LCA DATA GAPS IN FEEDSTOCKS OF BIOBASED PLASTICS
By
Alix Andra Grabowski

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Packaging – Master of Science
2013

ABSTRACT
LCA DATA GAPS IN FEEDSTOCKS OF BIOBASED PLASTICS
By
Alix Andra Grabowski
Bioplastics are a growing field, but with their expansion comes unique
environmental issues associated with the cultivation and processing of feedstocks.
Availability of appropriate, high quality data is a problem in life cycle assessment (LCA)
of biopolymers and other biobased materials that limits the accuracy and usefulness of
study results. It is therefore critical that these data gaps be closed. In order to determine
what data is needed to close these gaps, this study reviews currently available life cycle
inventory data for biobased polymer feedstocks, and assesses the data quality for the
selected feedstocks of corn, sugarcane, and soy. Life cycle inventory databases and
relevant publications were searched for appropriate data, and the results collected into a
summary table. The quality review was conducted using a pedigree matrix type scoring
system which was adapted from the ILCD handbook, and an overall quality score for
each dataset was calculated based on the matrix scores. A total of 287 datasets were
collected during the review for a total of 22 different feedstocks. The majority of these
datasets are from Europe and the USA, with the majority of Asia, the Middle East, and
Africa having very limited data available. From the quality analysis, it was determined
that more datasets that capture regional variations in crop cultivation are needed, as
well as more data on land use change. Additionally, for the processing phase of
production there is a need for more recent and better documented data.

ii

ACKNOWLEDGEMENTS

My sincerest thanks to Dr. Selke for all her help and her phenomenal patience
with my many questions, and Dr. Auras for explaining LCA to me three million times
despite the numerous blank stares he received in return. I’d also like to thank the Center
for Packaging Innovation and Sustainability for graciously funding the project, Dr.
Narayan for serving on my committee, and Dr. Patel for taking the time to call into our
meetings despite the time difference. A special thanks to Alison Schuitema, for never
complaining when I asked her for help with a tedious job. I couldn’t have done it
without you.
Thank you also to my family. You supported my decision to go back to school
even though I suspect you secretly thought I should just get a real job.
Last, but certainly not least, thanks to Brittany Burns and Juliana Arango for all
the hours spent writing at coffee shops, tea houses and libraries; for making me laugh
harder than I ever have; for listening to me whine about every class, paper, and deadline;
and for generally making grad school the best time of my life. I might have been able to
do it without you, but it wouldn’t have been nearly as fun.

iii

TABLE OF CONTENTS
LIST OF TABLES........................................................................................................... v
LIST OF FIGURES ....................................................................................................... vii
1.

INTRODUCTION .................................................................................................... 1

2.

LITERATURE REVIEW .......................................................................................... 5

3.

METHODS ............................................................................................................ 10

4.

RESULTS AND DISCUSSION .............................................................................. 19
Data Availability ........................................................................................................ 19
Quality Analysis ........................................................................................................ 27
Technological Representativeness ....................................................................... 33
Geographical Representativeness ........................................................................ 35
Temporal Representativeness .............................................................................. 37
Completeness ....................................................................................................... 38
Uncertainty ........................................................................................................... 39
Processing Data Quality ........................................................................................... 40

5.

CONCLUSIONS ................................................................................................... 45

APPENDICES .............................................................................................................. 46
Appendix A: Ecoinvent Uncertainty Scoring Information ........................................... 47
Appendix B: Completeness Check ........................................................................... 48
Appendix C: Data Quality Ratings ............................................................................ 65
Appendix D: Processing Data Completeness Check ................................................ 84
Appendix E: Processing Data Quality Ratings .......................................................... 98
Appendix F: Data Summary Table .......................................................................... 111
REFERENCES .......................................................................................................... 293

iv

LIST OF TABLES

Table 1 - List of publications searched………………………………………………………………………..10
Table 2 – Definition of evaluation categories adapted from the ILCD Handbook……….13
Table 3 –Quality Rating Definitions adapted from the ILCD Handbook………………………14
Table 4 –Pedigree Matrix Data Quality Rating Level Definitions adapted from van der
Berg et al………………………………………………………………………………………………………16
Table 5 – Quantity and type of data collected…………………………………………………………….19
Table 6 – Data Availability by Country…………………………………………………………………………25
Table 7 –Corn Dataset Names and Identifiers……………………………………………………………..28
Table 8 – Sugarcane dataset names and identifiers…………………………………………………….30
Table 9 – Soy dataset names and identifiers………………………………………………………………..30
Table 10- Ecoinvent Uncertainty Scoring Information…………………………………………………47
Table 11 - Corn Cultivation Completeness Check………………………………………………………..48
Table 12 - Sugarcane Cultivation Completeness Check………………………………………………56
Table 13 - Soy Cultivation Completeness Check………………………………………………………….61
Table 14 - Corn DQR……………………………………………………………………………………………………65
Table 15 - Sugarcane DQR…………………………………………………………………………………………..72
Table 16 - Soy DQR……………………………………………………………………………………………………..78
Table 17 - Corn Processing Completeness Check……………………………………………………….84
Table 18 - Sugarcane Processing Completeness Check………………………………………………89
Table 19 - Soy Processing Completeness Check………………………………………………………….95
Table 20 - Corn Processing DQR…………………………………………………………………………………98
v

Table 21 - Sugarcane Processing DQR……………………………………………………………………....102
Table 22 - Soy Processing DQR………………………………………………………………………………….107
Table 23 – Raw Agricultural Data Summary………………………………………………………………111
Table 24 – Raw Agricultural Data Summary Inputs……………………………………………….…..164
Table 25 – Processed Agricultural Data Summary……………………………………………….…….193
Table 26 - Processed Agricultural Data Summary Inputs…………………………………………..215
Table 27 – Wood Data Summary……………………………………………………………………………… 227
Table 28 – Wood Data Summary Inputs………………………………………………………………….…243
Table 29 – Chemical Data Summary…………………………………………………………………………..257
Table 30 – Chemical Data Summary Inputs…………………………………………………………….....278
Table 31 – Polymer Data Summary……………………………………………………………………………290

vi

LIST OF FIGURES

Figure 1 - Summary of data collected by crop and category. For the interpretation of
the references to color in this and all other figures, the reader is referred to
the electronic version of this thesis……………..…………………………………………..…..21
Figure 2 - Geographical Distribution of Datasets…………………………………………………….…...24
Figure 3 - Corn Data Quality Ratings and Component Scores by Dataset………………………….....….28

Figure 4 - Sugarcane Data Quality Ratings and Component Scores by Dataset………..….31
Figure 5 - Soy Data Quality Ratings and Component Scores by Dataset………………..…….32
Figure 6 – Corn Processing DQRs and Component Scores by Dataset……….…………..…….41
Figure 7- Sugarcane Processing DQRs and Component Scores by Dataset…………………..42
Figure 8 - Soy Processing DQRs and Component Scores by Dataset………………………….…43

vii

1. INTRODUCTION
Recently, the public and government organizations have become increasingly
aware of the sustainability challenges faced by the world. Greenhouse gases (GHGs) in
particular, and their link to both global warming and the consumption of fossil fuels,
have garnered substantial attention. With this increased awareness comes the
opportunity for the academic community to positively impact the decisions that are
made and the way business is conducted. However, in order to make sound
recommendations, reliable and high quality tools are necessary to objectively evaluate
situations and assess the best course of action.
Life Cycle Assessment (LCA) is an objective method of evaluating the
sustainability of a system and assessing its impacts on the environment [1]. Finding a
sustainable solution to a problem is never a simple task. Often each option being
considered has its own set of advantages and disadvantages which must be weighed
and understood. One of the strengths of LCA is its ability to quantify and categorize the
individual impacts of a system and facilitate comparison. It is an effective tool for this
purpose because of the level of detail and complexity it is able to convey; but like any
method it has its limitations. It has been called “a vital and powerful decision support
tool, complementing other methods, which are equally necessary to help effectively and
efficiently make consumption and production more sustainable” [2].
The quality of information available from an LCA study is largely limited by the
quality and completeness of the data on which it is built. Without complete, high quality
data, one cannot be confident that the results of an LCA study accurately represent the

1

situation. Therefore, the quality of the data used in a study affects the quality of the
recommendations that come out of said study [2].
This problem is exacerbated by the fact that LCA studies often require the
inclusion of an expansive amount of information to achieve their goals. For example, if
one wants to accurately describe the environmental impacts of a plastic container, it is
necessary to have data not only about the material and production process, but also
about the machinery being used, the power source being used, and a multitude of other
inputs. As Álvarez-Chávez puts it: “Because of the myriad issues to consider, it can be
very challenging to determine which plastic materials are safest and healthiest for
workers, consumers, and the environment” [3]. Because of the necessity of including
such a large scope of information, it is a common practice for LCA practitioners to draw
on data from many sources. It may be impossible or impractical to generate primary
data for every aspect of the system covered by a study. To solve this problem, previously
generated data from literature is often used to fill in the gaps in information.
This means that a bank of data to draw from is extremely useful if not outright
necessary for most LCA studies; this presents a particular challenge to practitioners
when attempting to conduct an LCA on a system that utilizes newer technologies. New
technologies are particularly vulnerable to the problem of low quality data. The relatively
short time they have been in existence means that there is a significantly smaller body
of research from which to pull, and subsequently one is often forced to settle for data
that is less than ideal.
Such is the case with data for biopolymers. Interest in biopolymers is increasing
because of their potential to address the issue of fossil fuel depletion and mitigate
2

greenhouse gas emissions [4]. An article in May 2012 predicted that the demand for
polylactic acid (PLA) is expected to increase by 20% per year [5]. Production of
biopolymers is expanding in response to this increased demand. However, bio-based
plastics bring with them their own set of problems. In a 2012 comparative analysis
Álvarez-Chávez et al state “A bio-based plastic is not necessarily a sustainable plastic;
this depends on a variety of issues, including the source material, production process,
and how the material is managed at the end of its useful life” [3]. In other words, in
order to correctly asses a biopolymer’s environmental footprint one must have accurate
and representative data about many aspects of the polymer’s life cycle, including the
production of that polymer. A particular challenge of LCA studies of biopolymers is
understanding the impact of growing the polymer feedstock since, as discussed by
Nemecek et al., “Environmental impact data for crops in the literature and the LCA
databases are scarce” [6].
This means LCA data for biopolymers must be obtained; but what type of data
should be collected? Before more information can be sought out, it is necessary to
identify what types of data are most needed. Identifying critical data gaps for LCA of
biopolymers will provide a foundation for future research. By indicating what areas are
weak in data availability, researchers can identify the types of data that should be
focused on when collecting primary data. If researchers are conducting a study on PLA,
for example, they may choose to focus their efforts on the collection of water usage
data instead of another type such as pesticide use, depending on which area has a more
significant data gap, and therefore less surrogate data to pull from. Understanding the
quality and completeness of currently available data also has the potential to clarify the

3

strengths and limitations of existing studies that use this data, as it may shed new light
on how representative the study is of the system it is trying to model.
The first step in the quest to solve data issues in LCA is addressed by van der
Voet et al. in a review of the state of LCA for the related field of biofuels: “The way
forward to remedy data problems is clear: identify data needs, collect more and better
data and make them accessible” [7]. A review of the state of currently available LCA data
is the first step to improving the quantity and quality of available data, because without
being aware of what is already published it would be impossible to know where future
efforts in data acquisition should be focused. The goal of this project is to identify the
critical data gaps present in LCA data for biopolymer feedstocks in order to illuminate
the next step in the path to a more sustainable bioplastic.

4

2. LITERATURE REVIEW
The importance of high quality data for LCA has been well documented. The
International Organization for Standardization (ISO) devotes several sections of their
guide for conducting an LCA to data quality requirements, and outlines several tools
and checks that a practitioner may use to assess the quality of their data [2].
Additionally, several Life Cycle Inventory (LCI) databases have their own methods of
evaluating data. In a 2011 article in the International Journal of Life Cycle Assessment,
Cooper et al. [8]explore the strengths and weaknesses of the current commonly used
methods for assessing data quality. These methods vary in the way they report data
quality. Ecoinvent and the ILCD handbook utilize a numerical scoring method where
data can be judged to fall anywhere from 1 (very good) to 5 (very bad). In contrast, the
LCA Commons database divides data into two categories, A and B, where A is high
quality data and B is low quality data. This method was developed as a way to
distinguish between data quality without the use of a numeric scoring system, which has
been criticized as being difficult to interpret. However, they all address the data quality
issues that are defined by ISO. In fact, the article concludes that “The strengths of data
quality analysis methods lie in the consideration of the data quality aspects specified by
the ISO standards” [8].
ISO names several areas that should be considered when evaluating data quality.
Specifically they are: time related coverage, geographical coverage, technological
coverage, precision (measure of the variability of the data values), completeness,
representativeness, consistency, reproducibility, source of data, and uncertainty of

5

information. Of the items on the list, the first four are better documented and explored
in terms of their effect on the outcomes of LCA studies.
When one thinks of examples of gaps in LCA data for agricultural products, the
first thing that comes to mind is an instance where there is no data for a certain process,
or no data for a certain crop. However gaps in data availability also occur between
technologies, geographic areas, and as methods or conditions change over time. Even a
seemingly simple example where only the region of production is varied can have
hidden depths. This is because agricultural methods can vary significantly by region,
even for the same crops; and this variation in turn can create large differences in the
environmental impacts of these different systems. For example, soybeans may be grown
using a traditional till, irrigated system in one region and a no-till, non-irrigated system
in another region. In this situation, the fossil fuel and water usage could both potentially
be significantly greater for the first system. This is supported by information from Kim
and Dale in a 2009 report on the sustainability and competitive position of biobased
chemicals. They found that “local variation in GHG emissions of platform chemicals
[ethanol and soy biodiesel] are significant due to tillage practices, irrigation, soil types
and fuel consumption” [9].
It is therefore necessary for an LCA practitioner to consider and adjust based on
the difference between both the geographical and the technological coverage of the
dataset they are using in a situation such as this. However, this is more easily said than
done. Variation between geographical areas can be complex and difficult to model. One
study that tracked water consumption in ethanol production from corn in 81 different
watersheds in Minnesota found significant variation in the range of water consumption
6

between watersheds. The study included the use of both irrigation water and process
water. The range in the amount of water used to produce 1 L ethanol was found to be 3
– 181 L in a watershed in central Minnesota. Contrastingly, ethanol produced in a
watershed in the south of the state had a much smaller range in the amount of water
used. Farms and production facilities based in the southern watershed used only 3 - 8 L
of water to produce 1 L ethanol from corn [10]. This demonstrates that the issue of
geographical correlation is not as simple as one might assume. LCA practitioners are
often compelled to use data from a different region than the one under study.
Generally, an effort is made to use data from a region with similar geography and
practices, but in this case large variation was found within a single region that was
utilizing relatively uniform technology. This suggests not only that it is quite difficult to
predict a correlation between regions, but also that even data from a slightly different
part of the same region may not be an accurate substitute.
This issue of hidden complexity is not isolated to a single incident or a single
input. There are cases of disagreement among researchers about the amounts of fossil
fuels that are used in the production of biopolymer feedstocks. A study by Reijnders and
Huijbregts in 2007 concluded that fossil fuels are most often used as a power source in
the production of palm oil based biodiesel, and generally represent 75% of the fuels
used in this process. This assumption is not consistent with information from Malaysia
and Indonesia which asserts that the main source of energy for processing of palm oil is
from combustion of palm residues, mainly shells and fibers, and that as little as 2% of
process energy comes from fossil fuels [11]. This is just one example of an instance
where uncertainty surrounding the technology being used sparked debate about what

7

data should be used to model a system, but it is a significant one. The variation of fossil
fuel use in turn affects the greenhouse gas emissions of the system, in this case by as
much as 21% [11].
Another example surrounding the uncertainty of greenhouse gas emissions in
relation to biomass production springs from the issue of precision. In an article about
sampling error in US crop surveys, Cooper et al. presents this example: “consider…a
comparison of the life cycles of a conventional fuel and a biofuel in which the
conventional fuel has an estimated mean greenhouse gas emission of 47 grams of
carbon dioxide equivalent per mega joule (g CO2eq/MJ) and the biofuel of 38 g
CO2eq/MJ. Without consideration of variability, the biofuel is found superior to the
conventional fuel” [12]. However, the article goes on to explain how the relative
standard errors (RSE) of the data these means are based on affect the viability of this
assertion. “Thus, without knowledge of the error and sample sizes, the comparison of
greenhouse gas emissions can be meaningless” [12]. Although this example is
theoretical, the paper presents a strong case for caution when making these
comparisons, and illustrates the effect data precision has on study results.
Recently, more attention has been focused on the impacts of agriculture that are
harder to measure, and therefore for which data are more difficult to obtain. The
inclusion of these impacts has the potential to significantly change the results of an LCA
study, and they bring with them additional challenges in data quality that researchers
must overcome. In a study about the effect of land use change (LUC) on GHG emissions,
Piemonte et al. point out the effects LUC has on the environmental impact of a
bioplastic: “If, on one hand, the bioplastics can save in terms of fossil resources and GHG
8

emissions, on the other, agricultural biomass production might cause adverse
environmental effects such as soil erosion, eutrophication of ground and surface waters,
or fragmentation of habitats” [4].
There is generally agreement that these types of impacts are important to
understanding the true environmental effects of biopolymers. Emissions from LUC, both
direct and indirect, may be especially important in understanding local and regional
effects of biomass production, but it is difficult to measure and quantify them, so they
are often excluded for practical reasons [13]. The effects of indirect land use change
(displacing previous agricultural production to other land) have been estimated to span
a large range of values, varying from a small effect (10 kg CO2-eq/GJ ethanol) to one
that is several times greater than the life cycle emissions of CO2 for gasoline (340 kg
CO2-eq/GJ ethanol) for the same feedstock [13]. The latter is obviously a concern, since
one of the main attributes of biopolymers that make them attractive is their ability to
mitigate GHG emissions. These data gaps impact the ability of LCA studies to accurately
describe a system. As Weiss et al conclude in “A Review of the Environmental Impacts of
Biobased Materials,” “The variability in the results of life cycle assessment studies
highlights the difficulties in drawing general conclusions” [13]. Certainly, there are many
sources of variability and many reasons why LCA results vary. However, it is just as
certain that the availability of appropriate data is one of these sources.

9

3. METHODS
In order to identify critical data gaps for bio-based plastics, a review of currently
available data was conducted. Information was collected from the LCA databases
Ecoinvent, GaBi-PE (Ecoinvent data modified by GaBi), USLCI, LCA Food DK, and LCA
Commons. The software programs SimaPro and GaBi were used to access all of these
databases except LCA Commons, which has not been integrated into the software
programs. The websites of LCA Food DK and USLCI were also searched for information.
Additionally, publications were searched for relevant data that had not yet been
integrated into the databases. Since it is not feasible to search all publications, a
selection of publications was chosen based on the likelihood that they would contain
relevant information. Table 1 contains the list of publications and timeframes searched.
Table 1 - List of publications searched
Publication Name

Time Frame

Journal of Cleaner Production

2013-2005

International Journal of LCA

2012-2000

Bioresource Technology

2012-2005

Biomass and Bioenergy

2012-2005

Environmental Science and Technology

Selected

Packaging Technology and Science

Selected

Journal of Industrial Ecology

2012-2008

Sustainability

2012-2005

Science

2012-2008

PNAS

2012-2005

10

All data pertaining to feedstocks that are viable for use in the production of biobased plastics were considered in the search. These data relate to many aspects of
bioplastic production, including raw agricultural feedstocks (i.e. corn, soybeans) through
processing steps (i.e. sugar or oil), platform chemical production (i.e. ethanol and
biodiesel), and polymer production. The relevant data sources were collected into a
summary table. The table organizes data sources by category (raw agricultural,
processed agricultural, wood, chemical, and polymer), and feedstock material.
The table also captures other information about the data that would be of
interest to a practitioner who is considering using one of the datasets, including where
the data is available, the details of the study (if applicable), the year(s) of publication,
and the region to which the data pertains. The data were also checked against common
agricultural inputs and the results catalogued in the summary table. A “1” in the column
of the indicated input means that the dataset in that row contains a flow for that input.
For example, if a soy data file has a “1” in the column marked “tillage” that means the
process of tilling the field and associated inputs are included in that data file. In this way,
a preliminary completeness check was conducted on all the data collected in the
summary table.
Because of the large number of data sources amassed, it was necessary to narrow
the focus of the project in order to conduct a deeper analysis of the data. Corn,
sugarcane, and soy were the feedstocks selected to be analyzed in more detail. These
feedstocks were chosen based on their current use as biopolymer feedstocks. The first
step was to conduct a completeness check on the available data for these crops.
Information from ISO 14044 and the ILCD handbook was used to inform this process [2,
14]. The completeness check involved searching each data file in detail and noting what
11

inputs were considered and, when possible, how they were accounted for. For the
database files, this meant looking through the data directly, at the flow level. For the
data contained in journal articles, it involved searching for information within the article
and looking up data source information from the references. An effort was made to
include all significant input categories for these feedstocks in the check. The input
categories that were included in this check are as follows: carbon sequestration, seed
production, soil preparation, transport of materials to the farm, fuel used on the field,
power for farm activities, machines, machine shelter, sowing, tilling, fertilizer, pesticide,
herbicide, lime, crop residue management, irrigation, harvesting, grain drying, direct
field emissions, crop storage, land occupation, and land use change.
Additionally, several inputs relating to biomass processing were considered in the
completeness check. These were considered separately, so some data files underwent
two completeness checks. The processing inputs include: loading, transportation of the
crop to the refinery, process chemicals, waste treatment, infrastructure, facility land use,
processing machinery, process energy, extraction and milling, refining, process water,
fermentation, and drying. Not all inputs were relevant to all data sets. For example, a
study that only considered GHG emissions would not include water use. It was noted in
the completeness check results when an input was specifically excluded from a study
because it was not within the study scope.
Next, a pedigree matrix scoring system was used to evaluate the data sets for
corn, soy, and sugarcane. The data were evaluated on their technological, geographical,
and temporal representativeness on a scale from 1-5. When information was available,
data uncertainty was also considered in the evaluation. From this evaluation and the
completeness evaluation, a Data Quality Rating (DQR) was calculated. Agricultural data
12

was considered separate from processing data. Some datasets therefore have two DQRs,
one for agricultural data quality, and one for processing data quality. The evaluation
criteria and the formula used to calculate the overall score are were adapted from the
ILCD Handbook and van der Berg et al. [14, 15]. Table 2 explains the different categories
considered in the evaluation of the data, and Tables 3 and 4 explain the ranking of the
quality ratings [14, 15].
It was necessary to consider the quality ranking recommendations of both the
ILCD and van der Berg et al. in order to complete a consistent analysis. The ILCD
rankings are described in general terms, and do not have category-specific
requirements. Therefore, the criteria put forth by van der Berg et al. were used to
supplement the ILCD recommendations. Specific category ranking requirements from
van der Berg et al. were applied. For example, data that was less than three years older
than the study date was given a score of 1 in temporal correlation. It should be noted
that the category “Reliability” from van der Berg et al. and the category
“Precision/Uncertainty” from the ILCD handbook are equivalent. The ILCD handbook
also includes another evaluation category, Methodological Appropriateness and
Consistency, which was not used in this evaluation. This category was excluded because
it is dependent on the goal and scope of the intended application of the dataset.

13

Table 2 – Definition of evaluation categories adapted from the ILCD Handbook
Indicator / Component

Definition / Comment

Technological
Representativeness (TeR)

“Degree to which the data set reflects the true population of interest
regarding technology, including for included background data sets,
if any.”

Geographical
Representativeness (GR)

Time Related
Representativeness (TiR)

Completeness (C)

“Degree to which the data set reflects the true population of interest
regarding geography, including for included background data sets,
if any.”
Comment: i.e. of the given location / site, region, country, market,
continent, etc.
“Degree to which the data set reflects the true population of interest
regarding time/ age of data, including for included background data
sets, if any.”
Comment: i.e. of the given year (and - if applicable – of intra-annual
and intra-daily differences).
“Share of (elementary) flows that are quantitatively included in the
inventory. Note that for product and waste flows this needs to be
judged on a system’s level.”
Comment: i.e. degree of coverage of environmental impact i.e. used
cut-off criteria.

Precision / uncertainty (P)

Measure of the variability of the data values for each data expressed
(e.g. low variance = high precision). Note that for product and waste
flows this needs to be judged on a system level.

14

Table 3 –Quality Rating Definitions adapted from the ILCD Handbook
Quality Level

Quality
Rating

Very Good

1

Good

2

Fair

3

Poor

4

Very Poor

5

Not Applicable

0

Definition
“Meets the criterion to a very high degree, having no relevant
need for improvement. This is to be judged in view of the
criterion’s contribution to the data set’s potential overall
environmental Impact and in comparison to a hypothetical
ideal data quality.”
“Meets the criterion to a high degree, having little yet
significant need for improvement. This is to be judged in view
of the criterion’s contribution to the data set’s potential
overall environmental Impact and in comparison to a
hypothetical ideal data quality.”
“Meets the criterion to still sufficient degree, having the need
for improvement. This is to be judged in view of the
criterion’s contribution to the data set’s potential overall
environmental Impact and in comparison to a hypothetical
ideal data quality.”
“Does not meet the criterion to a sufficient degree, having
the need for relevant improvement. This is to be judged in
view of the criterion’s contribution to the data set’s potential
overall environmental Impact and in comparison to a
hypothetical ideal data quality.”
“Does not at all meet the criterion, having the need for very
substantial improvement. This is to be judged in view of the
criterion’s contribution to the data set’s potential overall
environmental Impact and in comparison to a hypothetical
ideal data quality.”
Criteria could not be applied

15

Table 4 –Pedigree Matrix Data Quality Rating Level Definitions adapted from van
der Berg et al.
1

2

3

4

5

Verified
data based
on
measureme
nts

Verified data
partly based
on
assumptions or
non-verified
data based on
measurements

Non-verified
data partly
based on
assumptions

Qualified
Estimate (e.g.
by industrial
expert)

Non-qualified
estimate

Representativene
ss unknown or
incomplete data
from a smaller
number of sites
and/or from
shorter periods

Representative
data from a
smaller
number of
sites but for
adequate
periods

Representativ
e data from
adequate
number of
sites but from
shorter
periods

Representativ
e data but
from a smaller
number of
sites and
shorter
periods or
incomplete
data from an
adequate
number of
sites and
periods

Less than
three years
difference
to year of
study

Less than six
years
difference

Less than 10
years
difference

Less than 15
years
difference

Age of data
unknown or
more than 15
years difference

Data from
area with
similar
production
conditions

Data from
area with
slightly similar
production
conditions

Data from
unknown area or
area with very
different
production
conditions

Data from
processes and
materials
under study
but from
different
technology

Data on
related
processes or
materials but
same
technology

Data on related
processes or
materials but
different
technology

Geographical
Correlation

Representati
ve data
from a
sufficient
sample of
sites over an
adequate
period to
even out
normal
fluctuations

Data from
area under
study

Technological
Correlation

Temporal
Correlation

Completeness

Reliability

Indicator
Score

Data from
enterprises,
processes,
and
materials
under study

Average data
from larger
area in which
the area under
study is
included
Data for
processes and
materials
under study
but from
different
enterprises

16

Data was examined in the greatest detail possible when scoring each category
since most datasets have multiple sources of information that can be of varying quality.
These differences are accounted for by scoring each source of data and then resolving
their respective scores into a single category score. Background process data were
weighted less heavily when resolving scores, as were data that are not sensitive to a
specific category. For example, using a US electricity mix for sugar production in Brazil is
a significant difference because Brazilian sugarcane processing is generally powered by
the burning of bagasse, a sugarcane co-product. This substitution therefore represents a
significant technological difference. However, if US diesel tractor emissions were
substituted for Brazilian diesel tractor emissions, the difference is not as significant since
the technology is nearly identical.
Additionally, half scores were given to categories when deemed appropriate. For
example, a dataset published in 2010 for which half of the relevant data was from 2009
(less than 3 years difference) and half was from 2005 (less than six years difference)
would be given an overall score of 1.5 in the category of temporal correlation. In order
to ensure transparency, each score is reported with its individual justification.
Equation 1 was used to calculate the DQR for each dataset [14]. Note that the
lowest criteria score is weighted in the formula by five-fold. This is done because the
weakest quality indicator significantly weakens the overall quality of the dataset being
evaluated [14]. The Precision (P) quality indicator was only included in the calculation of
the DQR for Ecoinvent data. This was necessary due to the lack of information about
precision for most datasets. When evaluating the precision of the Ecoinvent data, the
Ecoinvent uncertainty scoring criteria were used to interpret the uncertainty scores for
17

each flow [16]. Then, a single uncertainty score was chosen for the file based on this
interpretation. An explanation of these criteria can be found in Appendix A.
A dataset with DQR less than or equal to 1.6 is considered high quality, while a
dataset with a DQR between 1.6 and 3 is considered to be of basic quality. Any dataset
with a DQR between 3 and 4 is considered to be an estimate [14].
Equation 1 – Data Quality Rating Formula and Definitions from the ILCD
Handbook
DQR = TeR_+_GR_+_TiR_+_C_+_P_+_M_+_(Xw*4)
i+4
DQR: Data Quality Rating of the LCI data set
TeR, GR, TiR, C, P, M: see table 2
Xw: weakest quality level obtained (i.e. highest numeric value) among the data
quality indicators
i: number of applicable data quality indicators

18

4. RESULTS AND DISCUSSION
Data Availability
The summary table containing the details of all the collected datasets can be
seen in Appendix F. Table 5 describes the type and number of entries it contains.
Table 5 – Quantity and type of data collected

Feedstock:
Wood
Agricultural
Residues
Corn
Wheat
Canola /
Rapeseed
Soy
Other
Sugarcane
Palm
Potato
Barley
Sugarbeet
Switchgrass
Rye
Algae
Cassava
Dedicated
Energy
Tree
Oat
Rice
Sorghum
Blood meal
Waste Oils
Total

Raw
Processed
Chemical
Agricultural Agricultural Feedstocks
Total Feedstocks Feedstocks (Ethanol/Biodiesel) Polymers
60
0
54
6
0
42
22
21

21
8
20

15
4
0

6
6
1

0
4
0

20
18
17
13
11
11
9
6
9
5
4
3

12
9
2
3
0
8
9
2
4
4
0
0

5
5
4
5
8
2
0
1
3
0
3
0

3
3
11
4
3
1
0
3
2
1
1
3

0
1
0
1
0
0
0
0
0
0
0
0

3
3
3
3
2
2
287

0
3
3
2
0

2
0
0
0
1
2
114

1
0

0
0
0
0
1
0
7

110

19

1
0
0
56

A total of 287 datasets were collected. It should be noted that in the case where
one study contained multiple data sets each was counted individually. For example, one
study compared US corn, UK sugar beet, and Australian sugarcane as producers of sugar
for fermentation [17]. This study contained datasets for the cultivation of each of these
crops, so three datasets were counted (one for sugarcane, one for corn, and one for
sugar beet). Likewise, a study with both cultivation data and processing data for corn
would contribute two datasets to the above count, one in the raw agricultural feedstock
category and one in the processed agricultural feedstock category.
Of the 287 datasets found, 110 are for raw agricultural feedstocks, 114 are for
processing of agricultural feedstocks, and 56 are for production of platform chemicals
(mainly ethanol and biodiesel). Only seven datasets were found for the complete
production of biopolymers. Figure 1 illustrates the number of datasets found for each
crop. Each bar is a stacked total which represents the composition of the datasets for
each crop by category. Wood and agricultural residues are excluded for reasons of
convenience.

