2019 Sugarcane Sugarbeet Corn Potato WheatBiopolymers, facts and statistics 2019 – 27 cane beet 2.3.1 Homopolyamides 2.3.2 Copolyamides 2.3.1.2 BioPA 11 2.3.2.1 BioPA 4.10 – BioPA 5.10 – BioPA 6.10

4.63 ha

58 891 m³ 3.03 ha 2.86 ha 2.74 ha 38 596 m³ 36 366 m³ 34 884 m³ Castor Castor Castor Castor bean bean bean bean (seeds) (seeds) (seeds) (seeds) 5.95 t 3.033.9 hat 2.863.68 ha t 2.743.53 ha t 38 596 m³ 36 366 m³ 34 884 m³

CastorCastor CastorCastor CastorCastor bean bean bean Castor oil1 oil1 oil1 1 (seeds) (seeds) (seeds) oil 3.033.91.56 hat t 2.863.681.47 ha t t 2.743.531.41 ha t t 2.38 t 38 596 m³ 36 366 m³ 34 884 m³ Hydrolysis Hydrolysis Hydrolysis CastorCastor CastorCastor CastorCastor bean bean bean oil1 oil1 oil1 Hydrolysis (seeds) (seeds) (seeds) 3.91.56 t t 3.681.47 t t 3.531.41 t t Ricinoleic Ricinoleic Ricinoleic acid acid acid Hydrolysis Hydrolysis Hydrolysis Ricinoleic Castor1.33 t Castor1.25 t Castor1.20 t oil1 oil1 oil1 acid 2-Octanol: 2-Octanol: 2-Octanol: NaOH 1.56Alkaline t 0.51 t NaOH 1.47Alkaline t 0.48 t NaOH 1.41Alkaline t 0.46 t 2.02 t Ricinoleic Ricinoleic Ricinoleic 0.32 t cracking Sodium: 0.30 t cracking Sodium: 0.28 t cracking Sodium: acid 0.18 t acid 0.17 t acid 0.16 t Hydrolysis1.33 t Hydrolysis1.25 t Hydrolysis1.20 t Heptanal Pyrolysis Sebacic Sebacic Sebacic 0.62 t 2-Octanol: 2-Octanol: 2-Octanol: NaOH acid* 0.51 t NaOH acid* 0.48 t NaOH acid* 0.46 t RicinoleicAlkaline RicinoleicAlkaline RicinoleicAlkaline cracking cracking cracking 0.32 t acid0.80 t Sodium: 0.30 t acid0.75 t Sodium: 0.28 t acid0.72 t Sodium: 0.18 t 0.17 t 0.16 t Undecane 1.33 t 1.25 t 1.20 t TMDA H2O PMDA H2O HDMA H2O * Condensation Condensation Condensation acid 0.35 t Sebacic 2-Octanol:0.15 t 0.38 t Sebacic 2-Octanol:0.13 t 0.41 t Sebacic 2-Octanol:0.13 t 1.01 t NaOH Alkalineacid* 0.51 t NaOH Alkalineacid* 0.48 t NaOH Alkalineacid* 0.46 t 0.32 t cracking0.80 t Sodium: 0.30 t cracking0.75 t Sodium: 0.28 t cracking0.72 t Sodium: Bio-PA 0.18 t Bio-PA 0.17 t Bio-PA 0.16 t 4.10 5.10 6.10 Ammonia H2 TMDA H2O PMDA H2O HDMA H2O Catalytic Condensation1.00 t Condensation1.00 t Condensation1.00 t conversion 0.35 t Sebacic 0.15 t 0.38 t Sebacic 0.13 t 0.41 t Sebacic 0.13 t 0.09 t 0.01 t acid* acid* acid* 0.80 t 0.75 t 0.72 t Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 Amino- TMDA H2O PMDA H2O HDMA H2O Condensation1.00 t Condensation1.00 t Condensation1.00 t undecane 0.35 t 0.15 t 0.38 t 0.13 t 0.41 t 0.13 t acid 1.09 t Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 H2O Condensation 1.00 t 1.00 t 1.00 t 0.09 t 1 one harvest per year 1 one harvest per year 1 one harvest per year 1 one harvest per year * Conversion rates: * Conversion rates: Bio-PA * ConversionRicinoleic rates:acid – Sebacic acid 60 % * Conversion Ricinoleic acid rates: – Undecane acid 50 % 11 Ricinoleic acid – Sebacic acid 60 % Ricinoleic acid – Undecane acid 50 % 1.00 t 1 one harvest per year * Conversion rates: Ricinoleic acid – Sebacic acid 60 % 28 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 29