20

Figure 1: Summary of data collected by crop and category. For the interpretation
of the references to color in this and all other figures, the reader is referred to the
electronic version of this thesis.

Data Summary by Crop
Raw Agricultural Feedstocks
Processed Agricultural Feedstocks
Chemical Feedstocks (Ethanol / Biodiesel)
Polymers
Waste Oils
Bloodmeal
Sorghum
Rice
Oat
Dedicated Energy Tree
Cassava
Algae
Rye
Switchgrass
Sugarbeet
Barley
Potato
Palm
Sugarcane
Other
Soy
Canola / Rapeseed
Wheat
Corn
0

5

10

15

20

25

The feedstock with the largest amount of datasets is wood, with a total of 60.
However, this is largely because the same base data is available in multiple iterations.
For example, data for board trimmings, sawdust, and woodchips in the US Pacific
Northwest are all available and build off of the same base data. Therefore the high total
is somewhat misleading. Agricultural Residue is the feedstock with the second highest
21

amount of datasets collected at 42. This category includes things like corn stover,
sugarcane bagasse, and wheat straw. This number is almost exclusively made up of
Ecoinvent files. Ecoinvent has separated the data associated with the co-products of
crop production into separate files, which means that for each crop Ecoinvent has data
available, it also has data for the associated agricultural residue. Here, all of these
residues are collected into a single category, which is why they are so numerous.
The crop with next largest amount of available data is corn (22), which is closely
followed by wheat (21), rapeseed (20), and soy (18). Rapeseed and canola are grouped
together because canola is a specific type of rapeseed suitable for human consumption
[18]. The category “other” is mostly composed of data for ethanol from mixed
feedstocks, which is available from Ecoinvent, and generic data for unspecified biomass.
Sugarcane (13) has slightly less data available in relation to the other commodity crops
commonly used to produce biopolymers. It should also be noted that no applicable
datasets could be found for chitosan, despite the fact that a search was specifically
conducted for this material. It should also be noted that over half (4 of 7) of the polymer
datasets are based on corn.
The table and figure show that 8 datasets are available for raw corn. However, the
131 datasets available for corn from the US database LCA Commons are represented by
a single entry in the summary table. This was done for reasons of practicality, since
datasets in LCA Commons are state specific for a single harvest year. The short data
collection time period is a quality issue, but 18 of the 131 data sets are aggregated over
multiple years. These aggregated datasets are still state specific; LCA Commons does
not have a file meant to represent averages for the entire USA. LCA Commons datasets

22

are also included for soy (137), oats (12), rice (6), and wheat (155). They are represented
in the table as described above.
Geographically, the data is skewed to Europe and North America with about 60%
of the datasets (175 of 287) from one of the two regions (105 for Europe and 70 for
North America). Additionally, about half of the European data (58 of 106) is for
Switzerland. This is likely related to the fact that the Ecoinvent project is based in
Switzerland. Likewise, nearly all of the North America data is for the USA. Only one of
the 70 datasets for North America is explicitly for another country, a study about
biodiesel production in Costa Rica. There are a number of USLCI datasets whose region
is described in general as “North America” but these files are more reflective of the USA
than of North American averages, being mainly built off of US data and modeling
technology typical of the US. Figure 2 describes the regional breakdown of the data,
while Table 6 gives a detailed breakdown of data availability by country.

23

Figure 2 – Geographical Distribution of Datasets

Geographic Distribution of Datasets
Raw Agricultural Data
Platform Chemical Data
70

Processed Agricultural Data
Polymer Data

64

60
50
38

40
30
20
10

19 18

16 15
9
4

1

13
8
1

7
3 5
1

0
Europe

North
America

Asia and
Middle East

24

South
America

1

5

1 0

Oceania

0 0 0 0
Africa

Table 6 – Data Availability by Country
Raw
Agricultural
Data
Count
Switzerland
39
USA
29
North
America
9
UK
7
Denmark
7

Processed
Agricultural
Data
USA
Malaysia

7
4
4
4
3
3
3
3
2
2
1

4
4
3
3
3
2
2
2

Australia
Iran

1 Indonesia
1 New Zealand

Taiwan

Thailand
Unspecified
China
Sweden
Argentina
Australia
Costa Rica
Denmark

Count
12
7

Switzerland
Australia
Brazil
North
America
Europe
General
Germany
UK
China
Denmark
Argentina

China
Germany
Unspecified
France
India
Brazil
Europe
Spain
Average:
USA and
Europe
Argentina

Chemical
Data
Switzerland
Brazil
North
America
USA
Europe

1 Philippines
Thailand

Polymer
1 Data
1 USA

2 France
1 India

1 Brazil
1 Europe
New
1 Zealand
1

Count
10
7
7
7
5
4
4
3
2
1
1
1
1
Count
4
1
1
1

As illustrated by Figure 2, the most obvious geographical data gap is that zero
datasets were found that represent anywhere on the continent of Africa. Additionally,
the category “Asia and the Middle East” covers a very large amount of area in theory,
but in reality the datasets are focused on Southeast Asia and China. Only one dataset,
potato production in Iran, breaks this pattern. The data designated Oceania is
composed of one dataset for New Zealand with the remainder representing Australia.
The South American data consists of entries from Brazil and Argentina, and is mostly for
25

sugarcane and soybeans. In general, the geographical concentration of the datasets is in
line with the demand for LCA data in each region. It makes sense that Europe and the
USA, which both have strong policies in place that promote biofuels and LCI database
projects, have more data available. Furthermore, the pattern of data concentration is
consistent with areas that produce large amounts of commodities that are commonly
used to produce biofuels and biopolymers.
A notable exclusion is that neither Canada nor Russia is represented in the
datasets. The two largest countries in the world, Canada and Russia are both also major
producers of grains that are commonly used to produce biodiesel and ethanol. Canada
is the largest producer of canola, and Russia was the second largest producer of wheat
in 2005 [19, 20]. The largest producer of wheat in 2005, India, is also under-represented
in data availability [20]. There are four datasets for India but none of them are for wheat.
Three are for agricultural residues suitable for cellulosic ethanol production (jute and
kenaf stalks), and the fourth is for jatropha (an oilseed crop that can be grown on
degraded land) [21].

26

Quality Analysis
A data quality evaluation was undertaken for cultivation of corn, soy, and
sugarcane. The full results of this evaluation with score justifications can be seen in
Appendix C. The processing data for these crops was evaluated separately, and results
of this evaluation can be found in Appendix E. Full results of the completeness check for
agricultural data are listed in Appendix B, and Appendix D contains the completeness
check results for the processed agricultural data. It should be noted that some studies
listed in the summary table for these crops were excluded from this evaluation. This was
done if it was discovered during the completeness check that the data was taken from
another source already under evaluation. For example, a sugarcane study that was built
off the Ecoinvent file “sugarcane, at farm” would be excluded because that dataset was
already included.
Figure 2 illustrates the composition of the corn DQRs by breaking them down
into their constituent categories of completeness, uncertainty, and technological,
geographical, and temporal representativeness. Figures 4 and 5 illustrate the same
thing, but for sugarcane and soy, respectively. Tables 7, 8 and 9 match the identifiers in
the figures with the corresponding dataset names for each feedstock.

27

Table 7 –Corn Dataset Names and Identifiers
Corn Dataset Name:

Identifier

Annual Report: Life Cycle Assessment to Improve the Sustainability
and Competitive Position of Biobased Chemicals: A Local Approach

A

Grain maize organic, at farm (Ecoinvent)

B

Silage maize organic, at farm (Ecoinvent)

C

Corn grain, at harvest in (year), at farm 85-91% moisture (state) (LCA
Commons)

D

Regional variations in GHG emissions of bio-based products in the
United States - Corn based ethanol and soybean oil

E

Corn, at farm (Ecoinvent)

F

Grain maize IP, at farm (Ecoinvent)

G

Silage maize IP, at farm (Ecoinvent)

H

Measuring ecological impact of water consumption by bioethanol
using life cycle impact assessment

I

Life cycle assessment of various cropping systems utilized for
producing biofuels: Bioethanol and Biodiesel

J

Improvements in Life Cycle Energy Efficiency and Greenhouse Gas
Emissions of Corn-Ethanol

K

LCA of cropping systems with different external input levels for
energetic purposes

L

Corn, whole plant, at field (USLCI)

M

Corn, production average, US, 2022 (USLCI)

N

28

Figure 3 – Corn Data Quality Ratings and Component Scores by Dataset
0

1

2

3

4
Technological
Rep

A
1.4

Geographical
Rep

B
1.6

Time Rep

C
1.6

Completeness

D
1.6

Uncertainty

1.6

Data Quality
Index

E
F
1.7
G
1.7

H
1.7
I
1.9
J
1.9
K
2.0
L
2.5
M
2.5
N
2.6
Average
1.9

29

Table 8 – Sugarcane dataset names and identifiers
Sugarcane Dataset Name:

Identifier

Life Cycle Assessment of fuel ethanol from sugarcane in Brazil
A comparative Life Cycle Assessment of PE Based on Sugar Cane and Crude
Oil
an environmental life cycle assessment comparing Australian sugarcane
with US corn and UK sugar beet as producers of sugar for Fermentation

O

Carbon footprint of sugar produced from sugarcane in eastern Thailand

R

Bioproduction from Australian sugarcane: an environmental investigation
of product diversification in an agro-industry

S

Sugarcane, at farm/BR (Ecoinvent)

T

Life cycle assessment of Australian sugar cane production with a focus on
sugarcane growing
A decision support tool for modifications in crop cultivation method based
on LCA: a case study on GHG emissions reduction in Taiwanese sugarcane
cultivation
Life Cycle Assessment of Sugarcane Ethanol and Palm Oil Biodiesel Joint
Production

P
Q

V
V
W

Table 9 – Soy dataset names and identifiers
Soy Dataset Name:
Soy beans organic, at farm/CH (Ecoinvent)
Soy beans IP, at farm/CH (Ecoinvent)
Soybeans; at harvest in (year); at farm; 85-92% moisture (state) (LCA
Commons)
Life cycle assessment of soybean based biodiesel production in Argentina
Soybeans, at farm/US (Ecoinvent)
Substitutable biodiesel feedstocks for the UK: a review of sustainability
issues with reference to the UK RTFO
Soybean grains, at field (1998-2000)/kg/US (USLCI)
Soybeans, at farm/BR (Ecoinvent)

Identifier
X
Y
Z
AA
BB
CC
DD
EE

Soybean grains, at field/kg/US (USLCI)

FF

Biogenic greenhouse gas emissions linked to the life cycles of biodiesel
derived from European rapeseed and Brazilian soybeans
Soy bean, from farm (LCA Food DK)

GG
HH

30

Figure 4 - Sugarcane Data Quality Ratings and Component Scores by Dataset
Technological Rep

Geographical Rep

Time Rep

Completeness
0
1

Uncertainty
2

Data Quality Index
4
5

3

O
1.8
P
2.1
Q
2.1
R
2.1
S
2.4
T
2.5
V
2.4
V
3.6

W
3.7
Average

2.5

31

Figure 5 - Soy Data Quality Ratings and Component Scores by Dataset
Technological Rep

Time Rep

Completeness
0

Geographical Rep
Uncertainty

Data Quality Index

1

2

3

4

5

X
1.6
Y
1.7
Z
1.6
AA
1.8
BB

2.3
CC
2.3
DD
2.4
EE
2.5
FF
2.5
GG
2.8
HH
4.5
Average
2.4

32

Overall, the DQRs for the corn datasets ranged from 1.4 to 2.6, with five of the
datasets ranked in the high quality range and the remaining eight datasets in the basic
quality range. The sugarcane datasets ranged from 1.8 to 3.7, with seven of the datasets
in the basic quality range and the remaining two classified as estimates. Sugarcane has
no datasets with a DQR in the high quality range. These less desirable DQRs for
sugarcane are mainly a result of poorer scores in the category of temporal
representativeness. The soy datasets have the largest range of DQRs, from 1.6 to 4.5.
Two of the datasets are classified as high quality, with the majority (eight) in the basic
quality range. The dataset “Soy bean, from farm”, available from the LCA Food DK
database, was the lowest scoring of all the datasets assessed with a DQR of 4.5. This
rating is outside the range defined by the ILCD handbook for the lowest quality level of
estimate (3.0 to 4.0). In the DQR table for soy, it has been given the designation “low
quality estimate”. The low DQR is a product of receiving the undesirable score of 5 in
the categories of both completeness and temporal representativeness. The temporal
score was given because the age of the data is unknown, and the completeness score
reflects the fact that very few flows are present in the dataset and major omissions were
found in both the inputs and the emissions.
Technological Representativeness
The datasets for all three feedstocks consistently scored the best in the
technological representativeness category. This means that data from a different process
than the one under study rarely had to be used as a substitute to fill a data gap. A
technology aspect of corn and soybean cultivation that warrants attention is tilling
practices. The method of tilling can have a large effect on the environmental impacts of

33

a system as it affects the use of fossil fuels and soil degradation, among other issues
[22]. Conventional till, reduced / conservation till, and no-till are the basic methods used
in corn and soy cultivation. In reduced and conservation till, the amount of tilling is
decreased in relation to the conventional method, and a different type of plough is
usually employed, while no-till uses a different planting technology to eliminate the
need to till altogether [22]. A 2010 USDA report estimates that 28.8% of corn in the US
was grown using conventional till practices in 2005 compared to 47.5% grown with
reduced or conservation tillage, and 23.5% which used no-till technology [23].
Ecoinvent uses a weighted average of conventional and conservation till practices
for both corn and soy in their files. Of the 13 datasets evaluated for corn, seven
(including Ecoinvent) reflected average tilling practices at the national level. Two
represent state level averages, and one was varied by county. One dataset is explicitly
for no-till technology. There is no corn dataset specifically representing conservation or
reduced tillage practices, despite the fact that this is the most common tillage practice
in the US.
A higher percentage of soy was produced using no-till in 2005 than corn (45.3%),
while 43.2% used reduced or conservation till technology and the remaining 11.6% was
conventional till. Unlike corn, a dataset modeling conservation till is available for soy.
However the majority of the soy files (seven of eleven) use a weighted average of
conventional and conservation till similar to that used by Ecoinvent.
This weighted average data is not ideal for an LCA study that seeks to model a
specific cultivation system because it does not accurately model any one tillage method.
Additionally, the state averages vary significantly from the national averages. Texas, for
example, produced 68.4% of corn in 2005 using conventional till while Nebraska used
34

conventional till for just 5.7% of its corn production. Therefore, depending on what state
is under study, the national average could either over- or under-estimate the amount of
inputs and emissions associated with tilling by a wide margin.
Sugarcane has other technological issues worth noting, particularly relating to the
method of harvesting. Sometimes sugarcane is burned before harvest, and sometimes it
is harvested green. This difference has an effect on emissions to air, and also on the
amount of process water used during milling [24]. This is because the burned sugarcane
becomes sticky from the release of juices during burning and therefore generally has a
large amount of debris mixed in with the harvested cane. As a result, a more vigorous
washing process is required during processing. The sugarcane datasets vary in the
percentage of cane that is burned before harvest. The Australian datasets reflect the
national average of around 40% burned and 60% green at time of harvest, while a
Brazilian dataset has the opposite ratio of 60% burned and 40% green harvest [24, 25].
Additionally, there is a significant difference between manual and mechanical
harvest. The Ecoinvent file that models sugarcane production in Brazil assumes that 80%
of the harvesting is done manually. In contrast, Australia uses dominantly mechanical
harvesting, which is reflected in the Australian datasets. Harvesting sugarcane is the part
of cultivation that contributes most significantly to global warming [26]. Therefore, data
that accurately represents the method used to harvest sugarcane is important for
accuracy of the final results of an LCA.
Geographical Representativeness
In the category of geographic representativeness the corn datasets scored fairly
well. All but one dataset either represents the area under study or is from a larger area

35

that includes the study area. The exception is a dataset based on a single site field study
conducted in central Italy. The publication associated with this data was intended to
model corn production in the “Mediterranean region”, so the dataset was classified as
from a smaller area within the Mediterranean and therefore was scored at a value of 3
[27].
Four of the corn datasets are based on US average values. This is problematic
because the US is quite large, and therefore subject to regional variations not only in
weather and conditions, but also in technology. For example, the USDA reports that
about 14% of corn grown in 2002 in the US was irrigated [28]. However, this 14% is not
evenly scattered over the entire growing region, but concentrated in certain areas.
Nebraska, for example, has an irrigation rate of 60.6%, much higher than the national
average [29]. A dataset based on national averages would therefore likely underestimate
the amount of water used if the system under study was in Nebraska.
More geographically specific data has the potential to solve this problem, and
some of it is available. Three datasets model corn cultivation at the county level. Of the
three, one is only for water in Minnesota and one is specifically for Scott County, Iowa.
These two datasets will therefore be of limited use to practitioners because of their
geographic and technological restraints. The third dataset, titled “Annual Report: Life
Cycle Assessment to Improve the Sustainability and Competitive Position of Biobased
Chemicals: A Local Approach”, by Kim and Dale is far more versatile in scope [9]. It
includes detailed data for several counties across corn growing states, and also has the
highest DQR (1.4) of all the datasets evaluated. This level of geographic detail is unique
to corn cultivation. State level data is the most geographically specific level of
information available for all other crops in this report. Only the LCA Commons data is
36

available by state for soy cultivation. The rest of the soy datasets reflect national
averages.
This type of regional variation is not unique to the USA. Four of the sugarcane
datasets represent Brazilian production, with two specifically for the state of Sao Paulo.
Three datasets are for Queensland, Australia. This region accounts for 98% of sugarcane
production in Australia, so the lack of data from other areas is not a particularly
important gap [24]. However, there is significant variation in the growing conditions and
intensity of inputs within this region. One study by Renouf et al. includes datasets for
the two areas of Queensland with the most disparate growing conditions [24].
As illustrated by the discussion above, geographical and technological differences
are often strongly related, with quality issues bridging both categories. These types of
quality issues call into the question the usefulness of datasets based on national
averages when there is significant variation within regions.
Temporal Representativeness
Of the three crops evaluated, sugarcane scored the worst in the category of
temporal representativeness with five of the nine sets receiving a rating of 3 or above.
Two soy datasets have a score of 5 in this category; one is based on primary data of an
unknown age, and the other uses a significant amount of data from 1979. Even the
Ecoinvent dataset for sugarcane relies on older data than the Ecoinvent files for corn
and soy, using data for agronomic inputs from 1988. This is uncharacteristic of the
Ecoinvent database, which collected most of their data between the late 1990s and the
mid-2000s.

37

Crop yield data is particularly sensitive to age because of advances in yields over
recent years. A yield increase basically has the effect of diluting the environmental
impacts of a cropping system by spreading them over more outputs. For this reason, the
results of LCA studies involving crops tend to be quite sensitive to yield changes [30].
Therefore, when new data is collected, a high priority should be given to the collection
of updated crop yields.
Completeness
Land use change, and specifically indirect land use change, is the largest problem
in the category of completeness. Four of the corn datasets do not include any type of
land use change data, and only two of the datasets explicitly include indirect land use
change. It should be noted that the database files (Ecoinvent, USLCI, and LCA Commons)
do not distinguish between the types of LUC at the flow level. Therefore, all that was
able to be determined about these files is that they include some land use change data,
but it was not possible to distinguish between direct LUC and indirect LUC for these
datasets.
This data gap is even more pronounced for sugarcane. Five of the ten datasets
evaluated do not account for land use change, and three of those five also do not
include land occupation. The Taiwanese sugarcane dataset does not include land use
change despite the fact that the article itself states that sugarcane production is
expected to expand in the region and that fallow land will likely be converted for
cultivation [30]. Additionally, three of the studies that do include LUC also state that
their information on this input is not complete. One of the Australian studies that
includes land use change presents the data with the qualifier that the methods used to

38

evaluate both LUC and water impacts have significant limitations [31]. Another study
echoes this sentiment when it states that the LUC emissions are uncertain due to lack of
uniform methods [32]. The Ecoinvent file also follows this pattern as there is a high
degree of uncertainty associated with the land use change flows. The soy datasets also
deal inconsistently with this impact, although it is less pronounced than in the
sugarcane data. Four of the eleven soy files do not include indirect LUC, and two of
these also exclude direct land use change.
The significance of this omission varies depending on the system under study. If
the study is for an established growing system that is not expanding, than it would not
be highly important information to include. However, for most of these crops,
production is expanding in response to increased demand for bio-products. This is
especially true in South America, where sugarcane production has expanded in Brazil at
an average rate of approximately 85,000 hectares per year since 1990 and soy
production in Argentina has gone from less than a million hectares to 13 million since
1970 [33, 34].
Uncertainty
As stated in the Methods section, an uncertainty evaluation was only done for the
Ecoinvent data. In general, these datasets had low uncertainty since data is mostly based
on verified measurements. The exception is that all transport distances in the Ecoinvent
datasets are estimates. The Ecoinvent sugarcane datasets also has a greater degree of
uncertainty than the soy or corn Ecoinvent datasets because the sugarcane file’s energy
and carbon dioxide data is partly based on qualified estimates.

39

Additionally, an uncertainty score was given to one USLCI file: “Corn, production
average, US, 2022”. It was possible to score this dataset because it is a qualified estimate
for corn production in the future, and therefore was given the standard uncertainty
score of 4 for qualified estimates.
As discussed above, many studies that included data for LUC expressed concerns
of uncertainty along with that data. It was not possible to give an overall uncertainty
score to these datasets, however, because these concerns were generally expressed only
in qualitative terms and uncertainty information was not available for the other inputs in
the datasets.

Processing Data Quality
Figures 6, 7 and 8 illustrate the results of the processing Data Quality Ratings for
corn, soy, and sugarcane. Overall, the processing datasets for each crop scored worse
than the cultivation datasets. The corn processing datasets have DQRs ranging from 1.6
to 4.1, with only one dataset in the high quality range. The sugarcane datasets range
from 1.9 to 3.6, with three datasets in the basic quality range and four that are
considered estimates. The soy processing data had the tightest range with datasets
scored between 2.3 and 3.1. All but one of the seven soy datasets are in the basic quality
range, with a single dataset qualifying as an estimate.

40

Figure 6 – Corn Processing DQRs and Component Scores by Dataset
Technological Rep

Geographical Rep

Time Rep

Completeness

Uncertainty

Data Quality Rating

0
Measuring ecological impact of water
consumption by bioethanol using life cycle
impact assessment

1

2

3

4

5

1.6

Ethanol, 95% in H2O, from corn, at
distillery/US (Ecoinvent)

2.4

Ethanol, denatured, corn dry mil (USLCI)
2.6
Regional variations in GHG emissions of biobased products in the United States - Corn
based ethanol and soybean oil

2.7

Maize starch, at plant/DE (Ecoinvent)
3.0
corn grain, at conversion plant, 2022 (USLCI)
3.5
Corn, in distillery (GaBi PE)
4.1
Average
2.8

41

6

Figure 7- Sugarcane Processing DQRs and Component Scores by Dataset
Technological Rep

Geographical Rep

Time Rep

Completeness

Uncertainty

Data Quality Rating

0

1

Life Cycle Assessment of fuel ethanol
from sugar cane in Brazil

2

3

4

1.9

Bioproduction from Australian sugarcane:
an environmental investigation of product
diversification in an agro-industry

2.0

Life cycle assessment of australian sugar
cane production with a focus on
sugarcane processing

2.4

A comparative Life Cycle Assessment of
PE Based on Sugar Cane and Crude Oil

3.1

Ethanol, 95% in H20, from sugar cane, at
fermentation plant/BR (Ecoinvent)

3.1
sugarcane, in sugar refinery (GaBi PE)
3.2
Life Cycle Assessment of Sugarcane
Ethanol and Palm Oil Biodiesel Joint
Production

3.6

Average
2.7

42

5

6

Figure 8 - Soy Processing DQRs and Component Scores by Dataset
Technological Rep

Geographical Rep

Time Rep

Completeness

Uncertainty

Data Quality Rating

0

1

Life cycle assessment of soybean based
biodiesel production in Argentina

2

3

4

5

2.3

Soybean oil, at oil mill/US (Ecoinvent)
2.3
Soy oil, refined, at plant/kg/RNA (USLCI)
2.3
Soybean oil, crude, degummed, at plant
(USLCI)

2.4

Soy biodiesel, production, at plant
(USLCI)

2.4

Soybean oil, at oil mill/BR (Ecoinvent)

2.5
Soya oil, at plant/kg/RER (Ecoinvent)
3.1
Average
2.5
The processing data for all three crops scored the worst overall in the category of
temporal representativeness. Part of the reason for this is that at least a portion of many
of the datasets are from industry statistics and unpublished data. Some of these sources
are extremely old and some are of unknown age. One study, “A Comparative Life Cycle
Assessment of PE Based on Sugar Cane and Crude Oil”, uses data from 1981 but verified
that it was still relatively accurate with laboratory experiments.

43

In general, the documentation of data sources was poorer for the processing
datasets than for the cultivation datasets, which is likely because of the use of industry
data for which it is often difficult to obtain details. This issue resulted in a diminished
ability to accurately assess these datasets. The Ecoinvent datasets also follow this
pattern, with high uncertainty relative to the Ecoinvent cultivation files. The combination
of these differences results in the processing data having less desirable DQRs as a whole
than the cultivation datasets.

44

5. CONCLUSIONS
Significant data gaps exist in the availability of life cycle inventory data for
biobased polymers. These gaps occur geographically, technologically, and temporally. In
addition, gaps exist for certain inputs, like land use change, independent of those
qualifying factors. National averages are unlikely to adequately represent either
technology used to cultivate a crop in any specific region or the growing conditions in
that region. There is therefore a need for more regionally explicit data that accurately
models the technology and conditions of a specific system under study. Land use
change is often not accounted for in otherwise relatively complete datasets, which is a
significant quality issue because it can have a large influence on the overall impacts of a
system. More data for land use change is needed, and standardized methods for
collecting and incorporating such data into LCA studies are also necessary. Newer and
better documented processing data is needed for biopolymer feedstocks, and newer
data for the cultivation of feedstocks, especially crop yields, would also be beneficial.
Finally, the currently available data is skewed heavily to Europe and the USA, leaving a
significant portion of the globe with very few datasets available. In conclusion,
understanding the impacts caused by the production of bioplastics is the first step on
the path to a more sustainable bioplastic, and in order to accurately evaluate these
impacts the data gaps described above must be resolved.

45

APPENDICES

46

Appendix A: Ecoinvent Uncertainty Scoring Information
Table 10- Ecoinvent Uncertainty Scoring Information
Indicator Score 1
2
Reliability
Verified data based Verified data partly
on measurements
based on
assumption or nonverified data based
on measurements
Completeness Data from all
Data from >50% of
relevant sites over relevant sites over
adequate period to adequate period
even out normal
fluctuations
Temporal
Less than 3 years
Less than 6 years
Correlation
difference to
difference to
reference year
reference year
(2000)
(2000)
Geographical
Data from area
Average data from
Correlation
under study
larger area in which
the area under
study is included
Further
Data from
Technological
enterprises,
Correlation
processes and
materials under
study (i.e. identical
technology)

Sample Size

>100

>20

3
Non-verified data
partly based on
qualified estimates

Data from only
some relevant sites
(<50%) or >50% of
sites but from
shorter periods
Less than 10 years
difference to
reference year
(2000)
Data from smaller
area than are
under study, or
from similar area
Data on related
processes or
material but same
technology, or
from processes and
materials under
study but different
technology
>10
47

4
Qualified estimate
(e.g. by industrial
expert; data derived
from theoretical
information
Representative data
from only one
relevant site or some
sites but for shorter
periods
Less than 15 years
difference to
reference year (2000)

5
Non-qualified estimate

Data on related
processes or markets
but different
technology, or data
on laboratory scale
processes and same
technology

Data on related
processes or materials
but on laboratory scale
of different technology

>=3

unknown

Representativeness
unknown or data from
a small number of sites
and shorter periods
Age of data unknown or
greater than 15 years
different from
reference year (2000)
Data from unknown or
distinctly different area

Appendix B: Completeness Check

Includes

Data Country

Data Source

Ecoinvent

1 kg corn grain functional
unit (water content 14%,
carbon content .375 kg/kg
fresh mass, biomass energy
content 15.9 MJ/kg fresh
mass, Yield 9315 kg/ha).
Emissions of N2O and NH3
to air are calculated with
emission factors from NREL
2006. Emission of nitrate to
water is calculated with a
nitrogen loss factor of 32%

includes cultivation of corn in
the USA including use of diesel,
machines, fertilizers, and
pesticides

Modeled for USA

modeled
with data
from
literature

USLCI

Harvested acres represent
91% of the planted acres.
The impacts of producing
1kg seed are assumed to be
equal to producing 1 kg
grain. Only consumptive use
of water taken into account.
This model uses
"conservational" or
"reduced" tillage.

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting. Carbon
sequestration is credited.
Diesel use in industrial
equipment included along with
electricity and quicklime.
Transportation of fertilizers to
the farm (400km) by train.

USA

Corn, whole
plant, at
field/kg/US
(Corn, at
field/kg/US)

Details

Data Years

Corn, at
farm/US S Corn, at
farm/US U

Data
Availability

Time of publications

Name

1998-2000

Table 11 - Corn Cultivation Completeness Check

Primary

48

49

Swiss lowlands

Ecoinvent

Includes the processes of soil
cultivation, sowing, ,
fertilization, harvest and drying
1 kg grain maize IP at form
of the grains. Machine
with moisture content of
infrastructure and shed for
14%. Fresh matter yield/ ha machine sheltering is included.
9279kg. Average production Inputs of fertilizers, pesticides
in Swiss lowlands with
and seeds as well as their
integrated production. 1996transports to the regional
2003 data collection
processing center (10km), and
direct emissions on the field.
Land occupation (non-irrigated)
is included.

Statistics,
pilot
network,
fertilizing
recommenda
tions, and
expert
knowledge

Swiss lowlands

Grain maize
organic, at
farm/CH S Crain maize
organic, at
farm/CH U

Ecoinvent

1996-2003

Grain maize
IP, at
farm/CH S Grain maize
IP, at
farm/CH U

Includes the processes of soil
cultivation, sowing, weed
control, fertilization, pest and
1 kg grain maize IP at form
pathogen control, harvest and
with moisture content of
drying of the grains. Machine
14%. Fresh matter yield/ ha
infrastructure and shed for
9279kg. Average production machine sheltering is included.
in Swiss lowlands with
Inputs of fertilizers, pesticides
integrated production. 1996and seeds as well as their
2003 data collection. This
transports to the regional
model is non-irrigated.
processing center (10km), and
direct emissions on the field.
Land occupation (non-irrigated)
is included.

1996-2003

Table 11 (cont’d)

Statistics,
pilot
network,
fertilizing
recommenda
tions, and
expert
knowledge

LCA
Commons

LCA Commons has very
specific datasets for each of
the states listed for multiple
years. Not all states have
data for all years.

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use.

50

Swiss Lowlands

1 kg silage maize IP, at farm.
Moisture content 72%

USA: various states

Corn grain,
at harvest in
(year), at
farm 8591%
moisture
(state)

Ecoinvent

1996-2003

Silage maize
IP, at
farm/CH S Silage maize
IP, at
farm/CH U

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm are
considered. Direct emissions on
the field are also included.

1995-2001, 2005

Table 11 (cont’d)

Statistics,
pilot
network,
fertilizing
recommenda
tions, and
expert
knowledge

Ecoinvent

1 kg silage maize organic, at
farm. Moisture content 72%.

51

Swiss Lowlands

Silage maize
organic, at
farm/CH S Silage maize
organic, at
farm/CH U

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm (1km)
are considered. Direct
emissions on the field are also
included. Carbon sequestration
accounted for.

1996-2003

Table 11 (cont’d)

Statistics,
pilot
network,
fertilizing
recommenda
tions, and
expert
knowledge

Life cycle
assessment
of various
cropping
systems
utilized for
producing
biofuels:
Bioethanol
and
Biodiesel

Biomass and
Bioenergy
2005

Functional unit 1 Ha arable
land used to produce
biomass. Corn monoculture
with ground cover, corn
monoculture with no stover
removal and no ground
cover, and corn / soybean
rotation are modeled. No till
agriculture is assumed for all
scenarios. Wet milling used
to convert corn to ethanol.
System expansion method is
used to deal with coproducts.

agronomic inputs, fuel used in
machines, harvesting, wet
milling, conversion of stover
into ethanol, soybean milling,
electricity, fertilizers

52

USA (Scott County Iowa for corn data)

Table 11 (cont’d)
Soil carbon
and nitrogen
estimated by
DAYCENT
model.
National
Agricultural
Statistics
Service
(agronomic
inputs).
Climate and
soil data
from crop
site (primary)

53

Italy

International
Journal of
LCA Sept
2009

Corn ethanol represents dry
Direct land use change is
milling process, and Soybean
included. It is measured and
oil was made using the
the change in soil carbon
crushing process. GHG only
organic material. Includes
reported category. Data is at
impacts associated with the
the county level, 40 counties
biomass, bio-refining, upstream
in the US corn belt were
processes.
chosen.

Tilling, fertilizer, weed control,
pest control, seed production,
transport of inputs to farm,
planting, irrigation, harvest.
Fuel used in cultivation,
machine manufacture and
maintenance, direct field
emissions,

Primary data
/ field study.
Literature
data used for
machine
production
and
transportatio
n of inputs
(Bentrup et
al 2004,
Audsley et al
1997)

USA

Regional
variations in
GHG
emissions of
bio-based
products in
the United
States Corn based
ethanol and
soybean oil

Journal of
Biomass and
Bioenergy,
July 2012

LCA to evaluate
environmental impacts of
sunflower and maize, both in
rotation with wheat. Reports
GWP, eutrophication, and
acidification. Three scenarios
were studied: Low input,
medium input, and high
input.

2009

LCA of
cropping
systems
with
different
external
input levels
for
energetic
purposes

17-year period, beginning in 1985

Table 11 (cont’d)

Corn
cultivation
data is from
(Kim and
Dale 2009)

water consumption during corn
cultivation and ethanol
production

54

USA (Midwest)

International
Journal of
LCA Jan 2012

Ecological impacts of corn
based ethanol - water
consumption in 81 spacially
explicit Minnesota
watersheds. Water data
taken from Minnesota
department of natural
resources and water
appropriations permit
program.

Fertilizer, fuels, agrochemicals,
transport, milling (crushing),
electricity, process chemicals

Primary /
observation
and field
study.

USA (Minnesota)

Measuring
ecological
impact of
water
consumptio
n by
bioethanol
using life
cycle impact
assessment

Unpublished

LCA of soy biodiesel and
corn ethanol. System
expansion method used to
allocate co-product impacts.
Both wet and dry milling of
corn is modeled. Local biorefinery data is utilized for
each area of study. Data is
broken down by county
across multiple states.

2004-2011 (data sources)

Life Cycle
Assessment
to Improve
the
Sustainabilit
y and
Competitive
Position of
Biobased
Chemicals:
A Local
Approach

2009 (reported)

Table 11 (cont’d)

Minnesota
department
of natural
resources,
USDA, Patzek
(2006),
Mishra and
Yeh (2011),
Nebraska
Energy Office
Renewable
Fuels
Association

Journal of
Industrial
Ecology,
February
2009

Researchers created their
own Biofuel Energy Systems
Simulator (BESS) software to
compare different types of
corn-ethanol systems in a
"seed-to-fuel life cycle.
Energy and GHG reported.

Four component submodels for
crop production, ethanol
biorefinery, cattle feedlot, and
anaerobic digestion. Energy for
grain drying and irrigation (but
not water). energy used for
feedstock production and
harvesting, including fossil fuels
for field operations and
electricity for grain drying and
irrigation.

55

USA

Improveme
nts in Life
Cycle
Energy
Efficiency
and
Greenhouse
Gas
Emissions of
CornEthanol

2009 (Published)

Table 11 (cont’d)

Crop yields:
USDA-NASS,
production
energy:
USDA-ERS
(2001).
Progressive,
high yield
cropping
scenario:
Verma et al
(2005).

A decision
support tool for
modifications in
crop cultivation
method based on
LCA: a case study
on GHG
emissions
reduction in
Taiwanese
sugarcane
cultivation

Data
Country

Life cycle
assessment of
Australian sugar
cane production
with a focus on
sugarcane
growing

Data
Years

diesel, machines,
fertilizers, and pesticides

Time of
publicati
ons
(various)

Scenarios modeled for
average (all of
Queensland) and
regional (Wet Tropics
and Burkedin)
growing. Nitrogen loss
from runoff could not
be modeled.

Diesel, water for irrigation,
seed cane, fertilizer, lime,
pesticide, transport of
farm inputs, production of
"capital goods" i.e. farm
equipment.

2002
(agricult
ural
data)2010
(study
publishe
d)

GHG data for the
cultivation of
Taiwanese sugarcane.
System modeled as 1
year new planting, 1
year ratoon, and 1
year fallow.

Soil preparation, growing,
harvesting, transport,
diesel, direct field
emissions, fertilizer,
pesticide, herbicide, power
generation. Carbon
sequestration by
sugarcane is taken into
account.

Details

Ecoinvent

Cultivation of sugar
cane with 20%
mechanical harvest
and 80% manual
harvest

Internation
al Journal
of Life
Cycle
Assessmen
t Nov 2010

Internation
al Journal
of LCA
November
2009

56

1979
(oldest
data) 2009
(publish
ed)

Data Source

Australia

Sugarcane, at
farm/BR S Sugarcane, at
farm/BR U

Includes

Data
Availability

Agriculture inputs:
Millford and Pfeffer
2002, industry
statistics

Taiwan

Name

Brazil

Table 12 - Sugarcane Cultivation Completeness Check

Agriculture and
Food Agency
(2006), Taiwan
Sugar Corporation
(1979), Water
Resources Agency
(2003-2005),
Interview (2007),
Chinese Fertilizer
Association (2005)

Carbon footprint
of sugar
produced from
sugarcane in
eastern Thailand

Biomass
and
Bioenergy
Vol 32
(2008)

Journal of
cleaner
production
vol 19

Fertilizer, lime, pesticide,
water for irrigation,
harvesting, milling,
bagasse combustion (for
sugarcane only),
electricity, transport,
machinery, fuel use,
clarification.

Reports estimated
GHG emissions from
CO2, CH4, and N2O.

Tilling, irrigation, herbicide
and pesticide application,
diesel, fertilizer, biomass
burning, transport, energy
use and waste water
treatment.

57

2008

2011

USA, UK, and Australia

an environmental
life cycle
assessment
comparing
Australian
sugarcane with
US corn and UK
sugar beet as
producers of
sugar for
Fermentation

Functional unit is 1 kg
of glucose or fructose
for fermentation. The
three things that were
found to have the
greatest effect on
environmental
performance were:
commodities
displaced by coproducts, agricultural
yields, and nitrogen
use efficiency. Each
crop was assumed to
have the same N, P
and K fertilizer profile.
This study assumes
that all crops were
produced in
monoculture.

Field Survey: Fuel
use in farm
equipment,
fertilizer and
pesticide use,
water use,
emissions. Industry
statistics: Cane
yields

Thailand

Table 12 (cont’d)

Fossil fuel use,
sugarcane biomass
data, and fertilizer
data are from: Field
survey,
questionnaire and
interview.

Bioproduction
from Australian
sugarcane: an
environmental
investigation of
product
diversification in
an agro-industry

Internation
al Journal
of LCA May
2009

Functional Unit is
10,000 Km covered by
car of a specific size
engine, but data is
broken into phases.

Soil preparation, cane
plantation, chemical
application, harvesting,
fuel ethanol process, and
energy co-generation

20032009

Sao Paulo, Brazil

Life Cycle
Assessment of
fuel ethanol from
sugar cane in
Brazil

Journal of
cleaner
production
Vol 39

System based,
consequential
approach. A range of
scenarios involving
different uses for coproducts was
explored.

Land use change, milling
and production,
fermentation, additional
cane growing (for
scenarios 3 and 4).

Publishe
d 2012

Australia

Table 12 (cont’d)

58

Water, energy, and
emission data from
SimaPro (2003)
Other: Primary data
from sugarcane
farms and
industries.
Energy flows and
balances: Hobson
P. (unpublished
data). Sugar cane
growing: Ranouf et
al (2010). Fuel use:
APACE (1998),
Environment
Australia (2002).

A comparative
Life Cycle
Assessment of PE
Based on Sugar
Cane and Crude
Oil

Journal of
Industrial
Ecology,
June 2012

Details LCA studies of
sugarcane-based LDPE
produced in Brazil and
used/disposed of in
Europe, Consequential
and attributional LCAs
conducted in parallel.
Impact categories:
GWP, acidification,
eutrophication,
photochemical ozone
creation, primary
energy consumption.

Fertilizer production, cane
cultivation including LUC,
ethanol production,
ethylene production, LDPE
production. Transport, use
phase, and disposal also
included.
Only CO2 was considered
in GWP and only from
fossil fuel origin. Biogenic
CO2 release was not
accounted for, and neither
was sequestration during
cane growing. Functional
unit is 1 kg PE.

59

20042006
(Literatu
re
publishe
d)

Brazil, Europe

Table 12 (cont’d)

Brazilian data for
every step through
ethanol production.
Sugar cane
cultivation data
from: Macedo et al
(2004), Cheesman
(2005), and Smeets
et al (2006).
Literature sources
used to estimate
LUC.

Life Cycle
Assessment of
Sugarcane
Ethanol and Palm
Oil Biodiesel Joint
Production

Journal of
Biomass
and
Bioenergy,
September
2012

Three LCA studies
were carried out in
parallel: one for the
traditional sugarcane
ethanol system,
another for a palm oil
biodiesel system, and
one for the joint
production system of
sugarcane ethanol and
palm biodiesel.

Seeds, production and use
of agricultural inputs,
cropping practices,
harvesting and
transportation,
manufacture and
maintenance of
machineries and
agricultural implements,
co-products disposal and
transportation,
manufacture and use of
chemicals inputs,
manufacture and
maintenance of
equipment and industrial
construction, and coproducts energy
generation. Land
occupation and
transformation.

60

Sao Paulo, Brazil

Table 12 (cont’d)

Primary / field
survey and
questionnaires

Soy bean, from
farm

Soy beans IP, at
farm/CH S - Soy
beans IP at
farm/CH U

Includes

"Carbon Sequestration should
be accounted for after the
product is built in its LCA
model, and should be included
depending on the use of end
of life fate of that product"
(USLCI).

Data
Years

Data
Country

Details

Data source
USDA, EPA,
Sheehan J
(1998), USGS
(1999),
National
Agriculture
Statistics,
Pesticide
Action
Network

Diesel used on field,
electricity and natural gas for
farm activities, fertilizer, lime,
land occupation, tilling,
transport, water for
irrigation, pesticide, herbicide

19902011

LCA Food DK

Land occupation arable,
fertilizer, fuel for farm
machines, lubricant oil for
machines

2002
(Data
file
created)

Argentina

Soybean grains,
at field

Data
Availability

Ecoinvent

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest
and grain drying. Machine
infrastructure and housing.
Inputs for fertilizers,
pesticides and seed as well as
their transports to the
regional processing center
(10km) are considered, direct
emissions on the field also
included.

19962003

Swiss lowlands

Name

USA

Table 13 - Soy Cultivation Completeness Check

USLCI

1 kg of soy beans IP, at farm
with a moisture content of
11%

61

Soybeans, at
farm/BR S Soybeans, at
farm/BR U

USLCI

1 planted acre for 1 year

Seed production, carbon
sequestration, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting. Only consumptive
use of water taken into
account. Quicklime and
transportation of inputs to
the farm is included.

Ecoinvent

1 kg soybeans modeled with
data from literature, some
data extrapolated from Europe
(production of fertilizers and
pesticides), and Switzerland
(machine use). Transports
modeled for standard
distances.

Use of diesel, machines,
fertilizers, and pesticides.
Carbon sequestration, land
transformation and
occupation, tilling, sowing,
harvesting, seed input, and
transport of inputs to the
farm.

62

19962004

19982000

USA

Soybean grains,
at field/kg/US

Ecoinvent
System
Processes /
Ecoinvent
Unit
Processes

Time of
literatur
e
publicati
ons
(various)

Brazil

Soy beans
organic, at
farm/CH S - Soy
beans organic,
at farm/CH U

Soil cultivation, sowing,
fertilization, harvest and grain
drying. Machine
infrastructure and housing.
Inputs for fertilizers,
1 kg soy beans organic, at farm
pesticides and seed as well as
with moisture content of 11%
their transports to the
regional processing center
(10km) are considered, direct
emissions on the field also
included.

Swiss lowlands

Table 13 (cont’d)

USDA, EPA,
Sheehan J
(1998), USGS
for NOAA
(1999),
National
Agriculture
Statistics,
Pesticide
Action
Network

Soybeans, at
farm/US S Soybeans, at
farm/US U

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use, seeding

19962000,
2002,
2006

USA: various states

Substitutable
biodiesel
feedstocks for
the UK: a
review of
sustainability
issues with
reference to
the UK RTFO

LCA
Commons

LCA Commons has specific
data for each state listed for
various years. Not all states
are available for all years.

Journal of
Cleaner
Production
2009

Data for Jatropha, soy, palm
oil, rape seed oil, and used
cooking oil for production of
biodiesel. Reports carbon
footprint and GHG savings
(except for Jatropha). Includes
GHG from direct and indirect
land use change.

GHG and carbon footprint
only. Land use changes
included in this calculation.

2009
(Publish
ed)

Import to UK

Soybeans; at
harvest in
(year); at farm;
85-92%
moisture
(state)

1 kg soybeans with moisture
content of 11%. Modeled with
data from literature, some
data extrapolated from Europe
(production of fertilizers and
pesticides), and Switzerland
(machine use). Transports
modeled for standard
distances.

Use of diesel, machines,
fertilizers, and pesticides.
Carbon sequestration, LUC
and use, tilling, sowing,
harvesting, seed input, and
transport of inputs to the
farm. Carbon sequestration,
land transformation (place
holder for ongoing process),
land occupation, lime,
sowing, tilling, harvesting,
and herbicide. Seed inputs
also included.

Time of
literatur
e
publicati
ons
(various)

USA, with some data from
Europe and Switzerland

Table 13 (cont’d)

Ecoinvent

63

NGO and
Policy sources

Biogenic
greenhouse gas
emissions
linked to the
life cycles of
biodiesel
derived from
European
rapeseed and
Brazilian
soybeans

Journal of
Cleaner
Production
2009

GHG emissions only. System
models soybeans grown for 25
years on land from cleared
rainforest or savannah with no
tilling.

64

20012005

Argentina

Life cycle
assessment of
soybean based
biodiesel
production in
Argentina

Primary data was obtained for
the crop portion of the data
only. Economic allocation was
used for co-products. Different
Only CO2, CH4, and N2O
types of soy (first and second were considered in the global
class, conservation till and no
warming calculation. Land
International
till) were used, and weighted
use change except for direct
Journal of
by their production volumes.
de-forestation is excluded.
LCA March
Second class soy uses the
Storage and drying is
2009
residual fertilizers in the soil
excluded. Agriculture,
from the wheat that is
extraction and refining, transcultivated in rotation with it,
esterification included.
while first class soy is
produced in monoculture and
has fertilizers applied.

2009

Brazil, Europe

Table 13 (cont’d)

Field survey
and primary
data from
Donato et al
2005 used for
agriculture
data.
Ecoinvent
data used for
all else.

Appendix C: Data Quality Ratings

Data Country

Technological
Rep

USA (Midwest)

Cultivation
practices varied
by county. State
level input
statistics used
when county
level
unavailable.
Current tillage
practices
represented.
1.5

Switzerland

Data Years
2008-2009
1996-2003

Data Availability
Unpublished
Ecoinvent

Annual Report: Life Cycle
Grain maize organic
Assessment to Improve the
at farm/CH S –
Sustainability and Competitive
Grain maize organic
Position of Biobased Chemicals: A
at farm/CH U
Local Approach

Name

Table 14 - Corn DQR

Represents
Organic
cultivation in
Switzerland
1

Geographical
Rep

Time Rep

Completeness

Data available
on a county
basis, accounts
for variation
between
locations

Corn data
averaged
from 20002006
(continuous)

System
expansion model
used, direct land
use change
included but not
indirect.

1

1

1.5

Data is from
Switzerland.

Data
collected
from 19962003

High level of
completeness.

1

2

1

65

Uncertainty

Data
Quality
Rating

High

1.4
Low
uncertainty
Transport
distances
are
estimates.
1.5

High

1.6

Switzerland
USA
Switzerland

1996-2003
Time of publications
1996-2003

Ecoinvent
Ecoinvent
Ecoinvent

Grain maize IP, at farm
/CH S - Grain maize IP, at
farm/CH U

Corn, at farm/US S –
Corn, at farm/US U

Silage maize organic,
at farm/CH S - Silage
maize organic, at
farm/CH U

Table 14 (cont’d)

Represents
Organic
cultivation in
Switzerland

Data is from
Switzerland.

Data
collected
from 19962003

High level of
completeness.

1
Data is for the
typical
cultivation
system used in
the USA. Tillage
practice not
stated, but
appears to
represent
conventional
tillage practices.
1

1

2

1

Data for the
USA. Regional
differences are
not accounted
for.

Data from
2004-2006.
Ecoinvent's
reference
year is 2000.

High level of
completeness.

1.5

2

1

Represents
cultivation
system used in
Switzerland
during the
reference
period.

Data is from
Switzerland
with general
European data

Data
collected
from 19962003

High level of
completeness

66

Low
uncertainty
Transport
distances
are
estimates.
1.5
Most data
has low
uncertainty.
Carbon
dioxide and
energy from
biomass are
estimates as
well as
transport
distances.
1.5
Low
uncertainty.
Transport
distances
estimates
some
agronomic
inputs have
moderate
uncertainty.

High

1.6

Basic

1.7

Basic

Table 14 (cont’d)

Switzerland
USA various states

1996-2003
1995-2001, 2005

Ecoinvent

1.5

2

1

Represents
cultivation
system used in
Switzerland
during the
reference
period.

Data is from
Switzerland
with general
European data

Data
collected
from 19962003

High level of
completeness.

1

LCA Commons

Corn grain, at harvest in
(year), at farm 85-91% moisture
(state)

Silage maize IP, at farm
/CH S - Silage maize IP, at
farm/CH U

1

1.5

2

1

1.5

1.7

Relatively
complete. Land
transformation is
accounted for
(including the
change caused
by rotational
cropping).
However, cutoff
values are
applied to many
inputs.
2

LCA
Commons
uses a
scoring
system of A
(good) and
B (less
good).
Uncertainty
varies by
state and
year.

High

Data for the
system under
study (USA)
broken down by
state.

Data available
at state level.

Available as
averages of
multiple
years or as
data from
individual
years.

1

1

1

67

1.5
Low
uncertainty
for most
flows.
Transport
distances
are
estimates.

1.7

Basic

1.6

USA (by county)
USA (Minnesota)

2009

Cultivation
practices varied
by county are
weighted for
state level
inputs.

Data available
on a county
basis, accounts
for variation
between
locations

1

2004-2011 (data sources)

International Journal of LCA
International Journal of LCA

Measuring ecological
impact of water consumption by
bioethanol using life cycle impact
assessment

Regional variations in GHG
emissions of bio-based products
in the United States - Corn
based ethanol and soybean oil

Table 14 (cont’d)

1

Represents
current corn
irrigation
practices in
region under
study.

Irrigation
requirements
for individual
farms taken
from water
permit office,
very specific
geographic
information
used.

1

1

68

Data from
2000-2008

2
Data from
2004-2011
with
disparate
sources. The
data from
2004 is
USDA corn
yield and
production
data at
county level.
2.5

GHG emission
study only.
Direct land use
change is
included. System
expansion is
used to deal with
allocation issues

High

1

1.6

Water only,
meets study
scope

Basic

1

1.9

USA (Scott County Iowa = corn data)
USA

2005 (study published)
2009 (study published)

Biomass and Bioenergy
Industrial Ecology

Improvements in Life Cycle Energy
Efficiency and Greenhouse Gas
Emissions of Corn-Ethanol

Life cycle assessment of
various cropping systems
utilized for producing biofuels:
Bioethanol and Biodiesel

Table 14 (cont’d)
Soil and
temperature
data is based
Three scenarios
on one country
are modeled, all
(Scott country,
with no-till
Iowa). This is
agriculture. The
within the US,
EPA estimates
but may not be
2/3 of corn in
representative
the US uses
of averages.
conservation
This is balanced
tilling, but not
by use of
necessarily no
national
till
statistics for
agronomic
inputs.
1.5
2
Standard and
Weighted
"progressive"
averages used
scenarios
for input rates.
modeled. EPA
Midwest
estimates
accounts for
change in the
88% of corn
technology
production in
used from
2005. The
2005-2009
"progressive"
(conservation
model is based
tillage increase on a single field
from 40% to
study in
70%)
Nebraska
69

Most data is
from 20002005 with
some
fertilizer
data from
1994 (urea
production)
and 1998.

System
expansion model
used, but land
use change not
included.

Basic

2

2

1.9

Data from
2001-2005,
study
published in
2009

Does not include
direct or indirect
land use change

Basic

Table 14 (cont’d)

Italy

1985-2002

Biomass and Bioenergy

2
Primary data
Primary data
from field
from Tuscany,
study
central Italy.
conducted
The Primary
1985-2002.
data is from a
Other data
smaller region
from 1997
than the
and 2004.
abstract sites
Spread of 19
as the area of
year the
study
primary data
(Mediterranean
has
region).
continuity.
3
2

2

2.0

Land use change
not accounted
for

Basic

2

2.5

Data is for US
cultivation.
Averaged
conservation
and
conventional
tillage practices.
Irrigated system

Data based on
US averages,
regional
variations not
captured.

Significant
data sources
from 19952003.

Several dummy
processes used
to model
agrochemicals.
Land use change
not accounted
for.

Basic

1

1.5

3

2.5

2.5

Three scenarios
(low,
traditional, and
high input is
modeled). S2
represents the
standard
practice in the
area, and
conditions are
well
documented

USA

Data Sources from 1995-2003

1

USLCI

Corn, whole plant, at field/kg/US
(Corn, at field/kg/US)

LCA of cropping systems with different
external input levels for energetic
purposes

2

2

70

USA

Estimates 2022

USLCI

Corn, production
average, US, 2022

Table 14 (cont’d)

Estimates of
production for
2022

Data based on
US averages,
regional
variations not
captured.

Calculated
estimates
for 2022

High level of
completeness
including land
transformation.

1

1.5

1

1

71

From
qualified
estimates
derived
from
theoretical
information.
4

Basic

2.6

Data Country
Brazil
Brazil

Data Years
2003-2009
2004-2006 (Literature published)

Data
Availability
International Journal of LCA May
2009
Journal of Industrial Ecology, June
2012

A comparative Life Cycle
Assessment of PE Based on Sugar
Cane and Crude Oil

Life Cycle Assessment of fuel
ethanol from sugarcane in Brazil

Name

Table 15 - Sugarcane DQR
Technological
Rep

Geographical
Rep

Time Rep

Cane is grown
in 6 year cycles.
20% replaced
every year.
Study reflects
Data specific to
the high
NE Sao Paulo
intensity of
Brazil
inputs typically
used in Brazil.
Cane is mostly
burned before
harvest.
1
1
Both
mechanical and
manual harvest
are modeled.
Details of
cultivation
process
modeled are
vague.

Brazilian data
used for cane
cultivation, but
from various
literature
sources.
Detailed
account of
geographic
information
unavailable.
72

Completeness

Primary data
collected from
2001-2008. N,
P, and K
fertilizer input
data from
2000 and
2003.

Land occupation
and land use
change are not
accounted for.
Study uses a
cut-off of 0.05%
for inputs.

2

Data sources
range from
2004-2009.
Various
literature
sources used.

2
Both
attributional
and
consequential
methods are
used. System
expansion is
used to deal
with coproducts
for the
consequential
method.

Uncertainty

Data
Quality
Rating

Basic

1.8

Land use
change
emissions
are
uncertain
due to lack
of uniform
methods.

Basic

Table 15 (cont’d)

Australia
Thailand

2008
2011

Journal of cleaner production vol 19

Carbon footprint of sugar
produced from sugarcane
in eastern Thailand

an environmental life cycle assessment
comparing Australian sugarcane with US
corn and UK sugar beet as producers of
sugar for Fermentation
Biomass and Bioenergy
Vol 32 (2008)

1.5

Monoculture
with
conventional
practices
assumed. New
planting every
4-6 years.
Typical
technology for
region.

2
Data for
Queensland,
which has a
large variation
in climate
conditions.
Min and max
values are
provided for
inputs, but
variation is
represented to
a limited
extent.
1.5

1
Sugarcane
Data is from
harvested
the study area
green and
(Eastern
burned.
Thailand).
Manual harvest
Cropping
Sugarcane
practices were
cultivation in
found to vary
Thailand is an
significantly
established
within this
process. Farms
area, which is
in operation for
reflected in the
20 years or
data.
longer.
73

2.5
Data for yields
(2003) and
harvesting
energy (2002)
are the most
significant due
to the
sensitivity of
results to
these two
items. Other
data ranges
from 19962007.
2.5

Survey
cropping
averages from
2003-2007.
Soil conditions
data is from
1993.

1
Land occupation
and land use
change not
accounted for.
Some data
partly based on
estimates.
Displacement
method used to
deal with coproducts.
2
Land occupation
and transport of
inputs to farm
not accounted
for. Land use
change excluded
since this study
was conducted
on established
farms. GHG
estimates from
CO2, N2O, and
CH4.

2.1
Min and max
input values
calculated.
Variation
due to
regional
differences
accounts for
most of this.
Study does
not capture
all variation.

Basic

2.1

N fertilizer
rate varied
by a factor
of 18, and
the yield
varied by a
factor of 3.

Basic

Table 15 (cont’d)

Australia

Published 2012

Journal of cleaner
production Vol 39

Bioproduction from Australian sugarcane:
an environmental investigation of product
diversification in an agro-industry

1

1.5

2

Data used from
Data from
Both standard
Queensland
Renouf et al
cropping
(98%
2010 is rated
system and
Australian
at 3. Other
"eco-efficient"
production),
data
cropping
but does not
introduced in
system
capture
this study is
modeled. Input variation in the
consistent with
levels for both
region as
this timeframe
clearly defined.
averages are
(2004-2009).
used.

1

1.5

3

74

2.5
Based on data
from Renouf et
al 2010, but
includes land
use change and
a progressive
cropping
scenario.
However, the
methods used
to evaluate
water and land
use have
significant
limitations.
1.5

2.1

Basic

2.4

Brazil
Australia

Time of literature publications (various)
2002 (agricultural data)-2010 (study
published)

Ecoinvent
International Journal of Life Cycle
Assessment Nov 2010

Life cycle assessment of Australian
sugar cane production with a focus on
sugarcane growing

Sugarcane, at farm/BR S
Sugarcane, at farm/BR U

Table 15 (cont’d)

Harvesting is
assumed to be
20%
mechanical and
80% manual.
Not indicated if
cane is
harvested
green or
burned. Tillage
practice not
identified.
2
60% irrigated,
39% burned
before harvest.
Methods
modeled are
varied by
region, based
on field survey.
5-6 year
cropping cycles
modeled, some
with fallow
periods

Data for land
use and
change from
1996. Data for
Data modeled
some
from literature.
agronomic
Most data
inputs from
specific to
1988, and
Brazil, but US harvesting and
electricity
fram activities
used.
data is from
1996. All other
data within 3
years of
reference year.
2
3
Average
scenario
modeled for
Queensland
(98%
Eight year gap
Australian
between
production),
agricultural
scenarios also data and study
modeled for
publication.
areas within
Queensland to
capture
variability.
75

Most flows are
complete. Land
occupation and
transformation
as well as
energy from
biomass and
CO2 have
uncertain
completeness.