1 one harvest per year * Conversion rates: Ricinoleic acid – Sebacic acid 60 % Bio-PA – Feedstock requirements in t (feedstock castor oil) Bio-PA – Feedstock requirements in t 5.95 6.0(feedstock castor oil) 5.88 2.3.2 Copolyamides Bio-PABio-PA variations – Feedstock –requirements Feedstock in t requirements in t 5.0(feedstock castor oil) 5.95 2.3.2.2 BioPA 10.10 6.0 (feedstock5.88 castor bean)

3.90 5.88 5.95 4.57 ha 6.04.0 3.68 5.0 3.53 58 143 m³ 5.03.0 4.0 3.90

t biopolymer 3.68 Castor / 3.53 2.0 3.90 bean oc k 4.0 3.0 3.68 3.53

(seeds) t biopolymer / 5.88 t eeds t 3.01.0

t f 2.0 oc k t biopolymer / 0 eeds t 2.0 oc k 1.0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA t f Castor 4.10 5.10 6.10 10.10 11 1 eeds t 1.0 oil 0 t f Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA 2.35 t 4.10 5.10 6.10 10.10 11 0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 10.10 11 Hydrolysis Bio-PA – Land use in ha (feedstock castor oil) Bio-PA – Land use in ha 6.0 (feedstock castor bean) Bio-PA(feedstock variations castor oil) – Land use in ha Ricinoleic Bio-PA – Land use in ha 5.0 acid 6.0(feedstock castor oil) 4.57 4.63 2.00 t 6.04.0 5.0 4.57 4.63 2-Octanol: 3.03 2.86 NaOH 0.77 t 5.03.0 2.74 Alkaline 4.0 4.57 4.63 0.48 t cracking Sodium: t biopolymer 3.03

ha / 4.02.0 0.27 t 3.0 2.86 2.74

t biopolymer 3.03 3.01.0 2.86 2.74

ha / 2.0 0.60 t Sebacic * t biopolymer acid 0

ha / 2.0 1.0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA NH 1.20 t 3 Nitrile 4.10 5.10 6.10 10.10 11 1.0 0.10 t synthesis 0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 10.10 11 0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 10.10 11 H2O Deca- 3 3 0.21 t dinitrile Bio-PABio-PA variations – Water use – in Water m use in m (feedstock castor bean) (different feedstocks) 0.60 t 3 Bio-PA – Water use in m 58 891 + 60 000 58 143 H /Ni (different feedstocks) Deoxidation 3 0.02 t Bio-PA – Water use in m 50 000 60 000(different feedstocks) 58 143 58 891

6040 000 38 596 58 891 50 000 36 366 58 143 DMDA 34 884

5030 000 40 000 38 596 0.51 t 36 366

t biopolymer 34 884 / H2O 3 20 000 38 596 m 40 000 30 000 36 366 Condensation 34 884 0.11 t t biopolymer /

3 3010 000 20 000 m 1 1 one harvest per year t biopolymer

one harvest per year / 3 20 0000

Bio-PA m * Conversion rates: 10 000 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA * Conversion Ricinoleic acid rates: – Sebacic acid 60 % 10.10 4.10 5.10 6.10 10.10 11 Ricinoleic acid – Sebacic acid 60 % 10 000 1.00 t 0 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PA 4.10 5.10 6.10 10.10 11 0 30 – Biopolymers, facts and statistics 2019 Bio-PA Bio-PA Bio-PA Bio-PA Bio-PABiopolymers, facts and statistics 2019 – 31 4.10 5.10 6.10 10.10 11 Bio-PUR – Feedstock requirements in t (feedstock castor oil) Bio-PUR – Feedstock requirements in t (feedstockBio-PUR – castorFeedstock oil) requirements in t 2.4 Polyurethanes Bio-PUR variations(feedstock castor – oil)Feedstock requirements in t 0.6 (feedstock castor bean) 0.55