Most inputs
have very
low
uncertainty,
but CO2 and
energy from
biomass are
qualified
estimates
(score of 4
on pedigree
matrix)

Basic

1.5

2

2.5

N loss from
runoff not
accounted for.
Scope of the
study does not
include land use
change.

Monte Carlo
analysis
revealed
"significant
variation"

Basic

Table 15 (cont’d)

Taiwan

1979 (oldest data)
- 2009 (published)

International Journal of LCA November 2009

A decision support tool for modifications in crop
cultivation method based on LCA: a case study on
GHG emissions reduction in Taiwanese sugarcane
cultivation

1

1

3

2

3 year cropping
cycle assumed
(plantingratoon-fallow).
Varying levels
of inputs
modeled to
compare effect
on yield and
GHG emissions.

Most data
specific to
Taiwan. Some
Chinese data
used for
fertilizer
production
impacts.

A significant
amount of
data is used
from 1979
from the
Taiwanese
Sugar
Corporation.
Including
agricultural
inputs and
machinery.

Land use change
is not included
despite the fact
that sugarcane
production is
expected to
increase in
Taiwan and
fallow land will
likely be
converted.

1

1

5

2

76

2.4

Sensitive to
sugarcane
yield

Data
Estimate

3.6

Brazil

Journal of Biomass and Bioenergy, September 2012

Life Cycle Assessment of
Sugarcane Ethanol and Palm Oil Biodiesel Joint
Production

Table 15 (cont’d)
Sugarcane data
from Sao
Paulo, Brazil.
Data sources
Palm data from
from 1995a different
2008, study
region. This
published
may affect the
Based on
2012. Main
accuracy of the
regional data
data sources
predictions in
that reflects
are primary
the "joint
actual practices
data / surveys.
production"
in that region.
No information
model. Only 3
about the
refineries
dates of the
surveyed.
surveys is
Effects of using
available.
different types
of land are
discussed.
1
2
5

77

All major
components are
accounted for.
However,
detailed info
about the
agricultural
inputs used is
not available
and the
transport of
inputs to the
farm is not
accounted for.

Data
Estimate

1.5

3.7

Data Country

Data from
Switzerland.

Data
from19962003

High level of
completeness.

1

2

1

Switzerland

Data Years
1996-2003
1996-2003

Ecoinvent
Ecoinvent

Name
Soy beans IP, at farm/CH S - Soy
beans IP at farm/CH U

Represents
organic
cultivation in
Switzerland
during the
data years
1

Soy beans organic,
at farm/CH S - Soy
beans organic, at
farm/CH U

Technological
Rep

Switzerland

Data Availability

Table 16 - Soy DQR

Geographical
Rep

Time Rep

Represents
cultivation
method
typical in
region during
data period.

Data from
Switzerland and
Europe

Data from
1996-2003

1

1.5

2

78

Completeness

Uncertainty

Low level of
uncertainty
for most
flows.
Transport
distances are
estimates.
1.5

Data
Quality
Index

High

1.6

High level of
completeness.

Low level of
uncertainty
for most
flows.
Transport
distances are
estimates.

Basic

1

1.5

1.7

USA

LCA Commons

Soybeans; at
harvest in (year);
at farm; 85-92% moisture (state)

Table 16 (cont’d)

Represents
system under
study.
Variation is
accounted
for.

Argentina

Published 2009

International Journal of LCA March 2009

Life cycle assessment of soybean based
biodiesel production in Argentina

1

Represents
cultivation
practices
typical of the
region
weighted by
production
volume.

Data
available as
Data for the USA
average
broken down by
over
state. Variation
several
captured.
years or for
individual
years.

1

1

Relatively
complete. Land
transformation
is accounted
for (including
the change
caused by
rotational
cropping).
However,
cutoff values
are applied to
many inputs.
2

Differences
Majority of
Most major
between regions
data is
contributors
are weighted
from 2005
accounted for,
based on
through
but only direct
productions
2007. Some
deforestation is
volume, but
background
measured for
further
data from
land use
refinement is
early 2000s
change.
called for.
used.

79

LCA
Commons
uses a
scoring
system of A
(good) and B
(less good).
Uncertainty
varies by
state and
year.

High

1.6

Basic

Table 16 (cont’d)

USA

Literature sources 20042006

Ecoinvent

Soybeans, at farm/US S Soybeans, at farm/US U

1

Represents
system under
study.

Soy data is for South America

Published 2009

Journal of Cleaner Production 2009

Substitutable biodiesel feedstocks for the
UK: a review of sustainability issues with
reference to the UK RTFO

1
Details of the
agricultural
system
modeled are
vague, but
monoculture
with high
amount of
agro
chemicals is
assumed.
Data from
multiple
sources used.
2.5

1.5

2

Some data from
the US, but
many inputs
adapted from
Swiss and
European data.
3
Data from
various regions
in South
America used
(Brazil,
Argentina, and
Bolivia) but
variation
between regions
not
distinguished.
Unclear what
types of data
were used from
each region.
2.5

80

2

1.8
Co2 and
energy from
biomass are
calculated
estimates.
Transport
distances
also
estimates.
1.5

Literature
sources
published
2004-2006

High level of
completeness

2

1

Soy data
from 20032009.
Multiple
sources
used.

GHG only,
matches study
scope. Direct
Land use
change is
accounted for
based on
different
conversion
scenarios.
Indirect land
use change not
accounted for.

Basic

2

1.5

2.3

Basic

2.3

Soybean grains, at field
(1998-2000)/kg/US

Table 16 (cont’d)

USA
Brazil

1998-2000
Modeled from literature

USLCI
Ecoinvent

Based on US
averages,
regional
differences not
accounted for.

Significant
data
sources
from 19952003.

1
Soybeans, at farm/BR S - Soybeans, at
farm/BR U

Based on US
averages.
Weighted
conventional
and
conservation
tillage.

1.5

1.5

Represents
cultivation
Some data
system under
specific to Brazil,
study, but
but agronomic
unclear if
inputs adapted
conventional
from Swiss and
or
European data.
conservation
tillage is used.

1.5

3

Literature
sources
published
2001-2006

2

81

Several dummy
flows used to
model
agrochemicals.
Farm processes
(harvesting,
tilling etc) not
called out in
flows.
3

Basic

2.4

CO2 and
energy from
High level of
biomass are
completeness
calculated
including land
estimates.
transformation
Transport
from forest and
distances
shrub land.
also
However,
estimates.
application of
Significant
Lime is not
uncertainty
accounted for.
in agronomic
inputs.
1.5

3

Basic

2.5

Table 16 (cont’d)

USA
Brazil

1997-2010
Submitted 2007

USLCI
Journal of Cleaner Production 2009

Soybean grains, at
field/kg/US

Based on US
averages,
regional
differences not
accounted for.

1997-2010
from
various
sources.

1
Biogenic greenhouse gas emissions linked to the life
cycles of biodiesel derived from European rapeseed
and Brazilian soybeans

Based on US
averages.
Weighted
conventional
and
conservation
tillage.

1.5

3

Different
scenarios (no
till and
conventional
till) modeled.
Data mostly
based on
averages.
Economic
allocation
used but
based on
prices from
Europe.

Some data from
Europe and the
US adapted for
Brazil. Regional
variations not
accounted for.

2.5

2.5

Dummy
processes are
used for
several
chemicals and
land use
change is not
accounted for.
2.5

Basic

2.5

Basic

3

82

Data from
1998 2008 from
disparate
sources.

Carbon and
GHG only,
matches scope
of study.
However,
many
estimates used.
Study assumes
land will be
abandoned
after 10 or 25
years.

2

2.8

Argentina

Unknown

LCA Food DK

Soy bean, from
farm

Table 16 (cont’d)
Cultivation of
soy but
unspecified
technology

Arable land data
is from
Argentina, other
data generic.

Unknown

3

3

5

83

Very few flows
present. Types
of fertilizer
unspecified.
Emissions
incomplete.
5

Low
Quality
Estimate
4.5

Appendix D: Processing Data Completeness Check
Data Availability

Details

Includes

Data Years

Data Country

Data Source

USLCI

This process transports corn grain
to the conversion plant. It does so
using transportation modal
allocation from the USDA Ethanol
Backgrounder (2007), assuming
this current allocation will apply in
2022. Distances for each mode are
from a combination of references;
still missing a good distance
estimate for barge, but since the
share of barge transportation is
~2%, the final result will not be
sensitive to this parameter.

Infrastructure impacts are
included in this process by
calling the Ecoinvent "transport"
processes. The production of
corn grain feedstock utilized in
this transport process allocates
inputs to the stover and grain
based on the amount of ethanol
that can be produced from each
co-product.

2022

USA

Unknown

corn grain, at conversion plant, 2022

Name

Table 17 - Corn Processing Completeness Check

84

This process is the production
steps required to obtain maize
starch from corn. The corn input
refers to Grain Maize IP. Allocation
was done on an economic basis.
Moisture content is 14% by weight.
The only emission included in this
process is heat. No other emissions
to the environment are accounted
for.

Mechanical separating steps,
swelling in process water,
milling of the swelled corns,
desiccation and drying of the
extracted starch. Processing of
water was included as well as
infrastructure use.

1998

Germany

Detailed literature study

processing data for corn grain, at
conversion plant 2022

Corn dried and stored, milling,
gluten drying, waste disposal,
fermentation, electricity, and
process water. Infrastructure
not included.

2000-2010

North America

McAloon 2000, Hsu
2010, Mueller 2007

Ecoinvent System Processes / Ecoinvent
Unit Processes
USLCI

Ethanol, denatured,
corn dry mil

Maize starch, at plant/DE S - Maize
starch, at plant/DE U

Table 17 (cont’d)

85

USA

transport of corn grains to the
distillery, processing to hydrated
ethanol. System boundary is at
the distillery and dehydration is
not included.

USA

86

Project Alcosuisse, industrial
data, literature. Tramsport Ecoinvent, modified by GaBi
distances estimates.

Unknown

GaBi PE
Ecoinvent Unit Process

1 kg hydrated ethanol 95% ( dry
basis, i.e. 1.05 kg hydrated ethanol
wet basis). Dry milling technology.
Economic allocation 97.6% to
ethanol. CO2 emissions are
allocated based on carbon balance.

Infrastructure, transport to
facility, waste treatment,
process chemicals, process
energy, water.

1990-2006

Corn, in distillery

Production of 1 kg hydrated
ethanol 95% dry basis (1.05kg wet
basis). Also delivers co-product
"DDGS, from corn, at distillery".
Economic allocation w/ factor of
97.6% to ethanol. Allocation
according to carbon balance for
CO2 emissions. Does not specify
which Ecoinvent file data is taken
from.

Ethanol, 95% in H2O, from
corn, at distillery/US S Ethanol, 95% in H2O, from
corn, at distillery/US U

Table 17 (cont’d)

87

Literature and industry sources from 19912009. Background data from Ecoinvent and
Ecobilan.

Dry milling process: grinding,
cooking, fermentation,
distillation and DDGS recovery.
Chemicals used are caustic lime,
soda, sulfuric acid, and urea.

USA

Corn ethanol represents dry milling
process, and Soybean oil was made
using the crushing process. GHG
only reported category. Data is at
the county level, 40 counties in the
US corn belt were chosen. System
expansion is used to deal with
allocation. DDGS displaces corn,
soy meal, and nitrogen in urea.

1991-2009

International Journal of LCA Sept 2009

Regional variations in GHG emissions of biobased products in the United States - Corn
based ethanol and soybean oil

Table 17 (cont’d)

International Journal of LCA Jan 2012

Measuring ecological impact of water consumption by
bioethanol using life cycle impact assessment

Ecological impacts of corn based
ethanol - water consumption in 81
spatially explicit Minnesota
watersheds. Water data taken
from Minnesota department of
natural resources and water
appropriations permit program.
Water consumption during corn
cultivation and ethanol
production. Represents dry
milling process.

88

Minnesota department of natural resources water permit
appropriation program, USDA (2004, 2010), Patzek (2006),
Mishra and Yeh (2011), Nebraska Energy Office (2011),
Renewable Fuels Association (2008).

USA (Minnesota)

2006-2011 (data sources)

Table 17 (cont’d)

Includes

Data Years

Data Country

The multi-output process
"sugarcane, in sugar
refinery" delivers the coproducts: Sugar, ethanol
95% in H2O, sugarcane
molasses, bagasse,
electricity, and vinasse.
Economic allocation with
80-85% to sugar and 1011% to ethanol. Allocation
according to carbon
balance for CO2. Sugar
Cane used is from "sugar
cane , at farm" BR

Process energy (from burning
of residues, coal, and wood),
process water (tap), waste
disposal (modeled with CH
municipal disposal, ash
disposal both landfill and
incineration), ethanol
fermentation plant, limestone,
transport (unspecified),
wastewater treatment,
process chemicals, and sugar
refining. Land use /
transformation not included.

2011 (entered into database)

Brazil

89

Data Source

Data
Availability

Details

Gabi-PE

sugarcane, in sugar refinery

Name

Table 18 - Sugarcane Processing Completeness Check

Time of publications
Brazil

Functional Unit is 10,000
Km covered by car of a
specific size engine, but
data is broken into phases.
Waste water from the
ethanol process is used to
irrigate the fields,
transported there by trucks.
Cane Washing, juice
extraction, refining,
fermentation, and distillation
of ethanol. Electricity cogeneration by steam.

90

2003-2008
Sao Paulo, Brazil

Water, energy, and
emission data from
Publications. Also "some data are
SimaPro (2003) Other:
derived from other or unknown
Primary data from
plant or have been estimated".
sugarcane farms and
industries (2001-2008)

Ecoinvent

Allocation based on
economic criteria.
Fermentation of sugar cane
including materials, energy
uses, infrastructure, and
emissions. Lubricating oil for
machines included.

International Journal
of LCA May 2009

Ethanol, 95% in H20, from sugar
Life Cycle Assessment
cane, at fermentation plant/BR S of fuel ethanol from
Ethanol, 95% in H20, from sugar
sugar cane in Brazil
cane, at fermentation plant/BR U

Table 18 (cont’d)

91

Energy flows and balances: Hobson P. (unpublished data).
Sugar cane growing: Ranouf et al (2010). Fuel use: APACE
(1998), Environment Australia (2002). Background
Data: Life Cycle Strategies (2009). Fermentation:
Aden et al (2002). PLA production: Vink et al (2003).

Australia

System based
consequential approach. A
base case and 5 scenarios
are represented. In the
base case, the mill produces
sugar and molasses (used as
animal feed). Scenario 1:
Cane crushing, juice extraction
excess electricity from
and purification,
bagasse is exported to the
concentration and
grid. Scenario 2: ethanol is
crystallization (if applicable).
made from molasses.
Fermentation including
Scenario 3: ethanol is made
chemical inputs, and PLA
from bagasse. Scenario 4:
production included.
ethanol is made from cane
juice with increased
sugarcane production.
Scenario 5: PLA is made
from cane juice with
increased sugarcane
production.

Published 2012

Journal of cleaner production Vol 39

Bioproduction from Australian sugarcane:
an environmental investigation of product
diversification in an agro-industry

Table 18 (cont’d)

92

Brazilian data for every step through ethanol production. Ethanol yield data from:
Macedo et al (2004). PE production based on process simulation and literature
(Kochet et al. 1981 and Barrocas and Lacerda 2007). Data from NTM 2009, Bargigli
et al. 2004, cottro et al 2003, and Tillman et al. 1992 also used.

Cultivation of cane, cane
washing, crushing, juice
extraction, PH adjustment,
fermentation and distillation
included in ethanol
production. Ethylene
production: heating,
dehydration by aluminum
catalyst, and purification to PE
grade ethylene. LDPE
production: polymerization at
130-330 C and 81-276 mpa.

Brazil, Europe

Details LCA studies of
sugarcane-based LDPE
produced in Brazil and
used/disposed of in Europe,
and fossil-based LDPE
produced, used, and
disposed of in Europe.
Consequential and
attributional LCAs
conducted in parallel. No
major differences found
between the two methods
for "key" impacts. Impact
categories: GWP,
acidification,
eutrophication,
photochemical ozone
creation, primary energy
consumption. Only CO2 was
considered in GWP and only
from fossil fuel origin.
Biogenic CO2 release was
not accounted for, and
neither was sequestration
during cane growing.
Functional unit is 1 kg PE.

1981- 2009 (Literature published)

Journal of Industrial Ecology, June 2012

A comparative Life Cycle Assessment
of PE Based on Sugar Cane and Crude Oil

Table 18 (cont’d)

93

Primary / field survey and questionaires.
Data for ethanol production collected
from three biorefineries.

Manufacture and use of
chemicals inputs, manufacture
and maintenance of
equipments and industrial
construction, and co-products
energy generation.

Sao Paulo, Brazil

Three LCA studies were
carried out in parallel: one
for the traditional
sugarcane ethanol system,
another for a palm oil
biodiesel system, and one
for the joint production
system of sugarcane
ethanol and palm biodiesel.
GHG and Energy only.

2012 (published)

Journal of Biomass and Bioenergy,
September 2012

Life Cycle Assessment of Sugarcane
Ethanol and Palm Oil Biodiesel Joint
Production

Table 18 (cont’d)

94

"Industry consultation" in association with Sugar Research and
Innovation at Queensland University of Technology (unpublished
data). Australian LCI database (2007), and Ecoinvent (2009).

process energy from bagasse,
dunder (used on fields),
process chemicals, flocculent,
lubricant for machinery,
displaced products for system
expansion; sorghum,
electricity, LP steam,
potassium chloride. Cane
crushing, juice purification,
juice concentration,
crystallization, and
fermentation. Fossil fuels for
start-up of boilers are not
included.

Australia

Three scenarios are
modeled. 1) sugar mill
produces only sugar an
molasses. Power is
generated from bagasse
combustion with no cogeneration. 2) sugar mill
produces sugar and
molasses with cogeneration from bagasse.
Excess power is exported to
the grid. 3) Sugar mill uses
co-generation and produces
ethanol from molasses.
Economic, mass, and
system expansion methods
of allocation are all
modeled. Water
evaporated from cane juice
is used for processing.

Study published 2011. Data sources 2007, 2009.

International Journal of Life Cycles Assessment 2011

Life cycle assessment of Australian sugar
cane production with a focus on sugarcane processing

Table 18 (cont’d)

95

Data source
Data from a US study, cross
Reusser 1994, Cederberg 1998,
checked with literature
von Daniken et al. 1995. Some
sources, industrial data.
values provided by European
Energy data from national
manufacturing companies.
statistics.

Data Country

Soy data refers to
"Soybeans, at farm/BR U"

Europe

Transport to process facility,
process energy (electricity and
natural gas), organic chemicals
for processing. Conditioning of
the beans, but not drying, is
included. Infrastructure and
land use.

Transport to mill, processing
into oil and meal. Process
chemicals are accounted for as
well as process energy.

Brazil (adapted from US
data)

Ecoinvent

Soy data file refers to "Soy
bean IP, at farm/CH". This
process refers to their
further processing. Process
energy is provided by
natural gas. Processing is
done in Europe with beans
that are assumed to be
imported.

Data Years

Includes

1998-2005

Data
Availability

Details

Ecoinvent

Soybean oil, at oil
mill/BRS – Soybean
oil, at oil mill/BR U

Soya oil, at plant/kg/RER S
Soya oil, at plant/kg/RER U

Name

Table 19 - Soy Processing Completeness Check

USLCI

Processing of Soy into Soy
oil. "Refers to Soybean
grains, at field"

Energy, process chemicals,
water, transportation to
processing facility, and dummy
process for waste disposal.

2010

USA

Omni Tech
2010, NOPA

Soy data file refers to
"soybeans, at farm/US"
Allocation based on carbon
balance. Solvent extraction
modeled, typical for USA.

Includes carbon sequestration,
soybean inputs, transport to
the oil mill, infrastructure of
the oil mill, process water
(tap), process energy, and
process chemicals.

2006 (data file
created)

USA

US study and
literature sources,
energy data from
national statistics

96

USA

Soy input refers to "Soy,
degummed, at plant". This
process adds process water,
process energy (natural gas
and electricity), and transport
to processing facility. It also
includes sodium hydroxide
(process chemical) and
disposal of liquid wastes.

Late 2000s

USLCI

Further processing of
"Dummy Soybean oil,
crude, degummed, at
plant/kg/RNA". No impacts
are associated with this
dummy file. Energy data is
from the late 2000s and all
other data is theoretical.
Carbon sequestration
should be accounted for by
the practitioner based on
the oil use.

Ecoinvent

Soybean oil, at oil Soybean oil,
mill/US S crude,
Soybean oil, at oil degummed, at
mill/US U
plant

Soy oil, refined, at plant/kg/RNA

Table 19 (cont’d)

97

Jungluuth et al 2007, IDIED 2004.

Only CO2, CH4, and N2O were
considered in the global
warming calculation. Land use
change except for direct deforestation is excluded.
Storage and drying is excluded.
Agriculture, extraction and
refining, trans-esterification
included.

Argentina

Primary data was obtained
for the crop portion of the
data only. Economic
allocation was used for coproducts. Oil extraction and
trans-esterification data
adapted from Jungluth et al
2007 and IDIED 2004.
Solvent extraction is
modeled. Allocation factors,
yields, natural gas,
electricity consumption and
mix, and transport
distances are specific to
Argentina.

North America

Materials, process energy,
water, transport,

2001-2005

USLCI

"CARBON SEQUESTRATION
should be accounted for
after the product is built in
its LCA model, and should
be included depending on
the use of end of life fate of
that product"(USLCI).

International Journal of LCA March 2009

Life cycle assessment of soybean based
biodiesel production in Argentina

Soy biodiesel, production, at plant

Table 19 (cont’d)

Appendix E: Processing Data Quality Ratings
Data Country

Data Years
1998 (volume) 2007 (data file
created)

Germany

Data
Availability
Ecoinvent

Maize starch, at plant/DE S
- Maize starch, at plant/DE U

Name

Table 20 - Corn Processing DQR

Time Rep

Completeness

Uncertainty

Data
Quality
Rating

Represents
technology
under study

Refers to
typical
conditions in
Germany

Most data of
unknown age.
Production
volume taken
from 1998.

Water, power,
transport and
infrastructure
accounted for.
The only
emission is
heat. No
process
chemicals
accounted for.

Low
uncertainty
for most
flows.
Transport
distances
are
estimates.

Estimate

1

1

4

3

2

3.0

Technological Geographical
Rep
Rep

98

Ethanol, 95% in H2O, from corn,
at distillery/US S - Ethanol, 95% in
H2O, from corn, at distillery/US U

Table 20 (cont’d)

USA
North America

Data from 1990 -2006
Data from 2000-2010

USLCI

Ecoinvent

1
Ethanol, denatured
, corn dry mil

Represents
technology
under study

Data for USA
adapted
mainly from
Europe
(similar
conditions).
US electricity
used.

2.5

3

1.5

File claim
corn dry
milling, but
input flows
refer to wet
milling
inputs.

Data for
North
America, bur
no regional
variations
accounted
for.

Data from 20002010 from
literature
sources. Data
file was
established in
2011. Energy
input data is
from 2007.

Good level of
completeness
but
infrastructure
and land use
not accounted
for.

Basic

3

1.5

2.5

2

2.6

Data from 1990Facility land
2006, data file
use and
created in 2006.
transformation
However, data
not accounted
reflects current
for. Otherwise
processing
complete.
technology.

99

Low
uncertainty
for most
flows.
Transport
distances
are
estimates.

Basic

2

2.4

North America

corn grain, at
conversion plant, 2022

Table 20 (cont’d)
Barge
transport
distance is
Transport only, uncertain,
meets study
but this is
scope
less than 2%
of the total
transport
distance.

USA

2005
1991-2009

USLCI
International Journal of LCA Sept 2009

Data for area
under study

Data is from
2005, this file is
an estimate for
2022

1
Regional variations in GHG emissions
of bio-based products in the United States –
Corn based ethanol and soybean oil

Data is for
technology
under study

1

5

1

3.5

Data is for
technology
under study

Site specific
data not
available,
industry
averages
used.

Oldest
foreground data
from 1998 and
oldest
background
data from 1991.
Chemical data
from 2000.
Industry process
statistics from a
range of 10
years.

GHG only,
meet study
scope. Inputs
have good
completeness.

Basic

1

2

3.5

1

2.7

100

Estimate

USA (Minnesota)

2006-2011

International Journal of LCA Jan 2012

Measuring ecological impact of
water consumption by bioethanol
using life cycle impact assessment

Table 20 (cont’d)

Data is for
technology
under study

Facility
specific data
from
Minnesota
DNR water
permits.

Data is from
2006-2011.
Study published
in 2012

Water only,
matches study
scope.

High

1

1

2

1

1.6

101

Data Country
Australia

Data Years
2011 (published)

Data
Availability
International Journal of Life
Cycles Assessment 2011

Life cycle assessment of Australian sugar cane
production with a focus on sugarcane processing

Name

Table 21 - Sugarcane Processing DQR
Technological
Rep

Geographical
Rep

Time Rep

Completeness

Three scenarios,
representative
of the area, are
modeled.

Data is for
Queensland,
Australia. This
accounts for
98% of
Australian
production.

Unpublished
data is of
unknown date.
Most other data
from 20042009, but oldest
background
data is from
1989.

Good level of
completeness,
but
infrastructure
and land
occupation /
transformation
not included.
System
expansion
model includes
displaced
products.

Basic

1

1

3

2

2.4

102

Uncertainty

Data
Quality
Rating

Time of publications

Ethanol, 95% in H20, from sugar
cane, at fermentation plant/BR S Ethanol, 95% in H20, from sugar
cane, at fermentation plant/BR U

Table 21 (cont’d)

Good level of
completeness,
but facility land
use /
transformation
not accounted
for.

Most flows
have
moderate
uncertainty.
Some data
is
estimated.

Estimate

3.5

3.1

Brazil

Ecoinvent
Journal of Biomass and
Bioenergy, September 2012

Data is for
Brazil, but
several inputs
are adopted
from Europe.

1
Life Cycle Assessment of Sugarcane Ethanol
and Palm Oil Biodiesel Joint Production

Data is for the
technology
under study.

Data from 1998
- 2004. Data
entered in 2010.
Some data is
based on
estimations.

2

4

1.5

Data is for Sao
Paulo, Brazil.
Local survey
taken.

Data sources
from 19952008, study
published 2012.
Main data
sources are
primary data /
surveys. No
information
about the dates
of the surveys is
available.

GHG and
Energy Balance
only, matches
study scope.
Transport
between the
farm and
processing
facility is not
accounted for.

Data is for the
technology
under study.

103

Estimate

Table 21 (cont’d)

Brazil

1981- 2009

Journal of Industrial Ecology 2012

A comparative Life Cycle Assessment of
PE Based on Sugar Cane and Crude Oil

1

1

5

1.5

High degree of
completeness,
but facility land
Data is mostly
Oldest source
use not
for technology
from 1981, but
Most data for
accounted for.
under study, but
verified with
Brazil, but some
Biogenic CO2 is
polymerization
experimentation
data taken from
also not
data adapted
for accuracy. All
Swedish
considered in
from similar
other sources
producer
either uptake
data.
from 1992
Borealis.
or emission
Experimentally
(emissions data)
because
verified.
to 2009.
disposal by
incineration is
assumed.

1.5

1.5

4

104

1.5

3.6
Polymerizati
on data
taken from
Borealis had
to be
allocated
because it
was for the
production Estimate
of both
HDPE and
LDPE. The
division was
based on
energy
consumptio
n.
3.1

Bioproduction from Australian sugarcane: an
environmental investigation of product
diversification in an agro-industry

Table 21 (cont’d)

Australia
Brazil

2002-2010
2001-2008

Journal of cleaner production Vol 39
International Journal of LCA
May 2009

Completeness
meets study
scope. This is a
consequential
LCA, so only
marginal
impacts are
accounted for
compared to
the base case.
Waste
treatment is
not sufficiently
documented.

1
Life Cycle Assessment of fuel
ethanol from sugar cane in
Brazil

Data is for
technology
under study.

Data for
Queensland.
Region
represents 98%
of sugarcane
production for
Australia, and
there is little
regional
difference in
processing.

Study first
submitted in
2010. Data
ranges from
2002-2010, but
milling, bagasse
combustion,
and ethanol
fermentation
from sucrose
are all data from
2010.

1

2.5

1.5

2.0

Data represents
technology
under study.

Data for Sao
Paulo, Brazil
(primary).
Background
data from
SimaPro
(generic)

Continuous data
from 20012008.
Background
data from
SimaPro 2003.
Study published
2009.

Good level of
completeness,
but land use
and
infrastructure
not accounted
for.

Basic

1

2

2

2

1.9

105

The
displaced
products
assumed for
the system
expansion
approach
may not be
appropriate
for all uses
of this data.

Basic

GaBi

sugarcane, in sugar refinery

Table 21 (cont’d)

Represents
technology
under study.

Some data
adapted from
europe.
Unknown data
used to modify
file.

Data from
"sugarcane, at
farm/BR" gets a
time rating of 3
(see sugarcane
DQR table).
Unknown data
used to modify
file.

Good degree of
completeness,
but land
occupation and
transformation
not accounted
for.

Estimate

1

3

4

1.5

3.2

106

Data Country
Argentina

Data Years
2004-2007

Data
Availability
International Journal of LCA March 2009

Life cycle assessment of soybean based
biodiesel production in Argentina

Name

Table 22 - Soy Processing DQR

Technological Rep

Represents the
processing system
under study.
Solvent extraction
technology is
modeled.

1

Geographical
Rep
Data from USA
and Europe
adapted to
Argentina.
Yields,
allocation
factors, natural
gas, electricity
consumption,
electricity mix
and transport
distances are
specific to
Argentina.
2.5

107

Uncertainty

Data
Quality
Rating

Time Rep

Completeness

Data used
from 2004
and 2007.
Study
published
2009.

Crop storage and
drying are
excluded due to
lack of data.
Facility land use
and
transformation
are not
accounted for.

Basic

2

2.5

2.3

Table 22 (cont’d)

Europe
Brazil

1994, 1998, 1995
1998-2005 Literature sources

Ecoinvent
Ecoinvent

Soya oil, at plant/kg/RER S –
Soya oil, at plant/kg/RER U

2
Soybean oil, at oil mill/BR S Soybean oil, at oil mill/BR U

Generally
represents
European
processing
facilities, but some
data from
European
manufacturing
companies whose
representativeness
of the whole
market is
unknown.

Data from
Europe and
Switzerland
used to model
Europe.

Main data
sources are
from 1994,
1998, and
1995. Data
entered in
2004.

Land use is
claimed to be
represented in
the
documentation,
but no flows are
present for this
input. Process
water is also not
included.

Moderate
to high
uncertainty
is present
for all
flows.

Basic

2

2

2

4

3.1

Data for soy oil
extraction process.

Data adapted
to Brazil from
US study
(similar
production
conditions).

Data
sources
from 19982005
(literature)
Data
entered in
2006.

Facility land use
not accounted
for, otherwise
complete.

Low to
moderate
uncertainty
for most
flows.
Transport
distances
are
estimates.

Basic

1

3

3

1.5

2

2.5

108

Soy oil, refined, at
plant/kg/RNA

Table 22 (cont’d)
North America
North America

Unknown
2010, unknown

USLCI
USLCI

Data for North
America

1
Soybean oil, crude, degummed, at
plant

Data for soy oil
refining

Energy
data from
"late
2000s", all
other data
theoretical.

1

3

Data for soy oil
extraction process.

Data for North
America

Data from
Omni Tech
2010, data
from NOPA
date
unknown.
NOPA data
is for water
and
processing
chemicals

1

1

3

109

only oil refining
inputs, matches
study scope.
Infrastructure
and facility land
use not
accounted for.
1.5
Soy oil
extraction only,
meet study
scope, but
Infrastructure
not accounted
for, facility land
use not
accounted for,
and waste
treatment is
dummy process.
2

Basic

2.3

Basic

2.4

1998-2005 Literature sources

Soybean oil, at oil mill/US S Soybean oil, at oil mill/US U

Table 22 (cont’d)

USA
North America

2010, 1998, 2001

Ecoinvent
USLCI

Data for the
USA.

1
Soy biodiesel, production
, at plant

Data represents
process under
study. Solvent
extraction
modeled.

Data
sources
from 19982005
(literature)
Data
endted in
2006.

1

3

Data represents
process under
study.

Data for North
America

Data from
2010,
1998, and
2001. File
created
2011.

1

1

3

110

Facility land use
not accounted
for, otherwise
complete.

1.5
Process
chemicals are
dummy
processes,
infrastructure
and facility land
use not
accounted for,
waste treatment
not included.
2.5

Low to
moderate
uncertainty
for most
flows.
Transport
distances
are
estimates.

Basic

2

2.3

Basic

2.4

Appendix F: Data Summary Table

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
1kg barley grains
grain drying. Machine
extensive, at farm
infrastructure and a shed for
respective barley straw
machine sheltering is included. 1996extensive, at farm, both w/
Inputs of fertilizers, pesticides, 2003
15% moisture content.
and seed as well as their
89.9% economic allocation
transports to the regional
to grains.
processing center (10km) are
considered. Direct emissions
on the field also included.

111

Data Country

Includes

Swiss Lowlands

10%

Details

Data Years

Allocation*

Data Availability
Ecoinvent

Barley straw extensive,
at farm/CH S –
Barley straw extensive,
at farm/CH U

Name

Table 23 – Raw Agricultural Data Summary

9%

Swiss Lowlands

10%

1kg barley grains IP, at
farm respective barley
straw IP, at farm, both w/
15% moisture content.
89.9% economic allocation
to grains.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included. 1996Inputs of fertilizers, pesticides, 2003
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field also included.

1kg barley grains organic,
at farm respective barley
straw organic, at farm,
both w/ 15% moisture
content. 91.3% economic
allocation to grains.

Soil cultivation, sowing, weed
control, fertilization, harvest
and grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
1996Inputs of fertilizers, pesticides,
2003
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field also included.

Swiss Lowlands

Ecoinvent
Ecoinvent

Barley straw organic, at farm/CH S Barley straw organic, at farm/CH U

Barley straw IP,
at farm/CH S –
Barley straw IP, a
t farm/CH U

Table 23 (cont’d)

112

Corn, Whole Plant, at Field

0%

1 planted acre for 1 year.
Harvested acres are 84%
of the planted acres. The
impacts of producing 1 kg
of seed are considered = to
producing 1 kg lint. Only
consumptive use of water
taken into account.

Seed production, tillage,
fertillizer and pesticide
application, crop residue
management, Irrigation,
Harvesting

113

USA

0%

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, Irrigation,
Harvesting

19982000

USA

USLCI
USLCI

Cotton straw, at field/kg/US

Corn stover, at
field/kg/US

Table 23 (cont’d)

100
%

Production of Jute delivers
the coproducts jute fibers,
irrigated system, at farm
and jute stalks, from fiber
production, irrigated
system at farm. Allocation
based on economical and
mass criteria.

Manual cultivation of Jute
from conventional production
standards. Cultivation,
pesticides, mineral fertilizer,
harvest, loading for transport
and extraction of fibers after
retting process.

India

100
%

Production of jute delivers
the coproducts jute fibers,
refined system, at farm
and jute stalks, from fiber
production, refined system
at farm. Allocation based
on economical and mass
criteria.

Manual cultivation of Jute
from conventional production
standards. Cultivation,
pesticides, mineral fertilizer,
harvest, loading for transport
and extraction of fibers after
retting process.

India

Ecoinvent
Ecoinvent

Jute stalks, from fibre
production, refined
system, at farm/IN S Jute stalks, from fibre
production, refined
system, at farm/IN U

Jute stalks, from fibre production, integrated
system, at farm/IN S - Jute stalks, from fibre
production, integrated system, at farm/IN U

Table 23 (cont’d)

114

Manual cultivation of Kenaf
from conventional production
standards. Cultivation,
pesticides, mineral fertilizer,
harvest, loading for transport
and extraction of fibers after
retting process.

0%

Potato, whole plant, at
field. 1 planted acre for 1
year

seed production, tillage,
fertilizer, pesticide, crop
residue management,
irrigation, harvesting

19982000

USA

1 planted acre for 1 year,
harvested acres are 97% of
planted acres. Only
comsumptive use of water
taken into account.

seed production, tillage,
fertilizer, pesticide, crop
residue management,
irrigation, harvesting. All
impacts are allocated to the
rape seed, and none to the
residues.

19982000

0%

115

India

100
%

Production of kenaf
delivers the co-products
"kenaf fibres" and "kenaf
stalks." Allocation was
based on economical and
mass criteria.

US and European averages

Ecoinvent
USLCI
USLCI

Rapeseed residues,
at field/kg/US

Potato
leaves, at
field/kg/US

Kenaf stalks, from fibre
production, at farm/IN S Kenaf stalks, from fibre
production,
at farm/IN U

Table 23 (cont’d)

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting. Only consumptive
use of water taken into
account.

10%

Cultivation of Rye delivers the coproducts rye grains and baled rye
straw. 1 ha cultivated w/ rye.
Allocation based on economic
criteria. 9.7% to straw. Emissions
of N2O and NH3 to air and the
emissions of nitrate to water are
calculated w/ standard factors
from Nemecek et at. 2004.

materials, energy uses,
infrastructure and emissions.

116

19982000

USA

0%

Farming of rice on 1 plant acre for
1 year. Harvested acres are 99%
of planted acres. Impacts of
producing 1 kg seed are assumed
to be equal to producing 1 kg
grain. Only consumptive use of
water is taken into account.

Europe

USLCI
Ecoinvent

Rye straw conventional, at
farm/RER S - Rye straw
conventional, at farm/RER U

Rice straw, at field/kg/US

Table 23 (cont’d)

10%

19962003

Swiss Lowlands

10%

Production of 1 kg rye straw
extensive, at farm. Economic
allocation 90.3% to grains (see
report for exceptions).

soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included.

Production of 1 kg rye straw IP, at
farm. Economic allocation 90.3%
to grains (see report for
exceptions).

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included.

19962003

Swiss Lowlands

Ecoinvent
Ecoinvent

Rye straw IP, at farm/CH S –
Rye straw IP, at farm/CH U

Rye straw extensive,
at farm/CH S –
Rye straw extensive,
at farm/CH U

Table 23 (cont’d)

117

100
%

Production mix for integrated
straw for Switzerland

118

Swiss Lowlands

19982000

USA

USLCI

0%

1 planted acre for 1 year,
harvested acres are 98% of
planted acres. Only consumptive
use of water taken into account.

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting. Only consumptive
use of water taken into
account.

8%

Swiss Lowlands

19962003

Ecoinvent

Production of 1 kg rye straw
organic, at farm. Economic
allocation 91.9% to grains (see
report for exceptions).

soil cultivation, sowing, weed
control, fertilization, harvest
and grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included.

Ecoinvent

Straw IP, at farm/CH S - Soybean residues, at
Straw IP, at farm/CH U
field/kg/US

Rye straw orgainic, at farm/
CH S - Rye straw orgainic,
at farm/CH U

Table 23 (cont’d)

Ecoinvent

LCA Food DK
100
%
Denmark

Straw organic,
at farm/CH S

100
%

Straw, from farm

Production mix for organic straw
for Switzerland

119

Swiss Lowlands

Table 23 (cont’d)

Cultivation of straw on a straw
area.

100
%

1 ha cultivated with sweet
sorghum. Emissions of N2O and
NH3 to air are calculated with
standard mineral fertilizers from
Nemecek et al. Emission of nitrate
to water calculated with nitrogen
loss factor of 32%. Allocation
based on economic criteria.

120

included steps are harvest and
loading for transport

Swiss Lowlands

100
%

China

Ecoinvent
Ecoinvent

Sweet sorghum stem,
at farm/CN U - Sweet
sorghum stem, at
farm/CH S

Straw, from straw
areas, at field/CH S –
Straw, from straw
areas, at field/CH U

Table 23 (cont’d)

8%

19962003

Swiss Lowlands

8%

1kg wheat grains extensive, at
farm respectively wheat straw
extensive, at farm. Both with a
moisture content of 15%. 92.5%
allocated to grain.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and housing
included. Inputs of fertilizers,
pesticides and seed as well as
grain transports to the
regional processing center
(10km) are considered. The
direct emissions on the field
are also included.

1kg wheat grains IP, at farm
respectively wheat straw IP, at
farm. Both with a moisture
content of 15%. 92.5% allocated
to grain

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and housing
included. Inputs of fertilizers,
pesticides and seed as well as
grain transports to the
regional processing center
(10km) are considered. The
direct emissions on the field
are also included.

19962003

Swiss Lowlands

Ecoinvent
Ecoinvent

Wheat straw IP, at farm/CH S –
Wheat straw IP, at farm CH U

Wheat straw extensive,
at farm/CH S - Wheat straw
extensive, at farm/CH U

Table 23 (cont’d)

121

7%

1kg wheat grains IP, at farm
respectively wheat straw IP, at
farm. Both with a moisture
content of 15%. 93.1% allocated
to grain.

122

Soil cultivation, sowing , weed
control, harvest and grain
drying. Machine infrastructure
and housing included. Inputs
seed as well as grain
transports to the regional
processing center (10km) are
considered. The direct
emissions on the field are also
included.

19962003

Swiss Lowlands

Ecoinvent

Wheat straw organic,
at farm/CH S - Wheat straw
organic,
at farm/CH U

Table 23 (cont’d)

North America

USLCI
USLCI

12%

Wheat production based on US
average yields and practices
extrapolated from historic data to
2022. Includes all crop production
processes from field preparation
to crop maturity. Infrastructure,
maintenance, and construction of
facilities and equipment is
included. Harvest and storage is
not included. Grain and
components are assumed to be at
field-dry conditions of 15%
moisture. Multiple output process
has been reviewed by Dr. Dwayne
Westfall, Dept. Soil and Crop
Sciences, Colorado State
University. Date of review Sept 10
- 30 2008. Data from NASS where
available. Projections based on
historic data. Extrapolation of
historic data to 2022 estimates.
Mass based between straw and
grain.
1 metric ton of wheat straw, dried
to 12% moisture

North
America

Spring wheat straw, production, average, US, 2022

15%

spring wheat
straw, ground
and stored,
2022

Table 23 (cont’d)

123

spring
wheat
straw,
carted, 2022

USLCI

winter wheat
straw, ground
and stored

USLCI

Crop Production - Wheat Farming
North
America

12%
Crop Production - Wheat Farming
North America

Table 23 (cont’d)

124

15%

Wheat production based on US
average yields and practices
extrapolated from historic data to
2022. Includes all crop production
processes from field preparation
to crop maturity. Infrastructure,
maintenance, and construction of
facilities and equipment is
included. Harvest and storage is
not included. Grain and
components are assumed to be at
field-dry conditions of 15%
moisture. Multiple output process
has been reviewed by Dr. Dwayne
Westfall, Dept Soil and Crop
Sciences, Colorado State
University. Date of review Sept 10
- 30 2008. Data from NASS where
available. Projections based on
historic data. Extrapolation of
historic data to 2022 estimates.
Mass based between straw and
grain. Biomass Production: 12/08
Incremental Allocation

125

2022

North America

USLCI

Winter wheat straw, production, average, US, 2022

Table 23 (cont’d)

USLCI

1 planted acre for 1 year.
Harvested acres are 85% of the
planted acres. The impacts of
producing 1 kg of seed are
considered = to producing 1 kg
grain. Only consumptive use of
water taken into account.

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, Irrigation,
Harvesting

19982000`

USA

1 kg barley grains conventional, at
farm.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and machine
shelter included. Inputs of
fertilizers, pesticides, and seed
and their transports to the
farm are considered. The
direct emissions on the field
are also included. System
boundary at farm gate.

20002004

Barrios, France

North America

USLCI

Crop Production - Wheat Farming

Ecoinvent

Barley grains conventional,
Barrios, at farm/ FR S –
Barley grains conventional,
Barrios, at farm/ FR U

Wheat straw, at
field/kg/US

Winter wheat
straw, carted

Table 23 (cont’d)

0%

100
%

126

100
%

1 kg barley grains conventional, at
farm.

20002004

Castilla-y-Leon, Spain

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and machine
shelter included. Inputs of
fertilizers, pesticides, and seed
and their transports to the
farm are considered. The
direct emissions on the field
are also included. System
boundary at farm gate.

1 kg barley grains conventional, at
farm.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and machine
shelter included. Inputs of
fertilizers, pesticides, and seed
and their transports to the
farm are considered. The
direct emissions on the field
are also included. System
boundary at farm gate.

20002004

Saxony-Anhalt, Germany

Ecoinvent
Ecoinvent

Barley grains conventional, Saxony-Anhalt,
at farm/ DE S - Barley grains conventional,
Saxony-Anhalt, at farm/ DE U

Barley grains conventional,
Castilla-y-Leon, at farm/ES S Barley grains conventional,
Castilla-y-Leon, at farm/ES U

Table 23 (cont’d)

127

91%

19962003

Swiss Lowlands

90%

1 kg barley grains extensive and
barley straw extensive. Economic
allocation factor of 89.9% to
grains.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and machine
shelter included. Inputs of
fertilizers, pesticides, and seed
and their transports to the
regional processing center (10
km) are considered. The direct
emissions on the field are also
included. System boundary at
farm gate.

1 kg barley grains organic and
barley straw organic. Economic
allocation factor of 91.3% to
grains.

Soil cultivation, sowing, weed
control, fertilization, harvest
and drying of the grains.
Machine infrastructure and
machine shelter included.
Inputs of fertilizers, seed and
their transports to the regional
processing center (10 km) are
considered. The direct
emissions on the field are also
included. System boundary at
farm gate.

19962003

Swiss Lowlands

Ecoinvent
Ecoinvent

Barley grains organic,
at farm/CH S –
Barley grains organic,
at farm/CH U

Barley grains extensive, at farm/CH S Barley grains extensive, at farm/CH

Table 23 (cont’d)

128

Seed, harvesting, grain drying,
sowing, fertilization, tilling,
weed control, transport,
pesticide control.

90%

1 kg barley grains extensive and
barley straw extensive. Economic
allocation with 89.9% to grains.

Seed, harvesting, grain drying,
sowing, fertilization, tilling,
weed control, transport,
pesticide control.

90%

1 kg barley grains IP and barley
straw IP. Economic allocation with
89.9% to grains.

Seed, harvesting, grain drying,
sowing, fertilization, tilling,
weed control, transport,
pesticide control.

Swiss
Swiss Lowlands
Lowlands

Swiss Lowlands

90%

1 kg barley grains IP and barley
straw IP. Economic allocation with
89.9% to grains.

91%

1 kg barley grains organic and
barley straw organic. Economic
allocation factor of 91.3% to
grains.

Seed, harvesting, grain drying,
sowing, fertilization, tilling,
weed control, transport.

Swiss
Lowlands

GaBi-PE
GaBi-PE
GaBi-PE
GaBi-PE

barley
organic

barley IP

barley
extensive

barley grains IP,
at farm

Table 23 (cont’d)

129

USA (Northern Planes)

Ecoinvent

1 kg corn grain fictional unit
(water content 14%, carbon
content .375 kg/kg fresh mass,
biomass energy content 15.9
MJ/kg fresh mass, Yield 9315
kg/ha)

includes cultivation of corn in
the USA including use of
diesel, machines, fertilizers,
and pesticides

Modeled for USA

100
%

Harvested acres represent 91% of
the planted acres. The impacts of
producing 1kg seed are assumed
to be equal to producing 1 kg
grain. Only consumptive use of
water taken into account.

130

USA

International Journal of LCA January
2012

Carbon footprint of oilseed crops
grown in semi-arid great plains

CO2 equivalents from
agricultural activities
(fertilizer, pesticides,
herbicides, diesel for machines
etc.).

USLCI

corn, whole
plant, at field

Corn, at
farm/US S –
Corn, at
farm/US U

Carbon footprint of canola
and mustard as a function of
the rate of N Fertilizer

Table 23 (cont’d)

100
%

Corn on 1 planted acre for 1 year
(yield 3421 kg)

USA

100
%

Includes seed production
tillage fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting

1 kg grain maize IP at form with
moisture content of 14%. Fresh
matter yield/ ha 9279kg. Average
production in Swiss lowlands with
integrated production. 1996-2003
data collection

Includes the processes of soil
cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and shed for
machine sheltering is included.
Inputs of fertilizers, pesticides
and seeds as well as their
transports to the regional
processing center (10km), and
direct emissions on the field.

Swiss lowlands

USLCI
Ecoinvent

Grain maize IP,
at farm/CH S –
Grain maize IP,
at farm/CH U

Corn, at
field/kg/US

Table 23 (cont’d)

131

100
%

1 kg silage maize IP, at farm.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm are
considered. Direct emissions
on the field are also included.

132

Swiss Lowlands

100
%

1 kg grain maize IP at form with
moisture content of 14%. Fresh
matter yield/ ha 9279kg. Average
production in Swiss lowlands with
integrated production. 1996-2003
data collection

Includes the processes of soil
cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and shed for
machine sheltering is included.
Inputs of fertilizers, pesticides
and seeds as well as their
transports to the regional
processing center (10km), and
direct emissions on the field.

19962003

Swiss Lowlands

Ecoinvent
Ecoinvent

Silage maize IP,
at farm/CH S –
Silage maize IP,
at farm/CH U

Grain maize organic,
at farm/CH S –
Crain maize organic,
at farm/CH U

Table 23 (cont’d)

LCA Commons has very specific
datasets for each of the states
listed for multiple years. Not all
states have data for all years.

19952001,
2005

USA: Various States

100
%

Fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use.

1 kg silage maize organic, at farm.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm (1km)
are considered. Direct
emissions on the field are also
included.

19962003

Swiss Lowlands

LCA Commons
Ecoinvent

Silage maize organic,
at farm/CH S - Silage maize
organic, at farm/CH U

Corn grain, at
harvest in (year),
at farm 85-91%
moisture (state)

Table 23 (cont’d)

133

LCA Food DK

GHG and carbon footprint
only. Land use changes
included in this calculation.

100
%

134

Swiss Lowlands

Ecoinvent
Journal of Cleaner Production
2009

Oat, organic,
from farm

Data for Jatropha, soy, palm oil,
rape seed oil, and used cooking oil
for production of biodiesel.
Reports carbon footprint and GHG
savings (except for Jatropha).
Includes GHG from direct and
indirect land use change.

100
%

Transport processes required to
collect biowaste from households
and deliver to treatment plant.
Credit is given for extraction of
CO2 from atmosphere.

Transport as part of municipal
waste collection scheme.
Distance 15km for municipal
waste and 17km from
collection point to treatment
center. 40% dry matter, 70%
organic matter

2009

UK

Biowaste, at collection
point/CH S - Biowaste, at
collection point/CH U
Substitutable biodiesel
feedstocks for the UK: a review
of sustainability issues with
reference to the UK RTFO

Table 23 (cont’d)

2005

USA: Various States

100
%

Fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use.

2009

Denmark

LCA Commons
LCA Food DK
Journal of Cleaner Production 2009

LCA Commons has very specific
datasets for each of the states
listed for 2005.

UK

Oats, at harvest
in 2005, at
farm 86-92%
moisture (state)
Oat,
conventional,
from farm
Substitutable biodiesel feedstocks
for the UK: a review of
sustainability issues with
reference to the UK RTFO

Table 23 (cont’d)

Produced on farm type 21

Data for Jatropha, soy, palm oil,
rape seed oil, and used cooking oil
for production of biodiesel.
Reports carbon footprint and GHG
savings (except for Jatropha).
Includes GHG from direct and
indirect land use change.

135

GHG and carbon footprint
only. Land use changes
included in this calculation.

19982000

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control and harvest.
Machine infrastructure and
shed for machine sheltering
included. Inputs of fertilizers,
pesticides and seed as well as
their transports to the farm
(1km). Direct field emissions
included.

19962003

Harvested acres are 97% of
planted acres. Impacts of 1kg seed
are assumed to equal those of 1
kg potatoes. Only consumptive
use of water taken into account.

100
%

1 kg potatoes IP, at farm with
moisture content of 78%

136

USA

seed production, tillage,
fertilizer, pesticide, crop
residue management,
irrigation, harvesting

North America

Potato, whole plant, at field. 1
planted acre for 1 year

Swiss lowlands

USLCI

100
%

USLCI
Ecoinvent

Potatoes IP, at farm/CH S –
Potatoes IP, at farm/CH U

potato, whole
plant,
at field

Potato, at
field/kg/US

Table 23 (cont’d)

100
%

1 kg of potatoes organic, at farm
with moisture content of 78%

19962003

Swiss lowlands

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control and harvest.
Machine infrastructure and
shed for machine sheltering
included. Inputs of fertilizers,
pesticides and seed as well as
their transports to the farm
(1km). Direct field emissions
included.

1 kg potatoes, at farm with
moisture content of 78%.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, irrigation
and harvest. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm are
considered. The direct
emissions on the field are
included and the system
boundary is the farm gate.

20012006

USA

Ecoinvent
Ecoinvent

Potatoes, at farm/US S –
Potatoes, at farm/US U

Potatoes organic,
at farm/CH U –
Potatoes organic,
at farm/CH S

Table 23 (cont’d)

137

100
%

Machinery, human labor,
diesel, biocide, fertilizer, water
for irrigation, seed.

2012

Weighted average of the marginal
production at 28 farm types

Soil cultivation, sowing,
fertilizing, plant protection,
harvesting, making silage and
transport of crops. Pesticides
and machine and building
construction and maintenance
also not included.

138

England / Wales

2010

Iran

Crop storage, cooling and
drying prior to sale. System
boundary is the farm gate.
Energy, pesticides, herbicides,
and fertilizer, land occupation,
and irrigation water included

Denmark

International Journal of Life Cycle
Assessment September 2010
Journal of Cleaner
Production Vol 33
LCA Food DK

Production of potatoes in Iran,
scenarios based on farm size.
Energy and GHG reported.

Potatoes,
from farm

Environmental burdens of
producing bread wheat, oilseed
rape, and potatoes in England
and wales using simulation
and system modeling

Fictional unit is 1 ton marketable
fresh weight. What is marketable
as food < what can be used for
ethanol production. Long term
approach to soil nutrients.

Energy consumption
and CO2 emission
analysis of potato
production based on
different farm size
levels in Iran

Table 23 (cont’d)

100
%

Cultivation of rape in Germany
modeled with data from literature

Use of diesel, machines,
fertilizers, and pesticides

1996,
2001,
2006

Germany

100
%

1 kg rape seed conventional,
Barrios, at farm (10% moisture
content)

soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and a shed for
machine sheltering. System
boundary is at farm gate. Seed
inputs and material transport

20002004

Barrios, France

Ecoinvent
Ecoinvent

Rape seed conventional,
Barrios, at farm/FR S –
Rape seed conventional,
Barrios, at farm/FR U

Rape seed conventional,
at farm/DE S –
Rape seed conventional,
at farm /DE U

Table 23 (cont’d)

139

100
%

1 kg rape seed conventional,
Saxony-Anhalt, at farm (9%
moisture content).

20002004

Saxony-Anhalt, Germany

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and a shed for
machine sheltering. System
boundary is at farm gate. Seed
inputs and material transport

1 kg rape seed extensive, at farm
(6% moisture content)

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure, shed for
machine sheltering, inputs for
seeds, transport of materials
to regional processing center
(10km). Direct emissions on
the field.

19962003

Swiss lowlands

Ecoinvent
Ecoinvent

Rape seed extensive, at
farm/CH S - Rape seed
extensive, at farm/CH U

Rape seed conventional, SaxonyAnhalt, at farm/DE S - Rape seed
conventional, Saxony-Anhalt,
at farm/DE U

Table 23 (cont’d)

140

1 kg rape seed, at farm with 6%
moisture content. Yield is given as
fresh matter, 12% moisture
content. Water for irrigation is
pumped form 48 m depth w/
electric pumps.

Soil cultivation, sowing, weed
control, fertilization, pesticides
and pathogen control, harvest.
Machine infrastructure and
sheltering, inputs of fertilizers,
pesticides and seed as well as
their transports to the farm,
direct emissions on the field.

20012006

1 kg rape seed, organic, at farm
with 6% moisture. Fresh matter
yield at 6% moisture given.

soil cultivation, fertilization,
harvest, drying and transport
to farm

141

Swiss lowlands

19962003

USA

100
%

1 kg rape seed IP, at farm with
moisture content of 6%.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
drying of the grains. Machine
infrastructure and sheltering,
inputs of fertilizers, pesticides
and seed as well as their
transports to the regional
processing center (10km),
direct emissions on the field.

Swiss lowlands

Ecoinvent

100
%

Ecoinvent

Ecoinvent

Rape seed,
at farm/US S –
Rape seed,
at farm/US U

100
%

Rape seed, organic,
at field/CH S - Rape
seed, organic, at
field/CH U

Rape seed IP, at farm/CH S Rape seed IP, at farm/CH U

Table 23 (cont’d)

1 planted acre for 1 year,
harvested acres are 97% of
planted acres. Only consumptive
use of water taken into account.

seed production, tillage,
fertilizer, pesticide, crop
residue management,
irrigation, harvesting

19982000

USA and European
average practices

USLCI
International Journal of
Life Cycle Assessment

100
%

Functional unit is 1 ton
marketable fresh weight. What is
marketable as food < what can be
used for ethanol production. Long
term approach to soil nutrients.

Crop storage, cooling and
drying prior to sale. System
boundary is the farm gate.
Energy, pesticides, herbicides,
and fertilizer, land occupation,
and irrigation water included

2010

England / Wales

Rapeseed,
at field/kg/US
Environmental burdens of producing
bread wheat, oilseed rape, and
potatoes in England and wales using
simulation and system modeling

Table 23 (cont’d)

142

USLCI
LCA Commons

100
%

seed production, tillage,
fertilizer, pesticide, crop
residue management,
irrigation, harvesting

19982000

LCA Commons has specific data
for each state listed for 2006.

Fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use.

2006

143

North America

UK

2009

USA

Rice grain,
at field/kg/US

Harvested acres are 99% of
planted acres. Impacts of 1kg seed
are assumed to equal those of 1
kg grain. Only consumptive use of
water taken into account.

GHG and carbon footprint
only. Land use changes
included in this calculation.

USA: Various
States

Journal of Cleaner
Production 2009
USLCI

1 planted acre for 1 year,
harvested acres are 97% of
planted acres. Only consumptive
use of water taken into account.

rapeseed,
whole plant,
at field

Data for Jatropha, soy, palm oil,
rape seed oil, and used cooking oil
for production of biodiesel.
Reports carbon footprint and GHG
savings (except for Jatropha).
Includes GHG from direct and
indirect land use change.

Rice; at harvest
in 2006 at
farm 63-90%
moisture (state)

Substitutable biodiesel
feedstocks for the UK:
a review of sustainability
issues with reference
to the UK RTFO

Table 23 (cont’d)

1 kg rice at farm. Yield given at
21% moisture

90%

1 ha cultivated with sweet rye.
Emissions of N2O and NH3 to air
are calculated with standard
mineral fertilizers from Nemecek
et al. Emission of nitrate to water
calculated with the method
described in Nemecek et al.
Allocation based on economic
criteria 90.3% grains, 9.7% straw.

144

20012006

USA

100
%

Soil cultivation, sowing, weed
control, pest and pathogen
control, irrigation and harvest.
Machine infrastructure and
shelter included. Inputs of
fertilizers, pesticides, and seed
as well as their transports to
the farm are considered. The
direct emissions on the field
are also included. System
boundary is at the farm gate.

Cultivation of rye in Europe
including materials, energy
uses, infrastructure, and
emissions.

Time
of
literat
ure
public
ations
(vario
us)

Europe

Ecoinvent
Ecoinvent

Rye grains conventional,
at farm/RER S –
Rye grains conventional,
at farm/RER U

Rice, at farm/US S –
Rice, at farm/US U

Table 23 (cont’d)

90%

19962003

Swiss Lowlands

90%

1 kg rye grains extensive.
Economic allocation with factor of
90.3% to grains.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and shed for
machine sheltering included,
as well as the inputs for
fertilizers, pesticides and seed.
Grain transports to the
regional processing center
(10km) are considered. Direct
emissions on the field are also
included.