0.6 0.5 0.48 0.55 0.6 0.55 0.5 0.48 0.4 0.5 0.48

0.4 0.3

t biopolymer 0.4 / 0.3 0.2 0.37 ha 0.43 ha oc k t biopolymer

/ 0.3

4 701 m³ 5 443 m³ t biopolymer / eeds t 0.2 oc k 0.15 t f 0.2 Castor Castor oc k eeds t 0.15 0 bean bean t f eeds t 0.15 Bio-PUR Bio-PUR (seeds) (seeds) t f rigid foam flexible foam 0 0.370.48 ha t 0.430.55 ha t Bio-PUR Bio-PUR 0 4 701 m³ 5 443 m³ rigidBio-PUR foam flexibleBio-PUR foam rigid foam flexible foam Castor Castor Castor Castor Bio-PUR – Land use in ha bean 1 bean 1 (seeds)oil (seeds)oil (feedstock castor oil) 0.480.19 t t 0.550.22 t t Bio-PUR – Land use in ha Bio-PUR variations0.5(feedstockBio-PUR – castorLand – use oil)Land in ha use in ha (feedstock castor bean) (feedstock castor oil)0.43 0.5 0.4 MeOH, CO Transesterification, MeOH MeOH, CO Transesterification, MeOH 0.5 Castor Castor 0.37 0.43 H , Catalyst epoxidation1 Glycerine H , Catalyst epoxidation1 Glycerine 2 2 0.4 0.43 oil oil 0.37 0.3 0.4 0.19 t 0.22 t 0.37

0.3

t biopolymer 0.2 Natural Natural 0.3

MeOH, CO Transesterification,oil polyols MeOH MeOH, CO Transesterification,oil polyols MeOH ha /

t biopolymer 0.2 epoxidation0.50 t Glycerine epoxidation0.60 t Glycerine 0.1 H2, Catalyst H2, Catalyst t biopolymer ha / 0.2 ha / 0.1 Isocyanates Isocyanates 0 Polyaddition Polyaddition 0.1 Bio-PUR Bio-PUR 0.50 t Natural 0.40 t Natural rigid foam flexible foam 0 oil polyols oil polyols Bio-PUR Bio-PUR 0 0.50 t 0.60 t rigidBio-PUR foam flexibleBio-PUR foam Bio-PUR Bio-PUR rigid foam flexible foam Rigid foam Flexible foam Isocyanates Isocyanates Polyaddition1.00 t Polyaddition1.00 t 3 0.50 t 0.40 t Bio-PUR – Water use in m (feedstock castor oil) Bio-PUR – Water use in m3 3 Bio-PUR Bio-PUR Bio-PUR(feedstock – castorWater oil)use in m 3 Bio-PUR variations(feedstock castor – Water oil) use in m (feedstock castor bean) Rigid foam Flexible foam

6 000 1.00 t 1.00 t 5 443 1 one harvest per year 4 701 6 000 4 500 5 443 6 000 4 701 5 443 4 500 3 000 4 701 4 500 t biopolymer /

3 3 000 1 500 m 3 000 t biopolymer / 3

t biopolymer 1 500 m

/ 0 3 1 500 Bio-PUR Bio-PUR 1 m one harvest per year rigid foam flexible foam 0 Bio-PUR Bio-PUR 0 rigid foam flexible foam 32 – Biopolymers, facts and statistics 2019 Bio-PUR Bio-PUR Biopolymers, facts and statistics 2019 – 33 rigid foam flexible foam

1 one harvest per year 2.5 Polysaccharide polymers 2.5.1 Cellulose-based polymers (Cellulosics) 2.5.1.2 Cellulose diacetate 2.5.1 Cellulose-based polymers (Cellulosics) 2.5.1.1 Regenerated cellulose

1.52 ha 0.82 ha 0.82 ha

Wood Wood Wood

2.50 t 1.33 t 1.33 t

Pulping process Pulping process Pulping process

0.82 ha 0.82 ha

Cellulose Cellulose Cellulose Wood Wood 1.00 t 0.53 t 0.53 t 1.33 t Acetic 1.33 t Acetic acid H2O anhydride Acetic acid NaOH Solving, Esterification Esterification 0.38 t 0.11 t 0.64 t 0.37 t 2.38 t bulging Pulping process Pulping process

Plasticizer Cellulose Plasticizer Cellulose Alkali- diacetate diacetate 0.20 t Cellulose 0.20 t Cellulose cellulose 1.00 t 1.00 t 3.38 t 0.53 t 0.53 t Acetic CS2 Acetic acid H2O anhydride Acetic acid Sulfidation Esterification Esterification 0.14 t 0.38 t 0.11 t 0.64 t 0.37 t

Cellulose- Plasticizer Cellulose Plasticizer Cellulose xanthate 0.20 t diacetate 0.20 t diacetate 3.52 t 1.00 t 1.00 t

H2SO4 CS2, NaSO2 Polymerization 1.15 t H2O

Regene- rated cellulose 1.00 t

34 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 35 Cellulosics – Feedstock requirements in t (feedstock wood) 2.5.2 Starch-based polymers Cellulosics – Feedstock requirements in t Cellulosics – Feedstock requirements in t 2.5.2.1 Thermoplastic starch (TPS) (feedstock wood) (feedstock wood) 2.50 2.5 2.50 2.5

2.0 2.0

1.5 1.5 1.33 0.19 ha t biopolymer 1.33 / t biopolymer

/ 1 197 m³ 1.0 oc k 1.0 oc k

eeds t 0.5 eeds t t f 0.5 Potato t f 0.16 ha 0.44 ha 0 4.17 t 0 Cellulose Regenerated 1 309 m³ 2 979 m³ diacetateCellulose Regeneratedcellulose diacetate cellulose

Corn Wheat or Cellulosics – Land use in ha 1.07 t 1.63 t Cellulosics – Land use in ha (feedstock wood) Cellulosics(feedstock – Land wood) use in ha (feedstock wood)

Starch 2.0 2.0 0.75 t 1.6 1.52 1.6 1.52

1.2 Plasticizer Destruction 1.2 (Extrusion) 0.82 0.25 t

t biopolymer 0.8 0.82

t biopolymer 0.8 ha /

ha / 0.4 0.4 TPS* 0 0 Cellulose Regenerated 1.00 t diacetateCellulose Regeneratedcellulose diacetate cellulose

* Starch* Starch content 75 75 % %

36 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 37 Starch-based polymers – Feedstock requirements in t (different feedstocks) Starch-based polymers – Feedstock requirements in t 2.5.2 Starch-based polymers Starch-based5 polymers – Feedstock requirements in t Starch-based(differentTPS feedstocks) polymers – FeedstockStarch requirementsStarch in t Starch 4.17 blend 30/70 blend 50/50 blend 70/30 2.5.2.2 Starch blends (diff erent(different4 feedstocks) feedstocks) 5 TPS Starch Starch Starch 2.98 53 4.17 blend 30/70 blend 50/50 blend 70/30