1 kg rye grains IP. Economic
allocation with factor of 90.3% to
grains.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and shed for
machine sheltering included,
as well as the inputs for
fertilizers, pesticides and seed.
Grain transports to the
regional processing center
(10km) are considered. Direct
emissions on the field are also
included.

19962003

Swiss Lowlands

Ecoinvent
Ecoinvent

Rye grains IP, at farm/CH S Rye grains IP, at farm/CH U

Rye grains extensive, a
t farm/CH S Rye grains extensive,
at farm/CH U

Table 23 (cont’d)

145

100
%

1 ha cultivated with sweet
sorghum. Emissions of N2O and
NH3 to air are calculated with
standard mineral fertilizers from
Nemecek et al. Emission of nitrate
to water calculated with nitrogen
loss factor of 32%. Allocation
based on economic criteria.

Diesel, machines, fertilizers
and pesticides

146

19962003

Swiss Lowlands

92%

1 kg rye grains organic. Economic
allocation with factor of 91.9% to
grains.

Soil cultivation, sowing, weed
control, fertilization, harvest
and grain drying. Machine
infrastructure and shed for
machine sheltering included,
as well as the inputs for
fertilizers, and seed. Grain
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field are also included.

China

Ecoinvent
Ecoinvent

Sweet sorghum grains,
at farm/CN S –
Sweet sorghum grains,
at farm/CN U

Rye grains organic,
at farm/CH S - Rye grains
organic, at farm/CH U

Table 23 (cont’d)

China
USA

USLCI
LCA Food
DK

Diesel, machines, fertilizers
and pesticides

Argentina

Ecoinvent

Soybean grains, at field

“Carbon Sequestration should be
accounted for after the product is
built in its LCA model, and should
be included depending on the use
of end of life fate of that product.
For example, a soy-based resin
may retain the sequestered
carbon indefinitely, while a soybased biodiesel releases the
sequestered carbon at use phase”
(USLCI).

sweet sorghum

1 ha cultivated with sweet
sorghum. Emissions of N2O and
NH3 to air are calculated with
standard mineral fertilizers from
Nemecek et al. Emission of nitrate
to water calculated with nitrogen
loss factor of 32%. Allocation
based on economic criteria.

Soy bean,
from farm

Table 23 (cont’d)

100
%

147

100
%

1 kg of soy beans IP, at farm with
a moisture content of 11%

19962003

Swiss lowlands

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and housing.
Inputs for fertilizers, pesticides
and seed as well as their
transports to the regional
processing center (10km) are
considered, direct emissions
on the field also included.

1 kg soy beans organic, at farm
with moisture content of 11%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and housing.
Inputs for fertilizers, pesticides
and seed as well as their
transports to the regional
processing center (10km) are
considered, direct emissions
on the field also included.

19962004

Swiss lowlands

Ecoinvent
Ecoinvent

Soy beans organic,
at farm/CH S –
Soy beans organic,
at farm/CH U

Soy beans IP, at farm/CH S –
Soy beans IP at farm/CH U

Table 23 (cont’d)

148

19982000

Brazil

100
%

1 planted acre for 1 year

100
%

1 kg soybeans with moisture
content of 11%. Modeled with
data from literature, some data
extrapolated from Europe
(production of fertilizers and
pesticides), and Switzerland
(machine use). Transports
modeled for standard distances.

Use of diesel, machines,
fertilizers, and pesticides

Time
of
literat
ure
public
ations
(vario
us)

LCA Commons has specific data
for each state listed for various
years. Not all states are available
for all years.

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use, seeding

19962000,
2002,
2006

149

USA

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation and
harvesting. Only consumptive
use of water taken into
account.

USA: Various States

Ecoinvent
LCA Commons

USLCI

Soybeans, at farm/BR S Soybeans, at farm/BR U
Soybeans; at harvest
in (year); at farm;
85-92%
moisture (state)

Soybeans,
at field/kg/US

Table 23 (cont’d)

LCA
Food DK

Weighted averages of the
marginal production at 28 farm
types

150

UK

Sugar beet,
from farm

100
%

2009

Use of diesel, machines,
fertilizers, and pesticides

Time
of
literat
ure
public
ations
(vario
us)

USA, with some data from
Europe and Switzerland

Ecoinvent

100
%

1 kg soybeans with moisture
content of 11%. Modeled with
data from literature, some data
extrapolated from Europe
(production of fertilizers and
pesticides), and Switzerland
(machine use). Transports
modeled for standard distances.

GHG and carbon footprint
only. Land use changes
included in this calculation.

Denmark

Journal of Cleaner
Production 2009

Data for Jatropha, soy, palm oil,
rape seed oil, and used cooking oil
for production of biodiesel.
Reports carbon footprint and GHG
savings (except for Jatropha).
Includes GHG from direct and
indirect land use change.

Soybeans, at farm
/US S –
Soybeans, at farm
/US U

Substitutable biodiesel
feedstocks for the UK: a
review of sustainability
issues with reference to
the UK RTFO

Table 23 (cont’d)

100
%

1 kg sugar beets IP, at farm with a
moisture content of 77%

Cultivation of sugar cane with 20%
mechanical harvest and 80%
manual harvest

151

19962003

Swiss lowlands

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control and harvest.
Machine infrastructure and
housing included. Inputs of
fertilizers, pesticides and seed
as well as their transports to
the farm (1km) are
considered. Direct emissions
on the field also included.

diesel, machines, fertilizers,
and pesticides

Time
of
literat
ure
public
ations
(vario
us)

Brazil

Ecoinvent
Ecoinvent

Sugarcane,
at farm/BR S –
Sugarcane,
at farm/BR U

Sugar beets IP,
at farm/CH S –
Sugar beets IP,
at farm/CH U

Table 23 (cont’d)

International Journal of Life
Cycle Assessment Nov 2010

International Journal of LCA November 2009

GHG data for the cultivation of
Taiwanese sugarcane

152

Soil preparation, growing,
harvesting, transport
2009
Taiwan

Australia

Life cycle assessment of
Australian sugar cane
production with a focus on
sugarcane growing

A decision support tool for modifications
in crop cultivation method based on LCA: a
case study on GHG emissions reduction in
Taiwanese sugarcane cultivation

Table 23 (cont’d)

Production and energetic
utilization of wood from short
rotation coppice - a life cycle
assessment

International Journal of
Life Cycle Assessment
July 2010

Environmental
assessment of black
locust based ethanol
as potential

International Journal of
Life Cycle Assessment
June 2011

Table 23 (cont’d)

This study is broken into parts: 1 production of wood (poplar
chips). This is the only part
applicable to this study as step 2 is
the combustion of the biofuel in a
medium sized car. This study
assumes carbon neutrality.

153

Soil preparation, harvest,
drying, and transport. FU = 1
oven dried ton.

Description mass of Switchgrass at
34% moisture with 20% harvest
loss.

154

This process Includes all crop
production processes from
field preparation to harvest
and baling. Assuming a 10year stand. Transport and
storage of bales is not
included.

2022

USA

USLCI
USLCI

US switchgrass production
(1hectare) for 2022. Based on
projections from Sokhansanj et al.
2009 and MacLaughlin and Kszos
2005. The mean switchgrass yield
in the US in 2008 was estimated
to be 11.3 Mg per ha, this includes
both upland and lowland
varieties. The projected 2022 yield
was determined by the following
(2022 yield = (2008
yield)*(1+.02)^14, where the 2008
yield = 11.2 Mg per hectare, 0.02
is the 2% annual yield
improvement, and 14 is the
number of years. The annual rate
of yield increase is estimated to
be 2%.

USA

Switchgrass, production, US, 2022
Switchgrass,
harvested,
wet

Table 23 (cont’d)

1 kg wheat grains conventional,
Barrios w/ moisture content
14.5%

20002004

Seeding, fertilizer, herbicide
(different amounts and types
for year 1 than for years 2-20).

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm are
considered. Direct emissions
on the field included, system
boundary at farm gate.

2022

North America

100
%

Seed, planting

Barrios, France

USLCI
International Journal of Life
Cycle Assessment
Ecoinvent

Planting,
switchgrass,
2022

Functional unit of study is power
to the wheels of a car, but
separate LCA data of Switchgrass
production is included. This is a 20
year crop cycle where year 1 is
separated out because it has
significantly different inputs.

Wheat grains conventional,
barrios, at farm/FR S –
Wheat grains conventional,
barrios, at farm/FR U

Includes only seed and planting

Life cycle assessment of
switchgrass derived ethanol
as transport fuel

Table 23 (cont’d)

155

100
%

1 kg wheat grains conventional,
Castilla-y-Leon w/ moisture
content 15%

20002004

Castilla-y-Leon, Spain

100
%

soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering are
included. Inputs of fertilizers,
pesticides, and seed as well as
their transports to the farm
are considered. Direct
emissions on the field
included, system boundary at
farm gate.

1 kg wheat grains conventional,
Saxony-Anhalt w/ moisture
content 14.5%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the farm are
considered. Direct emissions
on the field included, system
boundary at farm gate.

20002004

Saxony-Anhalt, Germany

Ecoinvent
Ecoinvent

Wheat grains conventional,
Saxony-Anhalt, at farm/
DE S -Wheat grains conventional,
Saxony-Anhalt, at farm/DE U

Wheat grains conventional,
Castilla-y-Leon, at farm/ES S –
Wheat grains conventional,
Castilla-y-Leon, at farm/ES U

Table 23 (cont’d)

156

93%

1 kg wheat grains extensive, at
farm respectively wheat straw
extensive, at farm both with
moisture content of 15%. 92.5%
allocated to grain, remainder to
straw.

157

19962003

Swiss lowlands

93%

1 kg wheat grains extensive, at
farm respectively wheat straw
extensive, at farm both with
moisture content of 15%. 92.5%
allocated to grain, remainder to
straw.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included, system
boundary at farm gate.

Swiss Lowlands

Ecoinvent
GaBi-PE

wheat extensive

Wheat grains extensive,
at farm/CH S
Wheat grain extensive,
at farm/CH U

Table 23 (cont’d)

93%

1 kg wheat grains IP, at farm
respectively wheat straw IP, at
farm both with moisture content
of 15%. 92.5% allocated to grains,
remainder to straw.

158

19962003

Swiss lowlands

93%

1 kg wheat grains IP, at farm
respectively wheat straw IP, at
farm both with moisture content
of 15%. 92.5% allocated to grains,
remainder to straw.

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, harvest and
grain drying. Machine
infrastructure and a shed for
machine sheltering are
included. Inputs of fertilizers,
pesticides, and seed as well as
their transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included, system
boundary at farm gate.

Swiss Lowlands

Ecoinvent
Ecoinvent

wheat IP

Wheat grains IP, at farm/CH S –
Wheat grains IP, at farm/CH U

Table 23 (cont’d)

93%

1 kg wheat grains organic, at farm
respectively wheat straw organic,
at farm both with moisture
content of 15%. 93.1% allocated
to grains, remainder to straw.

159

19962003

Swiss lowlands

93%

1 kg wheat grains organic, at farm
respectively wheat straw organic,
at farm both with moisture
content of 15%. 93.1% allocated
to grains, remainder to straw.

Soil cultivation, sowing, weed
control, fertilization, harvest
and grain drying. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides,
and seed as well as their
transports to the regional
processing center (10km) are
considered. Direct emissions
on the field included.

Swiss Lowlands

Ecoinvent
GaBi-PE

wheat organic

Wheat grains organic,
at farm/CH S – Wheat
grains organic,
at farm/CH U

Table 23 (cont’d)

1 kg wheat grains, at farm w/
moisture content 15%

100
%

1 planted acre for 1 year.
Harvested acres are 85% of the
planted acres. The impacts of
producing 1 kg of seed are
considered = to producing 1 kg
grain. Only consumptive use of
water taken into account.

Seed production, tillage,
fertilizer and pesticide
application, crop residue
management, irrigation,
harvesting

160

20012006

USA

100
%

Soil cultivation, sowing, weed
control, fertilization, pest and
pathogen control, irrigation
and harvest. Machine
infrastructure and a shed for
machine sheltering is included.
Inputs of fertilizers, pesticides
and seed as well as their
transports to the farm are
considered. The direct
emissions on the field are
included, system boundary at
farm gate.

19982000

USA

Ecoinvent
USLCI

Wheat grains,
at field/kg/US

Wheat grains, at farm/
US S - Wheat grains,
at farm/US U

Table 23 (cont’d)

LCA Commons

USA

LCA Food DK

100
%

Denmark

USLCI

Wheat
conventional,
from farm

1 planted acre for 1 year.
Harvested acres are 85% of the
planted acres. The impacts of
producing 1 kg of seed are
considered = to producing 1 kg
grain. Only consumptive use of
water taken into account.

LCA Commons has specific data
for each state listed for various
years. Not all states are available
for all years.

161

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use, seeding

19961998,
2000,
2004,
2009

USA: Various States

wheat, at field

100
%

Winter wheat; at
harvest in (year); at
farm; 86-90%
moisture (state)

Table 23 (cont’d)

USA: Various States

LCA Commons
LCA
Food DK

19961998,
2000,
2004,
2009

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use, seeding

19961998,
2000,
2004,
2009

California, North Dakota,
Montana

LCA Commons

Durum wheat;
at harvest in (year);
at farm; 88-89%
moisture (state)

LCA Commons has specific data
for each state listed for various
years. Not all states are available
for all years.

fertilizer, water, land use and
conversion, transportation,
tilling, harvest, pesticide and
herbicide use, seeding

Denmark

Spring wheat; exclu. Durum;
at harvest in (year); at farm;
86-90% moisture (state)

LCA Commons has specific data
for each state listed for various
years. Not all states are available
for all years.

Wheat,
organic,
from farm

Table 23 (cont’d)

100
%

162

The impact of local crops
consumption on the water
resources in Beijing

Journal of Cleaner Production
Vol 21

Reports water consumption only
for the following crops: Wheat,
maize, sweet potato, soybean,
groundnut, watermelon, open
vegetables, covered vegetables.
Water is divided into the sub
categories of blue, green and
gray.

163

Crop storage, cooling and
drying prior to sale. System
boundary is the farm gate.
Energy, pesticides, herbicides,
and fertilizer, land occupation,
and irrigation water included

2010

England / Wales

International Journal of Life
Cycle Assessment September
2010

Functional unit is 1 ton
marketable fresh weight. What is
marketable as food < what can be
used for ethanol production. Long
term approach to soil nutrients.

Rainfall, irrigation, nitrogen
application, planted area.

2011

China

Environmental burdens of
producing bread wheat, oilseed
rape, and potatoes in England
and Wales using simulation and
system modeling

Table 23 (cont’d)

GHG, eutrophication, and land
occupation reported. Different
fertilizer and yield scenarios are
reported.

Direct N and P emissions

*Allocation is based on economic values unless otherwise noted.

164

Denmark

International Journal of Life
Cycle Assessment June 2008

System delimitation in
agricultural consequential
LCA: outline of methodology
and illustrative case study of
wheat in Denmark

Table 23 (cont’d)

Weed Control
Pesticide
Crop Residue

F
G
H
I
J
K

1
1
1
1
1
1
1

1
1
1
1
1
1
1

165

N
O

1
1
1

1
1
1

Land Transformation

M

Land Occupation

L

Crop Storage

Direct Field Emissions

Fertilizer

E

Grain Drying

Tillage

D

Harvesting

Sowing

C

Irrigation

Machine Shelter

1

Farm Machine

Transport

1

Diesel used on field

Seed Production

B

Barley straw extensive,
at farm/CH S - Barley
straw extensive, at farm
/CH U
Name

A

Barley straw IP,
at farm/CH S –
Barley straw IP, at
farm/CH U

Table 24 – Raw Agricultural Data Summary Inputs

P
Q
R

1
1
1

1
1
1

D
E
F
G
H
I

Barley straw organic,
at farm/CH S –
Barley straw organic,
at farm/CH U
1
1
1
1
1
1
1

Corn stover, at
field/kg/US

1
1
1
1
1
1
1
1

Cotton straw,
at field/kg/US

Table 24 (cont’d)
A

1
1
1
1
1
1
1
1

B

1

C

166

J
L
M
N
O

1

K

1
1

P
Q
R

1
1

Kenaf stalks,
Jute stalks, from fibre
from fibre production, production, refined system,
at farm/IN S - Kenaf
at farm/IN S –
stalks,
Jute stalks, from fibre
from fibre production, production, refined system,
at farm/IN U
at farm/IN U

Jute stalks, from
fibre production,
integrated system,
at farm/IN S
- Jute stalks, from
fibre production,
integrated system,
at farm/IN U

Table 24 (cont’d)
A
B
C
D
E
F
G
H

1
1
1

1
1

1
1

167

I
J
Q
R

1
1
1

1
1
1
1

1
1
1
1

K
L
M
N
O
P

Rye straw conventional,
at farm/RER S –
Rye straw conventional,
at farm/RER U

H

Potato
leaves, at
field/
kg/US

1
1

1

Rice straw,
at field/kg/US

A
G

Rapeseed
residues,
at field/kg/US

Table 24 (cont’d)

1

B
C
D
E
F
J
K
L
M

1
1
1
1
1
1

1
1
1
1
1
1
1

1
1
1
1
1
1
1

168

I
N
O
P
Q

1

R

1

D
E
F
G
H
I
J

Rye straw extensive,
at farm/CH S –
Rye straw extensive,
at farm/CH U

1
1
1
1
1
1
1
1

Rye straw IP,
at farm/CH S
- Rye straw IP,
at farm/CH U

1
1
1
1
1
1
1
1

Rye straw
organic,
at farm/CH S –
Rye straw
organic,
at farm/CH U
1
1
1
1
1
1
1
1

Soybean
residues,
at field/
kg/US

Table 24 (cont’d)
A

1
1
1

B
C

169

M
N
O

1
1
1

1
1

1

1

K

1

L

1
1

Q
R

1
1
1

1
1
1
1

1
1
1
1

P

1

Straw,
From
farm

R

1
1

Straw
organic,
at farm
/CH S

Straw, from
Straw areas,
at field/CH S
- Straw, from
straw areas,
at field/CH U
A
Q

Straw IP,
at farm/
CH S –
Straw IP,
at farm
/CH U

Sweet sorghum
stem,
at farm/CN U
- Sweet sorghum
Stem at
at farm/CH S

Table 24 (cont’d)

1
1

1
1

1
1

B
C
D
E
F
G
H

170

I
J
K
L
M

1

N
O
P

spring
Spring
wheat
wheat
straw,
straw,
ground and production,
stored,
average, US,
2022
2022

Wheat straw
organic,
at farm/CH S –
Wheat straw
organic,
at farm/CH U

Wheat
Wheat straw
straw IP,
extensive,
farm/CH S – at farm/CH S –
Wheat
Wheat straw
straw IP,
extensive,
at farm CH U at farm/CH U

Table 24 (cont’d)
A
B

1

1

1

D
E
F
G
H
I
J

1
1
1
1
1
1
1

1
1
1
1
1
1
1

1
1
1
1

C

1

171

M
N
O

1
1
1

1
1

1

K
L
Q
R

1
1
1

1
1
1
1

1
1
1
1

P

1

1

Wheat
straw,
at field
/kg/US
spring
wheat straw,
carted, 2022

1

winter wheat
straw,
ground and
stored

1

Winter
wheat straw,
production,
average,
US, 2022

1

Winter
wheat
straw,
carted

Table 24 (cont’d)
A

1

B
C
D
E
F
G

1

H

1

172

I
J

1

K

1

L

1

M

1

N
O
P
Q

1

1

R

D
E
F
G
H
I
J

Barley grains
conventional,
Barrios, at farm/
FR S – Barley grains
conventional,
Barrios,
at farm/ FR U
1
1
1
1
1
1
1
1

Barley grains
conventional,
Castilla-y-Leon,
at farm/ES S Barley grains
conventional,
Castilla-y-Leon,
at farm/ES U

Barley grains
Barley grains
conventional,
extensive, at farm Saxony-Anhalt,
/CH S
at farm/ DE S - Barley grains
Barley grains
extensive, at farm conventional,
/CH
Saxony-Anhalt,
at farm/ DE U

Table 24 (cont’d)
A

1
1
1
1
1
1
1

1
1
1
1
1
1

1
1
1
1
1
1

B
C
M
N
O

1
1
1

1
1
1

1
1
1

1
1
1

173

K
L
Q
R

1
1
1

1
1
1
1

1
1
1
1
1

1
1
1
1
1

P

Carbon footprint
Barley grains
of canola and
Barley
organic, at
mustard as a
barley Barley Barley grains
farm/CH S function of
organic
IP extensive
IP,
Barley grains
the rate of N
at farm organic, at
Fertilizer
farm/CH U

Table 24 (cont’d)
A
B

1
1

1

D
E
F
G
H
I

1
1
1
1
1
1

1
1
1
1
1

1
1
1
1
1

1
1
1
1

1
1
1
1

C

1

M
N
O

1
1
1

1
1

1
1

1
1
1

1
1

1
1

174

J

1

L
Q
R

1
1

1
1
1

1
1
1
1

1
1
1
1

1

K

1
1
1

P

Corn,
at field
/kg/US

1

Grain maize IP,
at farm/CH S Grain maize IP,
at farm/CH U

1
1
1
1
1
1
1
1
1
1
1
1
1

Grain maize
organic, at
farm/CH S Crain maize
organic, at
farm/CH U
corn,
whole
plant, at
field

Corn, at
farm/US S Corn, at
farm/US U

Table 24 (cont’d)
A
C
D

1

B

1

E
F
G

1
1
1
1
1
1
1
1
1
1
1
1
1

1

H

175

I

1

1

J

1

K

1
1

M
N
O

1

1

P
Q
R

1

L

1

1

1

Biowaste, at
collection
point/CH S Biowaste, at
collection
point/CH U
Silage maize
organic, at
farm/CH S Silage maize
organic, at
farm/CH U

Corn grain, at
harvest in
Silage maize IP,
(year), at farm at farm/CH S 85-91%
Silage maize IP,
moisture
at farm/CH U
(state)

Table 24 (cont’d)
A
B

1
1

1
1

C
D
E
F

1
1
1

1

1
1
1

G

1

H
J

1
1

1

1

176

M
N
O

1
1
1
1

1
1
1

1
1
1

K
L

1
1

R

1
1

1

P
Q

1

1
1

1

I

1

Oat,
Oats, at harvest in
Oat,
conventional,
2005, at farm 86organic,
from farm
92% moisture (state) from farm

Substitutable biodiesel
feedstocks for the UK: a
review of sustainability
issues with reference to
the UK RTFO

Table 24 (cont’d)
A
B

1

C
D
E
F
G

1

H

1

177

I

1

J

1

K
L

1

M

1

1

N
O

1

P
Q
R

1
1

1
1

Potatoes IP, at
farm/CH S
- Potatoes IP, at
farm/CH U

1
1
1
1
1
1
1
1
1
1
1
1
1

Potatoes
organic, at
farm/CH U Potatoes
organic, at
farm/CH S
potato,
whole
plant, at
field
Potato,
at field
/kg/US

Substitutable
biodiesel
feedstocks for the
UK: a review of
sustainability
issues with
reference to the
UK RTFO

Table 24 (cont’d)
A
B
C
D
E
F

1

G

1

H

178

I

1

J

1

K

1

L

1

M

1

N
O

1

1

P
Q
R

1
1

1

1

1
1
1
1
1
1
1
1
1
1
1
1
1

Energy consumption
and CO2 emission
Potatoes,
analysis of potato
from farm production based on
different farm size
levels in Iran

Environmental burdens of
producing bread wheat,
oilseed rape, and potatoes
in England and wales
using simulation and
system modeling
Potatoes,
at farm/US S Potatoes,
at farm/US U

Table 24 (cont’d)
A
B

1
1

1

C

1

1

D
E
F
G
H
I
J

1
1
1
1
1
1

1

1

1

1
1
1

179

L
M

1
1
1

1
1
1
1

1
1
1

K

1

N

1

O
P

1

1

1

Q
R

1
1

Rape seed
conventional,
Barrios, at
farm/FR S –
Rapeseed
conventional,
Barrios, at
farm/FR U
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Rape seed
conventional,
Saxony-Anhalt,
at farm/DE S Rape seed
conventional,
Saxony-Anhalt,
at farm/DE U
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Rape seed
extensive, at
farm/CH S –
Rape seed
extensive, at
farm/CH U
Rape seed
conventional, at
farm/DE S - Rape
seed conventional,
at farm /DE U

Table 24 (cont’d)
A
C
D

1

B

1

E
F
G
H

180

I

1

J
K
L
M
O

1

P
Q
R

1

N

1

1
1
1
1
1
1
1
1
1
1
1
1
1
1

Environmental
burdens of
producing bread
Rapeseed,
wheat, oilseed
organic, at
Rapeseed,
rape, and
field/CH S –
at field
potatoes in
Rape seed,
/kg/US
England and
organic, at
wales using
field/CH U
simulation and
system modeling
Rapeseed,
at farm/
Rape seed IP, at
US S farm/CH S - Rape
Rapeseed, seed IP, at farm/CH
at farm
U
/US U

Table 24 (cont’d)
A
B

1

1

1

D
E
F
G
H
I
J

1
1
1
1
1
1
1

1
1
1
1
1
1
1

1
1

1
1

C

1

1
1

181
1

M
N
O

1
1
1

1
1

1

1

K

1

L

1

1
1

1
1

1

1

Q
R

1
1
1

1
1
1

1
1

P

1

1

Rice, at
farm/
US S –
Rice, at
farm/
US U

Rice; at
harvest in
2006 at
farm
63-90%
moisture
(state)
Rice grain,
at
field/kg/US

Substitutable
biodiesel feedstocks
rapeseed,
for the UK: a review
whole plant,
of sustainability
at field
issues with reference
to the UK RTFO

Table 24 (cont’d)
A

1

B

1

1

C

1

D

1

1
1

1

F

1
1

1

1

E
G
H

182

I
J

1
1
1

1
1
1
1

1

K

1

L

1

1

M

1

N
O

1

P
Q
R

1
1

1

1
1
1

1
1
1
1

1
1
1
1

Rye grains
extensive, at
farm/CH S Rye grains
extensive, at
farm/CH U
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Rye grains IP,
at farm/CH S –
Rye grains IP,
at farm/CH U

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Rye grains
organic, at
farm/CH S –
Rye grains
organic,
at farm/CH U

Rye grains
conventional, at
farm/RER S - Rye
grains
conventional, at
farm/RER U

Table 24 (cont’d)
A
B
C
D
F
G
H

183

I
J
K
L
M
N
O

1

P
Q
R

1

E

1

1
1
1
1
1
1
1
1
1
1
1
1
1
1

Soy beans IP,
at farm/CH S
- Soy beans IP
at farm/CH U

Sweet sorghum
grains, at farm
/CN S - Sweet
sorghum grains,
at farm/CN U

1
1
1

1
1

sweet
sorghum

D

1

Soybean
grains, at
field

C

1

Soy
bean,
from
farm

Table 24 (cont’d)
A

1

B

1
1
1

E

1

F

1

G

1

H

1

184

I

1

1

Q
R

1
1
1

1

J

1
1

1

K
L
M

1

N

1

O

1

P

1

1
1

Soybeans;
at harvest in
(year); at
farm; 8
5-92%
moisture
(state)
Soybeans, at
farm/BR S Soybeans, at
farm/BR U

Soy beans
Soybeans,
organic, at
at field/
farm/CH S - Soy
kg/US
beans organic, at
farm/CH U

Table 24 (cont’d)
A
B
C
D
E
F
G
H
I
J

1
1
1
1
1
1
1
1
1
1

1
1
1

1
1

1

1
1

1

1
1

1
1
1

185
1
1

K

1
1

M
N
O

1

L

1
1

1

1

P
Q
R

1
1

1

1
1

1
1

Sugar
beet,
from
farm

1

Soybeans, at
farm/US S Soybeans, at
farm/US U

Sugar beets
IP, at farm
/CH S - Sugar
beets IP, at
farm/CH U

Substitutable
biodiesel feedstocks
for the UK: a review
of sustainability
issues with reference
to the UK RTFO

Sugarcane,
at farm
/BR S Sugarcane,
at farm
/BR U

Table 24 (cont’d)
A

1
1

1
1
1

1
1
1

1

B
C
D
E

1

F
G
H

1
1
1

1
1
1

186

I

1

1

J

1

K
L

1

1
1

N/
A
1

N
O

1

1

1

P
Q
R

1
1

1

M

1

1
1

1
1

Production and
energetic
utilization of wood
from short
rotation coppice –
a life cycle
assessment
A
B
C
D

Life cycle
assessment of
Australian sugar
cane production
with a focus on
sugarcane growing
1
1
1
1

A decision support tool for
modifications in crop cultivation
method based on LCA: a case study
on GHG emissions reduction in
Taiwanese sugarcane cultivation

Table 24 (cont’d)

1
1
1

E
F
G
H

1
1
1

1
1
1

187

I

1

L
M

1

J

1
1

1
N/
A
1

K
N
O

1

P
Q

1

R

Life cycle
assessment of
Planting, Switchgrass,
switchgrass
switchgrass, harvested,
derived
2022
wet
ethanol as
transport fuel
Switchgrass,
production,
US, 2022

Environmental
assessment
of black locust
based ethanol as
potential

Table 24 (cont’d)
A

1

1

C
D
E
F
G

1

B

1

H

1

188

I

1

J

1

1

K
L
M
N
O
P
Q

1

1

1

R

D
E
F
G
H
I
J

Wheat grains
conventional,
barrios, at
farm/FR S –
Wheat grains
conventional,
barrios, at
farm/FR U
1
1
1
1
1
1
1

Wheat grains
conventional,
Castilla-y-Leon, at
farm/ES S -Wheat
grains conventional,
Castilla-y-Leon, at
farm/ES U
1
1
1
1
1
1

Wheat grains
conventional, SaxonyAnhalt, at farm/DE S Wheat grains
conventional, SaxonyAnhalt, at farm/DE U
1
1
1
1
1

Wheat grains
extensive, at
farm/CH S –
Wheat grain
extensive,
at farm/CH U

Table 24 (cont’d)
A

1
1
1
1
1

B

1

C
M
N
O

1
1
1

1
1
1

1
1
1

1
1
1

189

K
L
Q
R

1
1
1

1
1
1
1

1
1
1
1
1

1
1
1
1
1

P

wheat
organic

Wheat grains
organic, at
farm/CH S –
Wheat grains
organic, at
farm/CH U
wheat IP

Wheat grains IP,
at farm/CH S Wheat grains IP,
at farm/CH U
wheat
extensive

Table 24 (cont’d)
A

1

1
1

D

1

1

E

1

1

F

1

1

G

1

1

H

1

1

190

1

1

J

1

1

K
L
M

1

1

N

1

1

O

1

1

P

1

1
1

1

1
1

1

I
R

1

1

C
Q

1

B

1

Winter wheat; at
harvest in (year);
at farm; 86-90%
moisture (state)
Wheat
wheat,
conventional,
at
from farm
field

A
B

Wheat grains,
at farm/US S Wheat grains,
at farm/US U

1
1

Wheat
grains,
at field
/kg/US

Table 24 (cont’d)

1

1
1

D
E
F
G
H
I
J

1
1
1
1
1
1
1

1

C

1

1
1
1

191

1

1
1

K

1

L
M

1
1

1
1

1

N
O

1

P
Q
R

1
1

1

1

1
1

Environmental burdens
of producing bread
wheat, oilseed rape,
and potatoes in
England and Wales
using simulation and
system modeling
Wheat,
organic,
From
farm

A
B

Spring wheat;
exclu. Durum; at
harvest in (year);
at farm; 86-90%
moisture (state)

1

Durum wheat; at
harvest in (year);
at farm; 88-89%
moisture (state)

Table 24 (cont’d)

1

F
G
H
I
J

1
1
1
1
1
1

1
1
1
1
1
1

1
1
1

C

1

D
E

192

K
L

1

Q
R

1
1
1

1

M

1
1

1

N

1

O
P

1
1

System delimitation
The impact of
in agricultural
local crops
consequential LCA:
consumption
outline of methodology
on the water
and illustrative case
resources
study of wheat in
in Beijing
Denmark

Table 24 (cont’d)
A
B
C
D
E
F
G
H

193

I
J
K
L
M
N
O
P
Q

1

1

R

Data Country

Inventory refers to the production
of 1 kg sugar, respectively 1 kg
ethanol (95%w/w dry basis, i.e.
1.05kg hydrated ethanol 95% wet
basis). 1 kg excess bagasse, I kWh
electricity and 1 kg vinasse.

transport of sugarcane to the
sugar refinery and the processing
of the sugarcane to sugar,
ethanol, bagasse, excess
electricity and vinasse from
ethanol production. System
boundary is at the sugar refinery.
Treatment of waste effluents is
not included. Packaging is not
included.