t biopolymer 4 TPS Starch Starch Starch / 4.17 blend 30/70 blend2.11 50/50 blend 70/30 2 0.06 ha 0.09 ha 0.13 ha oc k 4 1.63 2.98 3 362 m³ 606 m³ 856 m³ 1.26 t biopolymer 1.07 1.17 / eeds t 2.98 31 2.11 0.83 0.77 t f 0.49 0.54 t biopolymer 2 oc k 0.32 Potato Potato Potato / 1.63 1.26 2.11 20 1.17 oc k 1.07 0.05 ha 0.13 ha 0.08 ha 0.22 ha 0.11 ha 0.31 ha eeds t 1.63 1.26 t 2.11 t 2.98 t 1 Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat0.83 Corn0.77 Potato Wheat t f 1.26 0.49 0.54 397 m³ 902 m³ 663 m³ 1 509 m³ 937 m³ 2 132 m³ 1.07 0.32 1.17 eeds t 1 0.83 0.77 t f 0 0.49 0.54 Corn Potato Wheat Corn0.32 Potato Wheat Corn Potato Wheat Corn Potato Wheat Corn Wheat Corn Wheat Corn Wheat 0.06 ha or 0.09 ha or 0.13 ha or 0 Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat 0.32 t362 m³ 0.49 t 0.54 t606 m³ 0.83 t 0.77 t856 m³ 1.17 t Starch-based polymers – Land use in ha (different feedstocks) Potato Potato Potato 0.05 ha 0.13 ha 0.08 ha 0.22 ha 0.11 ha 0.31 ha 1.26 t Starch 2.11 t Starch 2.98 t Starch Starch-based polymers – Land use in ha 397 m³ 902 m³ 663 m³ 1 509 m³ 937 m³ 2 132 m³ Starch-based(differentTPS feedstocks) polymersStarch – Land useStarch in ha (diff Starcherent feedstocks) 0.23 t 0.38 t 0.54 t Starch-based polymers – Land use in ha 0.5 blend 30/70 blend 50/50 blend 70/30 (different feedstocks)0.44 Corn Wheat Corn Wheat Corn Wheat 0.06 ha or 0.09 ha or 0.13 ha or Plasticizer Destruction Plasticizer Destruction Plasticizer Destruction 0.4 TPS Starch Starch Starch 362 m³ 606 m³ 856 m³ 0.5 0.32 t 0.07 t (Extrusion)0.49 t 0.54 t 0.12 t (Extrusion)0.83 t 0.77 t 0.16 t (Extrusion)1.17 t blend 30/70 blend 50/50 blend 70/30 TPS 0.44 Starch Starch Starch 0.31 0.50.3 blend 30/70 blend 50/50 blend 70/30 0.4 0.44 Potato Potato Potato 0.22 * * * 0.19 0.05 ha Starch0.13 haTPS 0.08 ha Starch0.22 haTPS 0.11 ha Starch0.31 haTPS t biopolymer 0.40.2 0.31 1.26 t 2.11 t 2.98 t 0.3 0.16 397 m³ 902 m³ 663 m³ 1 509 m³ 937 m³ 2 132 m³ ha / 0.13 0.13 0.23 t 0.30 t 0.38 t 0.50 t 0.54 t 0.70 t 0.11 0.31 0.30.1 0.08 0.09 0.22 0.19 0.05 0.06 t biopolymer 0.2 Corn Polymers Wheat Corn Polymers Wheat Corn Polymers Wheat 0.16 0.22 or Extrusion or Extrusion or Extrusion ha / 0.13 0.13 Plasticizer Plasticizer Plasticizer 0 0.19 0.70Destruction t 0.50Destruction t 0.30Destruction t t biopolymer 0.2 0.11 0.1 Corn0.16 Potato Wheat Corn Potato Wheat Corn0.08 Potato0.09 Wheat Corn Potato Wheat 0.32 t 0.07 t (Extrusion)0.49 t 0.54 t 0.12 t (Extrusion)0.83 t 0.77 t 0.16 t (Extrusion)1.17 t ha / 0.06 0.13 0.13 0.05 0.11 Starch Starch Starch 0.1 0.08 0.09 blend blend blend 0 0.05 0.06 ** ** ** Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat * 30/70 * 50/50 * 70/30 Starch TPS Starch TPS Starch TPS 0 1.00 t 1.00 t 1.00 t Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat 0.23 t 0.30 t 0.38 t 0.50 t 0.54 t 0.70 t * Starch content 75 % Polymers ** Ratio TPS/Polymer Polymers Polymers 3 3 Extrusion Extrusion Extrusion Starch-based polymers – Water use in m Plasticizer Destruction Plasticizer Destruction Plasticizer Destruction Starch-based polymers – Water use in m (diff erent feedstocks) 0.70 t 0.50 t 0.30 t (different feedstocks) 0.07 t (Extrusion) 0.12 t (Extrusion) 0.16 t (Extrusion) Starch Starch Starch Starch-based polymers – Water use in m3 blend* Starch content 75 % blend blend TPS Starch Starch Starch ** **Ratio TPS/Polymer ** ** (different feedstocks) 3 * 30/70 * 50/50 * 70/30 2 996 blend 30/70 blend 50/50 blend 70/30 TPS TPS TPS 3 000Starch-based polymers – Water use in m 1.00 t 1.00 t 1.00 t (different feedstocks) 0.30 t 0.50 t 0.70 t TPS Starch Starch Starch 2 500 2 996 blend 30/70 blend 50/50 blend 70/30 3 000 TPS Starch Starch Starch Polymers Polymers Polymers 2 132 2 996 Extrusion Extrusion Extrusion 2 000 blend 30/70 blend 50/50 blend 70/30 0.70 t 0.50 t 0.30 t 3 000 2 500 1 509 21 500 2 132 Starch Starch Starch 1 308 blend* Starch content 75 % blend blend 2 000 1 198 t biopolymer 2 132

** ** ** / 30/70** Ratio TPS/Polymer 50/50 70/30 3 21 000 902 937 1.00 t 1.00 t 1.00 t m 1 509 856 1 500 1 308 663 606 1 198 1 509 t biopolymer

/ 1 500 397 362 3 1 000 1 308 902 937 m 1 198 856 t biopolymer /

3 663 1 0000 902 606 937 m 856 500 Corn Potato Wheat Corn397 Potato362 Wheat Corn Potato Wheat Corn Potato Wheat 663 606 500 397 362 * Starch content 75 % 0 ** Ratio TPS/Polymer38 – Biopolymers, facts and statistics 2019 Corn Potato Wheat Corn Potato Wheat Corn Potato WheatBiopolymersCorn Potato, factsWheat and statistics 2019 – 39 0 Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat Corn Potato Wheat 3 Market data and land use facts

As already mentioned in the introduction, the focus of attenti To make things even more compelling, it is a fact that on is on “New Economy” bioplastics, including their position bio-based plastics, even after multiple material usage, can at the market. To give the reader an impression of the market still serve as an energy carrier. This means that additio share of these innovative and novel bioplastics the following nal crop lands, which are currently used for direct ener pages contain a summary of IfBB's research. gy production, could be set aside for the production of bioplastics. Prior material usage of biomass, as in the case When considering the most important Old Economy biopla of bioplastics, still permits subsequent trouble-free energy stics with their global production capacity of about 17 million recovery, whereas direct incineration of biomass (and tonnes annually, it turns out that the share of New Economy also crude oil-based products!) precludes an immediate bioplastics is almost 10 times lower, i.e. 12 % of the market subsequent material usage. In this case, more arable land volume of all bio-based plastics (Old and New Economy Bio for plant cultivation is needed and consequently another plastics included), with rising tendency. photosynthesis process, in order to gain new resources once again as feedstock for material usage. By size and large, Old and New Economy bioplastics (about 19 million tonnes) have a combined share of presently nearly Production capacities and land use 6 % of the global plastics market. Given the anticipated market Old and New Economy bioplastics growth, especially of New Economy bioplastics, over a 5-year period, the market share of Old and New Economy biopla stics is expected to reach a maximum of 10 % of the global 12 000 000 market for plastics within the next 5 years. The corresponding Natural rubber 12 000 000 land use of Old and New Economy bioplastics is currently at Natural rubber 56 000 approximately 15.7 million hectares, which is equivalent to Linoleum3 only 0.3 % of the global agricultural area or approximately 56 000 Linoleum3 1 % of the arable land. Comparing these figures reveals that 500 000 New Economy bioplastics1 New Economy bioplastics, which tend to be the only focus of 500 000 interest in land use discussions, use up only 5 % of the area New Economy bioplastics1 required for all bio-based plastics combined. 2 900 000 Cellulose2 2 900 000 Even though global forecasts predict a rapidly growing Cellulose2 market for these novel bioplastics in the next few years, 10 978 000 Natural rubber the need for agricultural areas will be still kept at a very 10 978 000 Natural rubber low level. While the market for new bioplastics has been 140 000 growing by around 6 % annually during the last three years Linoleum3 140 000 and a sustained growth is anticipated in the future, it can Linoleum3 be assumed that land use for New Economy bioplastics by 2 614 000 New Economy bioplastics1 2023 (4.4 million tonnes), for example, will be as low as 2 614 000 1 0.02 % of the global agricultural area or about 0.1 % of the New Economy bioplastics arable land (see figures on page 42 and page 46). Regard 5 800 000 Cellulose2 less of the significant growth rates, it should be mentioned 5 800 000 2 that the market share of these New Economy bioplastics is Cellulose still hovering at less than 1 % of the global plastics market 1 PLA, PHA, PTT, PBAT, Starch blends, Drop-Ins (Bio-PE, Bio-PET, Bio-PA) and other and is likely not to exceed 2-3 % in the near future. 2 Material use excl. paper industry 3 Calculations include linseed oil only