China

Brazil

Data Years

Allocation*

Includes

2004-2005

Ecoinvent

bagasse, from
sweet sorghum, at
distillery

100.0%

Bagasse, from
Sugarcane, at Sugar
refinery/BR/ S Bagasse, from
Sugarcane, at Sugar
refinery/BR/ U

Details

Ecoinvent

Name

Data Availability

Table 25 – Processed Agricultural Data Summary

194

195

USA

Bailing, transport

USA

Further processing of corn stover
production average 2022

2022

USLCI

corn stover, carted

Infrastructure impacts are
included in this process by calling
the Ecoinvent "transport"
processes. The production of corn
stover feedstock utilized in this
transport process assigns inputs to
corn stover based on activities
and inputs that are required for
the growth and harvest and
preprocessing of corn stover and
none of the activities or inputs
that would normally be used to
produce, harvest and preprocess
corn grain (so-called "incremental
allocation").(USLCI).

2022

corn stover, at
conversion plant,
2022

"This process transports corn
stover to the conversion plant. It
does so using transportation
modal allocation from the USDA
Ethanol Backgrounder (2007),
assuming those current
allocations are applicable to
stover and for year 2022.
Distances for each mode are from
a combination of references; still
missing a good distance estimate
for barge, but since the share of
barge transportation is ~2%, the
final result will not be sensititive
to this parameter."(USLCI).

USLCI

Table 25 (cont’d)

196

USA

"Corn stover based on US average
corn yeilds and practices
extrapolated from historic data to
"These processes include
2022. Grain and components are
additional harvesting energy for
assumed to be at field-dry
single pass harvest, additional
conditions of 15.5% moisture. The
nutrients, and hay substitition for
stover process has been seperated
loss of potential over-winter cattle
from corn to allow for incremental
feed."(USLCI).
allocation. Only processes directly
attributed to stover collection are
counted."(USLCI).

Direct emissions Sox, machinery,
storage, loading, drying
(electricity), grinding

USA

Corn stover,
production,
average, US, 2022

USLCI

corn stover, ground
and stored

"Taken from Sheehan, Corn Stover
Ethanol LCA. (Directly from
TEAM). The corn steep liquor
production involves the steeping
of harvested corn for a period of
from 24 to 48 hours in a light
sulfurous acid solution. All of the
production burdens from corn are
assumed to be allocated to the
production of the SOx." (USLCI).

USLCI

Table 25 (cont’d)

1995-2006

Malaysia

1 kg rape meal, economic
allocation of 25.7% to meal. CO2
emissions allocation based on
carbon balance.

Transport of rape seeds to the
mill, and the processing of the
seeds to rape oil and rape meal.
Cold press oil extraction
technique. System boundary at oil
mill.

1998-2006

Switzerland

197

Switzerland

1.4%

1 kg palm kernel meal, from palm
fruit bunches. 1.4% allocated to
meal. CO2 emissions allocated
based on carbon balance.

extraction of palm kernel meal,
energy supply from extracted
solids, and treatment of specific
wastwater effluents are taken into
account. System boundary is at
the oil mill. (Sterilization,
stripping, digestion, oil extraction,
screening, settling and refining).

1998-2005

Transport of sugar beets to the
sugar refinery, and the processing
of sugar beets to sugar, molasses
and pulps. System boundary is at
the sugar refinery. Treatment of
waste effluents is included.
Packaging of sugar is not included.

3.8%

1 kg sugar, 1 kg molasses andd 1
kg of pulps. Economic allocation
with allocation factor for common
stages of 91.7% to sugar, 4.5% to
molasses, and 3.8% to pulps.
Allocation is done according to
carbon balance for CO2 emissions.

25.7%

Rape meal, at oil
mill/CH S - Rape
meal, at oil mill/CH
U

Ecoinvent

Palm kernel meal,
at oil mill/MY S Palm kernel meal,
at oil mill/MY U

Ecoinvent

Pulps, from sugar
beet, at sugar
refinery/CH S Pulps, from sugar
beet, at sugar
refinery/CH U

Ecoinvent

Table 25 (cont’d)

Brazil

Fuctional unit is 1 kg of soybean
meal produced in argentina and
delivered to the netherlands.
Global warming, ozone depletion
acidification, eurtrophication and
photochemical oxidation
reported. Two scenarios reported
for allocation: economic and
mass.

fertilizer, diesel, electric,
transport, credit for avoided
products

Argentina / Netherlands

198

Europe

1 kg soybean meal (incl. hulls).
Economic allocation of 59.3% to
meal. CO2 allocated based on
carbon balance.

Transport of soybean to the mill,
processing of soybeans into meal
and oil by the solvent method
(pre-cracking, de-hulling, oil
extraction, meal processing and
oil purification).

1996-2003

Transport of rape seeds to the
mill, and the processing of the
seeds to rape oil and rape meal.
Cold press oil extraction
technique. System boundary at oil
mill.

1998-2005

25.7%
59.3%

1 kg rape meal, economic
allocation of 25.7% to meal. CO2
emissions allocation based on
carbon balance.

2008

LCA of soybean
meal

Ecoinvent

Soybean meal, at
oil mill/BR S Soybean meal, at
oil mill/BR U

Ecoinvent

Rape meal, at oil
mill/RER S - Rape
meal, at oil
mill/RER U

Ecoinvent

Table 25 (cont’d)

199

North America

transport of soybean to the mill,
processing of soybeans into meal
and oil by the solvent method
(pre-cracking, dehulling, oil
extraction, meal processing and
oil purification).

USA

1 kg soybean meal (incl. hulls).
Economic allocation of 65.5% to
meal. CO2 allocated based on
carbon balance.

2022

USLCI

65.5%

Soybean meal, at
oil mill/US S Soybean meal, at
oil mill/US U

Ecoinvent

wheat straw, at
conversion plant,
2022

"This process transports wheat
straw to the conversion plant. It
does so using transportation
modal allocation from the USDA
Ethanol Backgrounder (2007),
assuming the current corn grain
allocations described in the report
are applicable to wheat straw and
to year 2022. Distances for each
mode are from a combination of
references; still missing a good
distance estimate for barge, but
since the share of barge
transportation is ~2%, the final
result will not be sensitive to this
parameter." (USLCI).

1998-2005

Table 25 (cont’d)

Collection of waste vegetable oil
and deliver to the treatment
Treated vegetable oil consists of
plant, treatment for impurities
93.7% triglycerides and 6.7% fatty
and water removal, conditioning
acid methyl ester. Process refers
and storage of the oil. Treatment
to the acid-catalyzed esterification
of effluents is taken into account.
of free fatty acids and includes
Includes gross calorific value of
water removal, glycerin washing
the biomass and the carbon
and methanol recovery.
dioxide credit. System boundary is
at the oil refining facility.

200

Germany, India, Malaysia

Crop production, oil extraction,
combustion.

2011

Cradle to grave study. Includes
from the production of the
vegetable oil to its combustion.
Process contribution for each
stage is available. Reports: fossil
fuels, GWP, acidification, and
eutrophication. Data for Rape,
palm oil, and Jatropha

China

Vegetable oil, from
cooking oil, at
plant/CH S Vegetable oil, from
cooking oil, at
plant/CH U

Ecoinvent

Life cycle
assessment of
hydrotreated
vegetable oil from
rape, palm oil, and
Jatropha

Journal of cleaner
production vol 19 Iss 2-3

Table 25 (cont’d)

France

includes transport from
slaughterhouse to rendering plant
and further processing of
slaughterhouse wastes to tallow.
Energy demand for operation a
rending plant (electricity and
natural gas, use of tap water,
output of waste water and
building infrastructure) is
included. Upstream processes not
included as tallow material is
considered as waste.

Switzerland

100.00%

Tallow, at plant/CH
S - Tallow, at
plant/CH U

Ecoinvent

Vegetable oil, from
cooking oil, at
plant/FR S Vegetable oil, from
cooking oil, at
plant/FR U

Treated vegetable oil consists of
Collection of waste vegetable oil
93.7% triglycerides and 6.7% fatty
and deliver to the treatment
acid methyl ester. Process refers
plant, treatment for impurities
to the acid-catalyzed esterification and water removal, conditioning
of free fatty acids and includes
and storage of the oil. Treatment
water removal, glycerin washing
of effluents is taken into account.
and methanol recovery.
Includes gross calorific value of
C57H102O6. Data is mostly from
the biomass and the carbon
one US literature sources and
dioxide credit. System boundary is
adapted to FR.
at the oil refining facility.

Ecoinvent

Table 25 (cont’d)

1 kg tallow at rending plant. Since
the processes of making tallow
and meat and bone meal are very
similar, this process may also be
used to approximate the
production of meat and bone
meal.

201

Philippines
North
America

Malaysia

North
America
202

1995-2006

100.0%

Palm kernel oil,
processed, at
plant/RNA

USLCI

Palm Kernel Oil, at
oil mill/MY S - Palm
Kernel Oil, at oil
mill/MY U

Ecoinvent

Crude palm kernel
oil, at plant/RNA

100.0%
17.3%

Palm kernel oil, processed, at
plant. Physical refining using
steam distillation in high
temperature vacuum.

Ecoinvent

Production of 1 kg palm kernel oil.
17.3% allocated to palm kernel oil.
CO2 emissions based on carbon
balance

Extracting of palm kernel oil from
palm fruit bunches, energy supply
from extracted solids, treatment
of specific wastewater effluents
are included. System boundary is
at the oil mill.

Crude coconut oil,
at plant/PH S Crude coconut oil,
at plant/PH U

USLCI

Data based on the ECOSOL study
of the European surfactant
industry. Allocation based on
mass of outputs.

Material and energy input,
production of waste and
emissions for the steps from the
harvested coconuts to the crude
coconut oil (Halving, extraction of
water, meat removal and shell
production, drying and oil
extraction). Water consumption
and infrastructure are estimated.

100.0%

Table 25 (cont’d)

Table 25 (cont’d)

cultivation, oil milling, refining

203

Malaysia

Consequential LCA that used
system expansion to avoid
allocation. Rapeseed data is for
Denmark, Palm oil data is from
Malaysia and Indonesia

Denmark, Malaysia,
Indonesia

Production of 1 kg palm l oil.
81.3% allocated to palm oil. CO2
emissions based on carbon
balance

Extracting of palm oil from palm
fruit bunches, energy supply from
extracted solids, treatment of
specific wastewater effluents are
included. System boundary is at
the oil mill.

Malaysia

Waste, harvesting, transport,
incineration of waste wood.

1995-2006

Comparative LCA
of rapeseed oil and
palm oil

81.3%

Ecoinvent

USLCI

Palm oil, at oil
mill/MY S - Palm
oil, at oil mill/MY U

International
Journal of LCA
February 2010

Palm kernels, at
plant

"Bunch ash, crude palm oil, and
shells used in road construction
have been treated as coproducts,
for which credit has been given
on a mass basis. Mass imbalance
is due to unavailability of the
weight of empty fruit bunches
sent to incineration" (USLCI).

1996-2003

Distribution of 1 kg rape oil in
Switzerland

Transport of rape oil from the oil
mill to the end user, including
operation of storage tanks and
equipment. Emissions from
evaporation and treatment of
effluents.

204

Switzerland

Transport of seeds to mill,
processing of seeds to rape oil
and rape meal. The oil extraction
refers to the solvent extraction
method. System boundary at oil
mill.

Europe

1998-2006

74.3%

1 kg rape oil. Allocation is
economic with 74.3% to rape oil.
CO2 is allocated based on carbon
balance.

Switzerland

74.3%

1 kg rape oil. Allocation is
economic with 74.3% to rape oil.
CO2 is allocated based on carbon
balance.

Transport of seeds to mill,
processing of seeds to rape oil
and rape meal. The oil extraction
refers to the cold-press extraction
method. System boundary at oil
mill.

100.0%

Rape oil, at
regional
storage/CH S Rape oil, at
regional
storage/CH U

Ecoinvent

Rape oil, at oil
mill/RER S - Rape
oil, at oil mill/RER
U

Ecoinvent

Rape oil, at oil
mill/CH S - Rape
oil, at oil mill/CH U

Ecoinvent

Table 25 (cont’d)

1 kg soybean oil, system
boundary at oil mill. Economic
allocation of 40.7% to oil. CO2
allocated based on carbon
balance.

Transport of soybeans to the mill,
processing in to soybean oil.
Solvent extraction process (Precracking of beans, de-hulling, oil
extraction, meal processing and
oil purification).

Brazil

"Energy data: late 2000s, other
data: theoretical. CARBON
SEQUESTRATION should be
accounted for after the product is
built in its LCA model, and should
be included depending on the use
of end of life fate of that product"
(USLCI).

Waste disposal, process energy,
transport, materials

USA

205

Europe

28.0%

Manufacturing process starting
with dry beans. 28% allocation to
Soya Oil.

The inventory includes the
conditioning (but not drying) of
the beans before extraction. The
production of soya scrap is also
included as well as the
consumption of auxiliaries,
energy, infrastructure and land
use, generation of emissions to
land and water. Generation and
transportation of solid waste is
not included.

40.7%

Soy oil, refined, at
plant

Ecoinvent

Soybean oil, at oil
mill/BR S - Soybean
oil, at oil mill/BR U

USLCI

Soya oil, at
plant/RER S - Soya
oil, at plant/RER U

Ecoinvent

Table 25 (cont’d)

206

2011

Cultivation of seedlings,
cultivation of palms, land use,
milling, refining, Trans
esterification, and utilization of
diesel in engines of vehicles

USA

Transport of soybeans to the mill,
processing in to soybean oil.
Solvent extraction process (Precracking of beans, de-hulling, oil
extraction, meal processing and
oil purification).

Primary data collected from 12
palm nurseries, 102 plantations,
12 mills and 11 refineries.

North America

Ecoinvent

Determination of
GHG contributions
by subsystems in
the oil palm supply
chain using the LCA
approach

Materials, transportation, water,
waste disposal, natural gas,
electricity

Malaysia

Soybean oil, at oil
mill/US S - Soybean
oil, at oil mill/US U

1 kg soybean oil, system
boundary at oil mill. Economic
allocation of 34.5% to oil. CO2
allocated based on carbon
balance.

Soybean oil, crude,
degummed, at
plant

34.5%

USLCI

"CARBON SEQUESTRATION
should be accounted for after the
product is built in its LCA model,
and should be included
depending on the use of end of
life fate of that product. For
example, a soy-based resin may
retain the sequestered carbon
indefinitely, while a soy-based
biodiesel releases the
sequestered carbon at use
phase." (USLCI).

International Journal of
Life Cycle Assessment
August 2011

Table 25 (cont’d)

207

New Zealand

Credit for removed CO2

Switzerland

Process is only a credit entry
accounting for the extraction of
CO2 from the atmosphere. 4.9%
lactose, 0.5% lipids

2011

Farming processes: raising cattle,
fuel and electric for farm
activities, nitrogen and
phosphate fertilizer for cattle
feed. Meat Processing:
slaughtering, blood collection,
removing of offal, cutting of
meat, rendering, steam
coagulation, drying blood, milling
into blood meal. Other processes
included in Part 2 of the paper.

100.00%

Whey, at dairy/CH
S - Whey, at
dairy/CH U

International Journal of Life Cycle
Assessment - Bier et all.

An eco-profile of
thermoplastic
protein derived
from blood meal

LCA data for making NTP
(Novatein Thermoplastic Protein)
from blood meal. Only nonrenewable energy and GHG
emissions are reported. NTP has
applications similar to LLDPE.
New Zealand has higher than
average wind energy use, other
locations would have more nonrenewable energy reported.

Ecoinvent

Table 25 (cont’d)

2022

USA

Functional unit is 1 kg of glucose
or fructose for fermentation. The
three things that were found to
have the greatest effect on
environmental performance
were: commodities displaced by
bio-products, agricultural yields,
and nitrogen use efficiency.

USA, UK, and Australia

USLCI

an environmental
life cycle
assessment
comparing
Australian
sugarcane with US
corn and UK sugar
beet as producers
of sugar for
Fermentation

This process transports corn grain
to the conversion plant. It does
so using transportation modal
Infrastructure impacts are
allocation from the USDA Ethanol included in this process by calling
Backgrounder (2007), assuming
the Ecoinvent "transport"
this current allocation will apply processes. The production of corn
in 2022. Distances for each mode
grain feedstock utilized in this
are from a combination of
transport process allocates inputs
references; still missing a good
to the stover and grain based on
distance estimate for barge, but
the amount of ethanol that can
since the share of barge
be produced from each cotransportation is ~2%, the final
product
result will not be sensitive to this
parameter.

2008

corn grain, at
conversion plant,
2022

Biomass and Bioenergy
Vol 32 (2008)

Table 25 (cont’d)

208

Fertilizer, lime, pesticide, water
for irrigation, harvesting, milling,
bagasse combustion (for
sugarcane only), electricity,
transport, machinery, fuel use,
clarification.

Germany

Transport of sugar beets to the
sugar refinery, and the processing
of sugar beets to sugar, molasses
and pulps. System boundary is at
the sugar refinery. Treatment of
waste effluents is included.
Packaging of sugar is not
included.

Denmark

100.0%
4.5%

Molasses, from
sugar beet, at
sugar refinery/CH S
- Molasses, from
sugar beet, at
sugar refinery/CH
U

USA

Washing of potatoes, chopping,
separation of potato fruit water,
second washing, refining, starch
drying. Process water included,
infrastructure use considered.

Switzerland

100.0%

Germany

Ventilated storage, transport,
loading, conveyor belt

2022

USLCI

Potato starch /
potato flour

Ecoinvent

Potato starch, at
plant/DE S - Potato
starch, at plant/DE
U

Ecoinvent

Maize starch, at
plant/DE S - Maize
starch, at plant/DE
U

Further processing of corn grain
for 2022

LCA Food
DK

corn grain,
harvested and
stored

Ecoinvent

Table 25 (cont’d)

1 kg sugar, 1 kg molasses and 1
kg of pulps. Economic allocation
with allocation factor for
common stages of 91.7% to
sugar, 4.5% to molasses, and
3.8% to pulps. Allocation is done
according to carbon balance for
CO2 emissions.

209

USA, UK, and Australia
Thailand

Reports estimated GHG
emissions from CO2, CH4, and
N2O.

Tilling, irrigation, herbicide and
pesticide application, diesel,
fertilizer, biomass burning,
transport, energy use and waste
water treatment.

Brazil

Journal of cleaner
production vol 19

Sugarcane as a
Carbon Source:
The Brazilian Case

Fertilizer, lime, pesticide, water
for irrigation, harvesting, milling,
bagasse combustion (for
sugarcane only), electricity,
transport, machinery, fuel use,
clarification.

2008

Biomass and Bioenergy
Vol 32 (2008)

Carbon footprint of
sugar produced
from sugarcane in
eastern Thailand

Functional unit is 1 kg of glucose
or fructose for fermentation. The
three things that were found to
have the greatest effect on
environmental performance
were: commodities displaced by
bio-products, agricultural yields,
and nitrogen use efficiency.

2011

an environmental
life cycle
assessment
comparing
Australian
sugarcane with US
corn and UK sugar
beet as producers
of sugar for
Fermentation

Journal of
Biomass and
Bioenergy

Table 25 (cont’d)

210

Life cycle
assessment of
Australian sugar
cane with a focus
on cane processing

International Journal of
Lice Cycle Assessment
Sept 2010

The multi-output process
"sugarcane, in sugar refinery"
delivers the co-products: Sugar,
ethanol 95% in H2O, sugarcane
molasses, bagasse, electricity,
and vinasse. Economic allocation
with 80-85% to sugar and 10-11%
to ethanol. Allocation according
to carbon balance for CO2.

Australia

sugarcane, in sugar
refinery

GaBi PE

Table 25 (cont’d)

211

switchgrass,
carted, 2022

Crop Production - unspecified

212

Fertilizer, lime, pesticide, water
for irrigation, harvesting, milling,
bagasse combustion (for
sugarcane only), electricity,
transport, machinery, fuel use,
clarification.

USA, UK, and Australia

Biomass and Bioenergy
Vol 32 (2008)

Functional unit is 1 kg of glucose
or fructose for fermentation. The
three things that were found to
have the greatest effect on
environmental performance
were: commodities displaced by
bio-products, agricultural yields,
and nitrogen use efficiency.

2008

an environmental
life cycle
assessment
comparing
Australian
sugarcane with US
corn and UK sugar
beet as producers
of sugar for
Fermentation

USLCI

Table 25 (cont’d)

213

USA
USA

USLCI

Infrastructure impacts are
included in this process by calling
the Ecoinvent "transport"
processes.

2022

Switchgrass,
ground and stored,
2022

This process transports
switchgrass to the conversion
plant using transportation modal
allocation from the USDA Ethanol
Background (2007), assuming
these current corn grain
allocations are applicable to
switchgrass and to year 2022.
Distances are from a combination
of references; still missing a good
distance estimate for barge, but
since the share of barge
transportation is ~2%, the final
result will not be sensitive to this
parameter.

2022

Switchgrass, at
conversion plant,
2022

USLCI

Table 25 (cont’d)

2011

General

Algae harvesting, harvesting of
Chitin and processing into
Chitosan, Al and Fe extraction
and processing, machine
manufacturing and operation,
treatment of wastewater and
solid waste. All energy and
materials inputs are included, but
cultivation of algae is not.

General

This was a lab scale study and
only energy consumption was
reported, includes cleaning of the
filters. Study compares filters
with different pore sizes.

This article includes LCA data for
the harvesting and processing of
algae. It uses the TRACI model of
impacts.

*Allocation based on economic value unless noted otherwise

214

General

Algae cultivation, harvesting, dewatering, lipid extraction,
conversion into bio-diesel, byproduct management.

2012

Preferential
technological and
lice cycle
environmental
performance of
chitosan
flocculation for
harvesting of the
green algae
Neochloris
oleoabundans

All stages of production are
considered separately and
multiple methods are included
for each process.

2012

Harvesting
microalgal biomass
using submerged
microfiltration
membranes

Biosource Technology 2012

Combinational Life
Cycle Assessment
to inform Process
Design of Industrial
Production of Algal
Biodiesel

Bioresource
Technology
Environmental Science
volume 111 May and Technology 2011
2012

Table 25 (cont’d)

Land Use

Material

Transport

Waste Treatment

Infrastructure

Machines

Process Energy

De-Hulling

Digestion

Stripping

Extraction

Screening

Refining

Water

Drying

Direct Emissions

Harvesting

A

Name

Loading

Table 26 - Processed Agricultural Data Summary Inputs

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

Bagasse, from
Sugarcane, at Sugar
refinery/BR/ S Bagasse, from
Sugarcane, at Sugar
refinery/BR/ U

1

1

bagasse, from sweet
sorghum, at distillery

corn stover, at
conversion plant, 2022

corn stover, carted

1

1

1

1

1

215

Table 26 (cont’d)
A
corn stover, ground
and stored

Corn stover,
production, average,
US, 2022

Pulps, from sugar
beet, at sugar
refinery/CH S - Pulps,
from sugar beet, at
sugar refinery/CH U

Palm kernel meal, at
oil mill/MY S - Palm
kernel meal, at oil
mill/MY U

B

C

D

E

F

1

1

1

H

1

1

G

1

I

J

K

L

M

N

1

1

1

1

1

216

1

1

1

1

1

P

Q

1

1

O

1

R

Table 26 (cont’d)
A

B

C

D

E

F

G

H

I

J

K

L

Rape meal, at oil
mill/CH S - Rape meal,
at oil mill/CH U

1

1

1

1

1

Soybean meal, at oil
mill/BR S - Soybean
meal, at oil mill/BR U

1

N

1

Rape meal, at oil
mill/RER S - Rape
meal, at oil mill/RER U

M

LCA of soybean meal

1

1

1

1

1

1

wheat straw, at
conversion plant, 2022
Soybean meal, at oil
mill/US S - Soybean
meal, at oil mill/US U

1

1

217

1

O

P

Q

R

Table 26 (cont’d)
A
Life cycle assessment
of hydrotreated
vegetable oil from
rape, palm oil, and
Jatropha

B

C

D

E

F

G

1

H

I

J

K

L

M

N

O

P

1

Vegetable oil, from
cooking oil, at
plant/CH S - Vegetable
oil, from cooking oil,
at plant/CH U

1

1

1

1

Vegetable oil, from
cooking oil, at
plant/FR S - Vegetable
oil, from cooking oil,
at plant/FR U

1

1

1

1

Tallow, at plant/CH S Tallow, at plant/CH U

1

1

1

1

218

1

Q

R

Table 26 (cont’d)
A
Crude coconut oil, at
plant/PH S - Crude
coconut oil, at
plant/PH U

B

C

D

F

1

1

E

G

1

H

I

J

K

L

N

O

P

1

1

M

Q

R

1

Crude palm kernel oil,
at plant/RNA

Palm Kernel Oil, at oil
mill/MY S - Palm
Kernel Oil, at oil
mill/MY U

1

1

1

Palm kernel oil,
processed, at
plant/RNA
Palm kernels, at plant
Palm oil, at oil mill/MY
S - Palm oil, at oil
mill/MY U

1

1

1

1

1

1

219

1

Table 26 (cont’d)
A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

Comparative LCA of
rapeseed oil and palm
oil
Rape oil, at oil mill/CH
S - Rape oil, at oil
mill/CH U

1

1

1

Rape oil, at oil
mill/RER S - Rape oil,
at oil mill/RER U

1

1

1

Rape oil, at regional
storage/CH S - Rape
oil, at regional
storage/CH U

1

Soya oil, at plant/RER
S - Soya oil, at
plant/RER U

1

Soybean oil, at oil
mill/BR S - Soybean
oil, at oil mill/BR U

1

1

1

1

1

1

220

1

1

1

1

1

R

Table 26 (cont’d)
A

B

C

D

E

Soy oil, refined, at
plant

1

1

1

1

Soybean oil, crude,
degummed, at plant

1

1

1

1

Soybean oil, at oil
mill/US S - Soybean
oil, at oil mill/US U

1

1

1

H

1

An eco-profile of
thermoplastic protein
derived from blood
meal

G

1

Determination of GHG
contributions by
subsystems in the oil
palm supply chain
using the LCA
approach

F

1

I

J

K

L

M

N

221

P

Q

R

1

1

1

1

1

1

O

1

1

1

Table 26 (cont’d)
A

B

C

D

an environmental life
cycle assessment
comparing Australian
sugarcane with US
corn and UK sugar
beet as producers of
sugar for
Fermentation

1

corn grain, harvested
and stored

1

E

F

G

H

1

1

1

1

1

1

I

J

K

L

M

N

O

1

1

P

Q

R

Whey, at dairy/CH S Whey, at dairy/CH U

corn grain, at
conversion plant, 2022

Maize starch, at
plant/DE S - Maize
starch, at plant/DE U

222

1

1

Table 26 (cont’d)
A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

1

1

P

Q

R

Potato starch, at
plant/DE S - Potato
starch, at plant/DE U
Potato starch / potato
flour
Molasses, from sugar
beet, at sugar
refinery/CH S Molasses, from sugar
beet, at sugar
refinery/CH U
an environmental life
cycle assessment
comparing Australian
sugarcane with US
corn and UK sugar
beet as producers of
sugar for
Fermentation

1

1

1

1

1

1

1

223

1

1

Table 26 (cont’d)
A
Carbon footprint of
sugar produced from
sugarcane in eastern
Thailand

B

C

D

E

1

1

F

G

1

H

I

J

K

L

M

N

O

Sugarcane as a Carbon
Source: The Brazilian
Case

sugarcane, in sugar
refinery

1

Life cycle assessment
of Australian sugar
cane with a focus on
cane processing

224

1

Q

R

1

1

P

1

Table 26 (cont’d)
A

B

D

1

an environmental life
cycle assessment
comparing Australian
sugarcane with US
corn and UK sugar
beet as producers of
sugar for
Fermentation

C

E

1

F

G

H

1

I

J

1

K

L

1

M

N

O

1

P

1

Q

R

1

switchgrass, carted,
2022
Switchgrass, at
conversion plant, 2022
Switchgrass, ground
and stored, 2022
Combinational Life
Cycle Assessment to
inform Process Design
of Industrial
Production of Algal
Biodiesel

1

1

1

1

225

1

1

1

Table 26 (cont’d)
A

B

C

D

E

F

G

H

1

1

I

J

K

L

M

N

O

P

Q

R

Harvesting microalgal
biomass using
submerged
microfiltration
membranes
Preferential
technological and lice
cycle environmental
performance of
chitosan flocculation
for harvesting of the
green algae Neochloris
Oleoabundans

1

1

226

1

1

Transportation for the
collection as well as the
further transportation to
the next paper production
site. Nothing else included.