40 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 41 10 000

9 000

3.1 New8 000 Economy bioplastics global production capacities 3.2 New Economy bioplastics production capacities by material type 7 000 2018

13.8 % 13.6 % 6 000 61.5% PLA % % 13.8 Biodegradable13.6 % bio-based61.5/non-biodeg radable PLA Biodegradablepolyesters4 bio-based/non-biodegradable 2.0 % polyesters4 7.2 % 5 000 2.0Others % Biodegradable7.2 % Others Biodegradablestarch blends 4 353 3.7 % starch blends 3.7Bio-PA % 4 000 Bio-PA 3.7 % 38.5 % 3.7PHA % 1 785 total biodeg38.5radable % PHA 7.7 % total % 3 000 biodegradable 7.7Bio-PE % 2.61 1.1 Regenerated% 2 614 Bio-PE 2.61million 1.1 milliontonnes Regeneratedcellulose2 000 t 2 274 tonnes cellulose2 2 028 2 045 1 033 % 0.2 %

in 1 4.6

2 000 ecast 881 4.6PTT % 0.2Cellulose % 737 752 o r derivativesCellulose 2

F PTT derivatives2 2 568 1 000 1 393 1 581 43.5 % 1 291 1 293 43.5Bio-PET 30 %3 Bio-PET 303 0 1 Biodegradable cellulose esters 2015 2016 2017 2018 2023 2 Compostable hydrated cellulose foils 3 Bio-based content amounts 30 % 4 Contains PBAT, PBS, PCL

2023 Bio-based/non-biodegradable 12.3 % Biodegradable12.3 % 4 5.1 % Biodegradable 59.9 % Biodegradablepolyesters Biodegradable5.1 % polyesters4 bio-based59.9/non-biodeg %r adable Biodegradablestarch blends bio-based/non-biodegradable starch blends Total capacity 19.0 % 3.8 % 19.0PLA % 3.8PHA % PLA PHA

0.6 % 40.1 % total Regenerated0.6 % 2.0 % 2 biodeg40.1radable % total Regeneratedcellulose 2.0Others % 2 biodegradable 4.35 cellulose Others 4.35million 3.7 % milliontonnes 0.1 % 3.7Bio-PP 30% tonnes 0.1Cellulose % Bio-PP 30 derivativesCellulose 2 2.5 % derivatives2 2.5Bio-PA % Bio-PA 8.7 % 5 39.1 % 8.7Bio-PE % 3 Bio-PE5 39.1Bio-PET 30 % 3.9 % Bio-PET 303 3.9PTT % PTT 1 Biodegradable cellulose esters 2 Compostable hydrated cellulose foils 3 Bio-based content amounts 30 % 4 Contains PBAT, PBS, PCL 5 Contains Bio-PE 30 and Bio-PE 100 42 – Biopolymers, facts and statistics 2019 Biopolymers, facts and statistics 2019 – 43 11.8 % Europe Packaging – rigid 3.3 New Economy bioplastics production7.8 % capacities10.7 by % region 3.4 New Economy bioplastics production capacities (incl. food South America serviceware)Packaging – North America rigid 2018 1814 kt by market segment (incl.1 369 food 309 serviceware) kt 2018 1 369 279 0.3 % 1 200 kt Australia/Oceania in % total 1 200 PLA Bio-PET 30 2 2.61 % 1 000

11.8 adable 204 million Europe adable Biodegradable 2 kt tons PLA Bio-PETBio-PE 30 9 kt 7.8 % 1 000 Starch blends 10.7 % adable South America 800 adable North America Biodegradable -biodeg r 1 bio-based/ 3 biodeg r Others Bio-PE 1814 kt % Starch blends Others

69.4 no n 309 Asia 800 -biodeg r Packaging – 1 bio-based/ 3 kt biodeg r 600 Others Others flexible

279 0.3 % no n kt Australia/Oceania Packaging494 – in % 600 Textiles flexible total 400 (incl. non‐woven

in 1 000 t 494 Agriculture and fibers) 2.61 Automotive Consumer Textiles 204 and and 252 million 400 Electrical goods (incl. non‐woven kt tons in 1 000 t and Building horticulture transports 9 Agriculture Automotive 176 and fibers) kt 200 electronic and 145 130 Consumer Electrical and and goods 252 Others (incl. casing) construction and Building horticulture transports 21 21 176 69.4 % 200 8 electronic and 145 130 0 Asia Others (incl. casing) construction 8 21 21 0

1 Contains regenerated cellulose and biodegradable cellulose ester 2 Bio-based content amounts to 30 % 3 Contains durable starch blends, Bio-PC, Bio-TPE, Bio-PUR (except thermosets), Bio-PA, PTT

2023 2023

3 000 Packaging – rigid 3 000 PLA Bio-PET 302 (incl. food 20.2 % 2 500 serviceware)Packaging – Europe adable rigid adable Biodegradable (incl.2 239 food PLA Bio-PETBio-PE3 302 4.7 % 2 500 Starch blends serviceware) South America 2 000 adable

adable 2 239 9.4 % Biodegradable -biodeg r 1 bio-based/ 34 North America biodeg r StarchOthers blends Bio-PEOthers 2 848 2 000 no n -biodeg r

kt 1 bio-based/ 4 881 kt 1 500 biodeg r Others Others 409 0.2 % no n kt in % Australia/Oceania Packaging – total 1 500 flexible 1 000 in 1 000 t Textiles 848 4.3520.2 % Packaging – (incl. non‐woven flexible million Europe Consumer Agriculture Automotive 1 000 Electrical and fibers) 207 tons in 1 000 t and Textiles 848 kt Building and goods and 427 4.7 % 500 horticulture transports (incl. non‐woven 9 and electronic 321 Agriculture kt South America Others Electrical Consumer Automotive and fibers) 9.4 % construction (incl. casing) 214and 180 Building and goods and North65.4 America % 500 68 horticulture 427 and23 electronic33 321 transports 2 848 Asia 0 881 kt Others construction (incl. casing) 214 180 kt 68 23 33 409 0.2 % 0 1 in % Contains regenerated cellulose and biodegradable cellulose ester kt Australia/Oceania 2 total Bio-based content amounts to 30 % 3 Contains Bio-PE 30 and Bio-PE 100 4.35 4 Contains durable starch blends, Bio-PC, Bio-TPE, Bio-PUR (except thermosets), Bio-PA, PTT million 207 kt tons 9 kt 44 – Biopolymers, facts and statistics 2019 65.4 % Biopolymers, facts and statistics 2019 – 45 Asia 3.5 Land use for New Economy bioplastics 2018 and 2023

For final land use estimation only the most commonly used crop was taken into consideration. Yield data from FAO statistics served as a basis for calculation (global, weighted average over the past 10 years, 2005-2014). To approximate land use in this bottom-up approach, the producer-specific production capacities of a type of bioplastics were multiplied by the output data of the corresponding process routes. In case a producer-specific feedstock type for was not known, the most commonly used crop for this bioplastic type was taken into calculation.

In all of the calculations no allocation was made, which means land use was fully, by 100 %, allocated to the raw materials for G bioplastics and not split up between various parallel side products l such as proteins or straw in wheat. So this approach leads to a ob a rather conservative estimate. l la n ral a d ltu rea u a ic 5 r b r g i e a l li a l o n a

h b

1 a o

l =

G 3

% i

* Bioplastics *

a 2018: 499 800 ha = 0.004 % **

= 2023: 913 700 ha = 0.007 % **

% Material use 106 million ha = 0.79 % ** Biofuels Pasture 53 million ha = 0.39 % ** 3.5 billion ha Arable land* 1.4 billion ha = 26.1 % ** = 10.4 % ** Food & Feed 1.24 billion ha = 9.25 % **

* Also includes area growing permanent crops as well as approx. 1 % fallow land. Abandoned land resulting from shifting cultivation is not included. ** Percentage compared to total global land area

46 – Biopolymers, facts and statistics 2019 A large amount of additional information is also available at www.ifbb-hannover.de

Hochschule Hannover | Heisterbergallee 10 A D-30453 Hannover | Germany

Phone: +49 511 9296-2268 Fax: +49 511 9296-99 2268 E-mail: [email protected]

Published by IfBB – Institute for Bioplastics and Biocomposites

ISSN (Print) 2363-8559 ISSN (Online) 2510-3431

EDITION 6, 2019