Switzerland

Collected waste paper has
a biogenic C content of
40.4 % (Average 2004)

Transportation for the
collection as well as the
further transportation to
the next paper production
site. Nothing else included.

Europe

Data Country

Collected waste paper has
a biogenic C content of
40.4 % (Average 2004)

227

Data Years

Allocation*

Ecoinvent

Waste paper,
mixed, from public
collection, for
further
treatment/RER S Waste paper,
mixed, from public
collection, for
further
treatment/RER U

Includes

100.0%

Data Availability

Waste paper,
mixed, from public
collection, for
further
treatment/CH S Waste paper,
mixed, from public
collection, for
further
treatment/CH U

Details

100.0%

Name

Ecoinvent

Table 27 – Wood Data Summary

Switzerland
Europe
USA

228

US Inland West

100.0%
100.0%

USLCI

Bark, softwood,
average, state or
private moist cold
forest, at forest
road, INW

Collected waste paper

Includes sorting of
collected paper (energy
and materials), treatment
of extracted wastes, and
transportation to the next
paper production site.
Nothing else included.

1989-1996

Forest residue,
processed and
loaded, at landing
system

Collected waste paper

Includes sorting of
collected paper (energy
and materials), treatment
of extracted wastes, and
transportation to the next
paper production site.
Nothing else included.

USLCI

Waste paper,
sorted, for further
treatment/RER S Waste paper,
sorted, for further
treatment/RER U

Ecoinvent

Waste paper,
sorted, for further
treatment/CH S Waste paper,
sorted, for further
treatment/CH U

Ecoinvent

Table 27 (cont’d)

3.3%

Germany / Central Europe

100.0%

Bark mulch, at
oriented strand
board production,
US SE/kg/US

transports from forest,
sawing, and debarking at
sawmill

Germany /
Central Europe

Ecoinvent

Volume refers to the wood
not including the bark.
Allocation is based on
economic value.

US South
East

100.0%

Bark chips,
softwood, u=140%,
at plant/RER S Bark chips,
softwood, u=140%,
at plant/RER U

Bark chips,
softwood, u=140%,
at forest road/RER S
- Bark chips,
softwood, u=140%,
at forest road/RER
U

Ecoinvent

Tractor driven debarking
delivers the two
coproducts "round wood,
softwood, debarked,
u=70% at forest road" and
"bark chips, softwood,
u=140%, at forest road".
Allocation based on the
overall proceeds of the
process. Data for Germany
used for central Europe.

USLCI

Table 27 (cont’d)

229

US South
East

Debarking of logs, sawing
and sorting of green
lumber

US Pacific
North West

US South
East

Log debarking at plant
yielding wood and bark,
bark used to fire boiler.

230

US Pacific
North West

13.0%
5.6%

USLCI

Log debarking at plant
yielding wood and bark,
bark used to fire boiler.

6.3%

Bark, at rough
green lumber
sawmill, softwood,
US PNW/kg/US

Debarking of logs, sawing
and sorting of green
lumber

7.7%

Bark, at plywood
plant, US SE/kg/US

USLCI

Bark, at plywood
plant, US
PNW/kg/US

USLCI

Bark, at sawmill, US
SE/kg/US

USLCI

Table 27 (cont’d)

Co-products of
laminated veneer
lumber production,
unspecified, US
PNW/kg/US

4.4%

Allocation based on mass
or volume

USLCI

Co-products of glue
laminated beam
production, at
plant, unspecified,
US SE/kg/US

231

Sweden and Finland
US Pacific
North West
US South East
US Pacific
North West

100.0%

Allocation based on mass
or volume

Final manufacture of
1000cuft of LVL from
plywood and dry veneer
(and coproducts)

18.3%

Final manufacture of
1000cuft of Glulam beams
from dry and rough green
lumber (and coproducts)

Allocation based on mass
or volume

17.6%

Final manufacture of
1000cuft of Glulam beams
from dry and rough green
lumber (and coproducts)

Co-products of glue
laminated beam
production, at
plant, unspecified,
US PNW/kg/US

USLCI

Allocation based on
economic criteria.

Debarking and further
production of sawn
timber, wood chips and
sawdust. Transport of
wood from forest road to
sawmill is included.

USLCI

Chips, Scandinavian
softwood (plantdebarked, u=70%,
at plant/NORDEL S Chips, Scandinavian
softwood (plantdebarked, u=70%,
at plant/NORDEL U

Ecoinvent

Table 27 (cont’d)

Trim and saw process, at
plywood plant. Allocation
based on mass or volume.

Trim and saw process, at
plywood plant. Allocation
based on mass or volume.

232

US Pacific North West US South East US South East US South East

Gate to gate system
analysis. Allocation based
on mass or volume

Final manufacture of
1000cuft of LVL from
plywood and dry veneer
(and coproducts)

US South
East

8.7%
0.7%
1.4%

USLCI

Gate to gate system
analysis. Allocation based
on mass or volume

11.6%

Hogfuel, from
trimsaw, plywood
plant, US SE/kg/US

Allocation based on mass
or volume

4.4%

Hogfuel, from trim
and saw at plywood
plant, US
PNW/kg/US

USLCI

Fines, at oriented
strand board
production, US
SE/kg/US

USLCI

Dust and scrap, at
oriented strand
board production,
US SE/kg/US

USLCI

Co-products of
laminated veneer
lumber production,
unspecified, US
SE/kg/US

USLCI

Table 27 (cont’d)

US South
East

Rotary peeling of logs to
produce green veneer.
Allocation performed
using mass or volume.

US Pacific
North West

Rotary peeling of logs to
produce green veneer.
Allocation performed
using mass or volume.

US South East

US Pacific
North West

9.0%
5.2%

USLCI

Final trim and saw to
length of plywood.
Allocation based on mass
or volume.

5.3%

Peeler core, from
green veneer
production at
plywood plant, US
SE/kg/US

Final trim and saw to
length of plywood.
Allocation based on mass
or volume.

7.5%

Peeler core, from
green veneer
production at
plywood plant, US
PNW/kg/US

USLCI

Panel trim, from
trim and saw at
plywood plant, US
SE/kg/US

USLCI

Panel trim, from
trim and saw at
plywood plant, US
PNW/kg/US

USLCI

Table 27 (cont’d)

233

Allocation based on mass
or volume.

Planed green lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

Allocation based on mass
or volume.

Rough green lumber
processing, at sawmill.
Debarking of logs, sawing
and sorting of green
lumber.

234

US Pacific
North West

US South
East

Allocation based on mass
or volume.

Planed dried lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

US Pacific
North West

13.1%
7.0%
7.0%

USLCI

Allocation based on mass
or volume

Planed dried lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

US Pacific North
West

Pulp chips, at rough
green lumber
production, US
PNW/kg/US

26.8%

Planer shavings,
from green lumber,
at planer mill, US
PNW/kg/US

USLCI

Planer shavings,
from dried lumber,
at planer mill, US
PNW/kg/US

USLCI

Planer shavings, at
planer mill, US
SE/kg/US

USLCI

Table 27 (cont’d)

Allocation based on mass
or volume.

Pulp chips, from
green veneer
production at
plywood plant, US
PNW/kg/US

Allocation based on mass
or volume.

235

US Pacific
North West

US South
East
US Pacific
North West
US Pacific
North West

31.5%
6.0%

Rotary peeling of logs to
produce green veneer.

6.0%

Planed green lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

Pulp chips, from
green lumber, at
planer mill, US
PNW/kg/US

23.8%

Allocation based on mass
or volume.

Planed dried lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

USLCI

Allocation based on mass
or volume.

Rough green lumber
processing, at sawmill.
Debarking of logs, sawing
and sorting of green
lumber.

USLCI

Pulp chips, from
dried lumber, at
planer mill, US
PNW/kg/US

USLCI

Pulp chips, at
sawmill, US
SE/kg/US

USLCI

Table 27 (cont’d)

Allocation based on mass
or volume.

Planed dried lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

US South
East

US South
East

Allocation based on mass
or volume.

Final manufacture of 100
linear feet of generic Ijoists from LVL and OSB
inputs

Allocation based on mass
or volume.

Rough green lumber
processing, at sawmill.
Debarking of logs, sawing
and sorting of green
lumber.

Allocation based on mass
or volume.

Rough green lumber
processing, at sawmill.
Debarking of logs, sawing
and sorting of green
lumber.

236

US South
East

Rotary peeling of logs to
produce green veneer.

US Pacific
NorthWest

56.1%
7.0%
1.9%
8.6%

USLCI

Allocation based on mass
or volume.

US South
East

Sawdust, at
sawmill, US
SE/kg/US

5.9%

Sawdust, at rough
green lumber
production, us
PNW/kg/US

USLCI

Sawdust, at planer
mill,US SE/kg/US

USLCI

Sawdust from I-Joist
processing, at plant,
US SE/kg/US

USLCI

Pulp chips, from
green veneer
production at
plywood plant, US
SE/kg/US

USLCI

Table 27 (cont’d)

Planed dried lumber
processing, at planer mill.
Transfer, de-sticking of KD
lumber, planing, sorting
and stacking.

1.0%

Processing of 1000 board
feet of surfaced, green
softwood lumber.
Allocation based on mass
or volume.

Planed green lumber
processing, at planer mill.
Transfer, planing, sorting
and stacking.

US Pacific
North West

Allocation based on mass
or volume.

Final manufacture of 100
linear feet of generic Ijoists from LVL and OSB
inputs

US Pacific
North West

237

US Pacific
North West

1.0%

Processing of 1000 board
feet of surfaced, kiln dried
softwood lumber.
Allocation based on mass
or volume.

10.2%

Sawdust, from IJoist processing, at
plant, US
PNW/kg/US

USLCI

Sawdust, from
green lumber, at
planer mill, US
PNW/kg/US

USLCI

Sawdust, from
dried lumber, at
planer mill, US
PNW/kg/US

USLCI

Table 27 (cont’d)

0.8%

US Pacific
North West
US South
East

Economic allocation

Debarking and further
production of sawn
timber, wood chips and
sawdust. Transport of
wood from forest road to
sawmill is included.

Sweden and Finland

100% economic allocation

Final trim and saw to
length of plywood

USLCI

Allocation based on mass
or volume.

Sawdust, from trim
and saw, plywood
plant, US SE/kg/US

USLCI

Final trim and saw to
length of plywood

Sawdust, from trim
and saw at plywood
plant, US
PNW/kg/US

Ecoinvent

Allocation based on mass
or volume.

0.4%

Table 27 (cont’d)

Sawdust,
Scandinavian
softwood (plantdebarked), u=70%,
at plant/NORDEL S Sawdust,
Scandinavian
softwood (plantdebarked), u=70%,
at plant/NORDEL U

238

28% hard wood, 72% soft
wood bulked volume.
Density 239-169 kg/m3
dry mass.

Transport of waste urban
and demolition wood to
the chopping facility
(50km), infrastructure,
chopping of the wood into
chips in sawmill,
consumption of water and
the disposal of effluents
and wastes from sorting.
Includes carbon dioxide
credit. No specific
treatment of the wood is
considered.

Austria and Switzerland
/ Central Europe

Bulked volume dried
matter content 239
kg/m3.

Chopping of residual
hardwood with a
stationary chopper in the
sawmill. No transports for
the inputs are assumed.

Austria /
Central Europe

100.0%
100.0%

Wood chips,
hardwood, from
industry, u=40%, at
plant/RER S - Wood
chips, hardwood,
from industry,
u=40%, at
plant/RER U

Ecoinvent

Waste wood chips,
mixed, from
industry, u=40%, at
plant/CH S - Waste
wood chips, mixed,
from industry,
u=40%, at plant/CH
U

Ecoinvent

Table 27 (cont’d)

239

100.0%

Austria /
Central Europe

Average mix 72%
softwood and 28%
hardwood (Swiss average).

Chopping residual wood
with stationary chopper in
the sawmill. No transports
are included.

Austria and Switzerland
/ Central Europe

Ecoinvent

Wood chips, mixed,
from industry,
u=40%, at
plant/RER S - Wood
chips, mixed, from
industry, u=40%, at
plant/RER U

Bulked volume dried
matter content 239
kg/m3.

Chopping of residual
hardwood with a mobile
chopping in the forest.
Also includes the driving of
the mobile chopper to and
within the forest.

100.0%

Wood chips,
hardwood, u=80%,
at forest/RER S Wood chips,
hardwood, u=80%,
at forest/RER U

Ecoinvent

Table 27 (cont’d)

240

100.0%

Austria and Switzerland
/ Central Europe

Bulked volume dried
matter content 169
kg/m3.

Chopping residual
softwood with stationary
chopper in the sawmill. No
transports are included.

Austria /
Central Europe

Ecoinvent

Wood chips,
softwood, from
industry, u=40%, at
plant/RER S - Wood
chips, softwood,
from industry,
u=40%, at
plant/RER U

Average mix 72%
softwood and 28%
hardwood (Swiss average).

Chopping of residual wood
with a mobile chopper in
the forest. Also includes
the driving of the mobile
chopper to and within the
forest.

100.0%

Wood chips, mixed,
u=120%, at
forest/RER S Wood chips, mixed,
u=120%, at
forest/RER U

Ecoinvent

Table 27 (cont’d)

241

*Allocation is mass based unless indicated otherwise.

242

Italy

2012 (published)
Chopping of residual
softwood with a mobile
chopper in the forest. Also
includes the driving of the
mobile chopper to and
within the forest.

Austria /
Central Europe

Bulked volume dried
matter content 169
kg/m3.

USA (Maine)

Weed control, tree felling,
skidding, seedling
production, site
preparation and
plantation, harvesting and
woodchip processing,
transport to facility, soil
scarification, bucking,
de-limbing.

The use of ethanol derived
from black locust was
found to have the lowest
impact in most categories.
Global warming potential
over 100 years reduced
97%, Acidification
potential reduced 42%,
Eutrophication potential
reduced 41%, Fossil fuels
use reduced 76%.

100.0%

Wood chips,
softwood, u=140%,
at forest/RER S Wood chips,
softwood, u=140%,
at forest/RER U

Journal of cleaner
Production Vol 19 Iss 8

Comparative life
cycle assessment of
ethanol production
from fast-growing
wood crops (black
locust, eucalyptus
and poplar)

Functional unit is 4 cubic
meters of hardwood chips.
Cradle to gate study.

Ecoinvent

Attributional life
cycle assessment of
wood chips for
bioethanol
production

Journal of Biomass
and Bioenergy April 2012

Table 27 (cont’d)

Waste paper,
mixed, from public
collection, for
further
treatment/CH S Waste paper,
mixed, from public
collection, for
further
treatment/CH U

Transport of
Equipment

Debarking

Sawing

Planting

Sorting

Stacking

Handling

Drying

Rotary Peeling

Facility
Infrastructure

Water
Consumption

Energy

Materials

Waste Treatment

A

Name

Transport to
Process Facility

Table 28 – Wood Data Summary Inputs

B

C

D

E

F

G

H

I

J

K

L

M

N

O

1

243

Table 28 (cont’d)
A

M

N

O

Waste paper,
sorted, for further
treatment/CH S Waste paper,
sorted, for further
treatment/CH U

1

1

1

Waste paper,
sorted, for further
treatment/RER S Waste paper,
sorted, for further
treatment/RER U

1

1

1

Waste paper,
mixed, from public
collection, for
further
treatment/RER S Waste paper,
mixed, from public
collection, for
further
treatment/RER U

B

C

D

E

F

G

H

I

J

K

L

1

244

Table 28 (cont’d)
A

B

C

D

E

F

G

H

Forest residue,
processed and
loaded, at landing
system
Bark, softwood,
average, state or
private moist cold
forest, at forest
road, INW
Bark chips,
softwood, u=140%,
at forest road/RER S
- Bark chips,
softwood, u=140%,
at forest road/RER
U
Bark chips,
softwood, u=140%,
at plant/RER S Bark chips,
softwood, u=140%,
at plant/RER U

1

1

1

1

1

245

I

J

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

E

F

Bark, at sawmill, US
SE/kg/US

1

1

1

Bark, at plywood
plant, US
PNW/kg/US

1

Bark, at plywood
plant, US SE/kg/US

1

Bark, at rough
green lumber
sawmill, softwood,
US PNW/kg/US

1

1

1

Bark mulch, at
oriented strand
board production,
US SE/kg/US

246

G

H

I

J

K

L

M

N

O

Table 28 (cont’d)
A
Chips, Scandinavian
softwood (plantdebarked, u=70%,
at plant/NORDEL S Chips, Scandinavian
softwood (plantdebarked, u=70%,
at plant/NORDEL U

1

B

C

D

1

E

F

1

Co-products of glue
laminated beam
production, at
plant, unspecified,
US PNW/kg/US
Co-products of glue
laminated beam
production, at
plant, unspecified,
US SE/kg/US
Co-products of
laminated veneer
lumber production,
unspecified, US
PNW/kg/US

247

G

H

I

J

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

E

F

Co-products of
laminated veneer
lumber production,
unspecified, US
SE/kg/US
Dust and scrap, at
oriented strand
board production,
US SE/kg/US
Fines, at oriented
strand board
production, US
SE/kg/US
Hogfuel, from trim
and saw at plywood
plant, US
PNW/kg/US

1

Hogfuel, from
trimsaw, plywood
plant, US SE/kg/US

1

248

G

H

I

J

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

Panel trim, from
trim and saw at
plywood plant, US
PNW/kg/US

F

G

H

I

J

1

Panel trim, from
trim and saw at
plywood plant, US
SE/kg/US

E

1

Peeler core, from
green veneer
production at
plywood plant, US
PNW/kg/US

1

Peeler core, from
green veneer
production at
plywood plant, US
SE/kg/US

1

Planer shavings, at
planer mill, US
SE/kg/US

1

1

249

1

1

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

E

F

G

H

Planer shavings,
from dried lumber,
at planer mill, US
PNW/kg/US

1

1

1

1

Planer shavings,
from green lumber,
at planer mill, US
PNW/kg/US

1

1

1

1

1

1

Pulp chips, at rough
green lumber
production, US
PNW/kg/US

1

1

1

Pulp chips, at
sawmill, US
SE/kg/US

1

1

1

Pulp chips, from
dried lumber, at
planer mill, US
PNW/kg/US

1

1

250

I

J

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

F

G

H

1

Pulp chips, from
green lumber, at
planer mill, US
PNW/kg/US

E

1

1

I

J

1

Pulp chips, from
green veneer
production at
plywood plant, US
PNW/kg/US

1

Pulp chips, from
green veneer
production at
plywood plant, US
SE/kg/US

1

Sawdust from I-Joist
processing, at plant,
US SE/kg/US
Sawdust, at planer
mill, US SE/kg/US

1

1

1

251

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

E

F

Sawdust, at rough
green lumber
production, us
PNW/kg/US

1

1

1

Sawdust, at
sawmill, US
SE/kg/US

1

1

G

H

1

1

Sawdust, from dried
lumber, at planer
mill, US PNW/kg/US

1

1

1

Sawdust, from
green lumber, at
planer mill, US
PNW/kg/US

1

1

1

Sawdust, from IJoist processing, at
plant, US
PNW/kg/US

252

I

J

K

L

M

N

O

Table 28 (cont’d)
A

B

C

D

Sawdust, from trim
and saw at plywood
plant, US
PNW/kg/US

F

1

Sawdust, from trim
and saw, plywood
plant, US SE/kg/US

E

1

Sawdust,
Scandinavian
softwood (plantdebarked), u=70%,
at plant/NORDEL S Sawdust,
Scandinavian
softwood (plantdebarked), u=70%,
at plant/NORDEL U

1

1

1

253

G

H

I

J

K

L

M

N

O

Table 28 (cont’d)
A
Waste wood chips,
mixed, from
industry, u=40%, at
plant/CH S - Waste
wood chips, mixed,
from industry,
u=40%, at plant/CH
U

B

1

D

E

F

1

1

1

254

G

H

I

J

K

L

1

1

Wood chips,
hardwood, from
industry, u=40%, at
plant/RER S - Wood
chips, hardwood,
from industry,
u=40%, at
plant/RER U
Wood chips,
hardwood, u=80%,
at forest/RER S Wood chips,
hardwood, u=80%,
at forest/RER U

C

1

M

N

O

1

Table 28 (cont’d)
A

B

Wood chips, mixed,
from industry,
u=40%, at
plant/RER S - Wood
chips, mixed, from
industry, u=40%, at
plant/RER U
Wood chips, mixed,
u=120%, at
forest/RER S Wood chips, mixed,
u=120%, at
forest/RER U
Wood chips,
softwood, from
industry, u=40%, at
plant/RER S - Wood
chips, softwood,
from industry,
u=40%, at
plant/RER U

C

D

E

F

1

1

1

1

255

G

H

I

J

K

L

M

N

O

Table 28 (cont’d)
A
Attributional life
cycle assessment of
wood chips for
bioethanol
production

B

D

E

1

1

C

F

1

1

Comparative life
cycle assessment of
ethanol production
from fast-growing
wood crops (black
locust, eucalyptus
and poplar)
Wood chips,
softwood, u=140%,
at forest/RER S Wood chips,
softwood, u=140%,
at forest/RER U

1

G

1

256

H

I

J

K

L

M

N

1

O

ethanol, denatured,
mixed feedstocks, at
conversion facility,
2022
Ethanol, denatured,
switchgrass,
biochemical

USLCI

Blend of corn, corn stover,
forest residues,
switchgrass and wheat
straw ethanol

Materials

Materials, waste disposal,
electricity, diesel, water

257

North
America

North
America

North
America

Data Country

Materials, waste disposal,
electricity, diesel, water

North
America

Ethanol, denatured,
wheat straw,
biochemical

North
America

USLCI

Materials, waste disposal,
electricity, diesel, water

Includes

Data Years

Ethanol, denatured,
corn stover,
biochemical

Details

2022

USLCI

Materials, waste disposal,
electricity, diesel, water

Allocation*

Data Availability

Ethanol, denatured,
forest residues,
thermochemical

USLCI

Name

USLCI

Table 29 – Chemical Data Summary

258

USA

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of corn grains to the
basis). Dry milling
distillery, processing to hydrated
technology. Economic
ethanol. System boundary is at
allocation 97.6% to
the distillery and dehydration is
ethanol. CO2 emissions are
not included.
allocated based on carbon
balance.

Unspecified

Production of 1 kg
hydrated ethanol 95% dry
basis (1.05kg wet basis).
Also delivers co-product
"DDGS, from corn, at
distillery". Economic
allocation w/ factor of
97.6% to ethanol.
Allocation according to
carbon balance for CO2
emissions.

1990-2006

97.6%

USLCI

Corn dried and stored, milling,
gluten drying, waste disposal,
fermentation, electricity

97.6%

Ethanol, 95% in
H2O, from corn, at
distillery/US S Ethanol, 95% in
H2O, from corn, at
distillery/US U

GaBi PE

Corn, in distillery

Ecoinvent

Ethanol, denatured,
corn dry mill

North
America

Table 29 (cont’d)

Multi-output process
"grass, to fermentation"
delivers the co-products
Ethanol 95%, fibers from
grass, and proteins from
grass. Economic allocation.
Data from a Swiss pilot
plant.

Fermentation of grass including
materials, energy uses,
infrastructure, and emissions.

Time of publications

Switzerland

1 kg hydrated ethanol 95%
( dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of potatoes to the
basis). Dry milling
distillery, processing to hydrated
technology. Economic
ethanol. System boundary at the
allocation 95.6% to
distillery gate, dehydration not
ethanol. CO2 emissions are
included.
allocated based on carbon
balance.

2002-2005

Switzerland

95.6%

Ethanol, 95% in
H2O, from potatoes,
at distillery/CH S Ethanol, 95% in
H2O, from potatoes,
at distillery/CH U

Ecoinvent

Ethanol, 95% in
H2O, from grass, at
fermentation
plant/CH S - Ethanol,
95% in H2O, from
grass, at
fermentation
plant/CH U

Ecoinvent

Table 29 (cont’d)

259

97.7%

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of grains to the
basis). Dry milling
distillery, processing to hydrated
technology. Economic
ethanol. System boundary at the
allocation 97.7% to
distillery gate, dehydration not
ethanol. CO2 emissions are
included.
allocated based on carbon
balance.

2002-2006

Europe

94.5%

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of molasses to the
basis). Dry milling
distillery, processing to hydrated
technology. Economic
ethanol. System boundary at the
allocation 97.7% to
distillery gate, dehydration not
ethanol. CO2 emissions are
included.
allocated based on carbon
balance.

1998-2005

Switzerland

Ethanol, 95% in
H2O, from sugar
beet molasses, at
distillery/RER S Ethanol, 95% in
H2O, from sugar
beet molasses, at
distillery/RER U

Ecoinvent

Ethanol, 95% in
H2O, from rye, at
distillery/RER S Ethanol, 95% in
H2O, from rye, at
distillery/RER U

Ecoinvent

Table 29 (cont’d)

260

Allocation based on
economic criteria.

Fermentation of sugar beets
including materials, energy uses,
infrastructure, and emissions.

Time of publications

Modeled for Switzerland
with data from Finland

Allocation based on
economic criteria.

Fermentation of sugar cane
including materials, energy uses,
infrastructure, and emissions.

Time of publications

Brazil

Ethanol, 95% in H20,
from sugar cane, at
fermentation
plant/BR S - Ethanol,
95% in H20, from
sugar cane, at
fermentation
plant/BR U

Ecoinvent

Ethanol, 95% in
H2O, from sugar
beets, at
fermentation
plant/CH S - Ethanol,
95% in H2O, from
sugar beets, at
fermentation
plant/CH U

Ecoinvent

Table 29 (cont’d)

261

10-11%

1 kg hydrated ethanol 95%
( dry basis, i.e. 1.05 kg
hydrated ethanol wet
basis). Dry milling
technology. Economic
allocation 97.7% to
ethanol. CO2 emissions are
allocated based on carbon
balance.

Transport of sugarcane to the
sugar refinery and the
processing to ethanol. System
boundary is at the sugar
refinery. Treatment of waste
effluents is not included (most
waste water used on nearby
fields).

1994-2006

Brazil

91.0%

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
basis). Dry milling
technology. Economic
allocation 97.7% to
ethanol. CO2 emissions are
allocated based on carbon
balance.

Transport of sorghum to the
distillery and the processing to
ethanol. System boundary is at
the sugar refinery. Treatment of
waste effluents is not included
(most waste water used on
nearby fields).

1992-2005

China

Ethanol, 95% in H20,
from sweet
sorghum, at sugar
refinery/CN S Ethanol, 95% in H20,
from sweet
sorghum, at sugar
refinery/CN U

Ecoinvent

Ethanol, 95% in H20,
from sugarcane
molasses, at sugar
refinery/BR S Ethanol, 95% in H20,
from sugarcane
molasses, at sugar
refinery/BR U

Ecoinvent

Table 29 (cont’d)

262

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of wood to the
basis). Dilute acid predistillery and the processing to
hydrolysis and
ethanol. System boundary is at
simultaneous
the sugar refinery. Dehydration
saccharification and cois not included. Process heat and
fermentation. Economic
power supply is generated by
allocation 97.7% to
unconverted solids.
ethanol. CO2 emissions are
allocated based on carbon
balance.

263

Switzerland
Switzerland

Fermentation of whey including
materials, energy uses,
infrastructure, and emissions.

1999-2006

Allocation based on
economic criteria.

99.7%

Ethanol, 95% in H20,
from wood, at sugar
refinery/CH S Ethanol, 95% in H20,
from wood, at sugar
refinery/CH U

Ecoinvent

Ethanol, 95% in H20,
from whey, at sugar
refinery/CH S Ethanol, 95% in H20,
from whey, at sugar
refinery/CH U

Ecoinvent

Table 29 (cont’d)

264

Sweden
Brazil

Dewatering of ethanol 95%,
including ethanol mix.

Switzerland

Dewatering of ethanol 95%

Modeled with ethanol
production from sugar
beets, whey and grass.

1999

Dewatering of ethanol 95%

2007

99.7%

Ethanol, 99.7% in
H2O, from biomass,
at distillation/CH S Ethanol, 99.7% in
H2O, from biomass,
at distillation/CH U

Ecoinvent

Ethanol, 99.7% in
H2O, from biomass,
at distillation/BR S Ethanol, 99.7% in
H2O, from biomass,
at distillation/BR U

1 kg hydrated ethanol 95%
(dry basis, i.e. 1.05 kg
hydrated ethanol wet
Transport of wood to the
basis). Dilute acid predistillery and the processing to
hydrolysis and
ethanol. System boundary is at
simultaneous
the sugar refinery. Dehydration
saccharification and cois not included. Process heat and
fermentation. Economic
power supply is generated by
allocation 97.7% to
unconverted solids.
ethanol. CO2 emissions are
allocated based on carbon
balance.

Ecoinvent

Ethanol, 95% in H20,
from wood, at sugar
refinery/SE S Ethanol, 95% in H20,
from wood, at sugar
refinery/SE U

Ecoinvent

Table 29 (cont’d)

Dehydration of hydrated
ethanol (95%). Treatment of
waste also included.

2002-2006

Europe

1 kg corn-based ethanol.
Molecular sieve
technology used for
dehydration.

Dehydration of hydrated
ethanol (95%). Treatment of
waste also included.

USA

265

China

1 kg rye-based ethanol.
Molecular sieve
technology used for
dehydration.

1992-2005

Dehydration of hydrated
ethanol (95%). Treatment of
waste also included.

1990-2006

Ethanol, 99.7% in
H2O, from biomass,
at distillation/US S Ethanol, 99.7% in
H2O, from biomass,
at distillation/US U

Ecoinvent

Ethanol, 99.7% in
H2O, from biomass,
at distillation/RER S Ethanol, 99.7% in
H2O, from biomass,
at distillation/RER U

1 kg Sorghum-based
ethanol. Molecular sieve
technology used for
dehydration.

Ecoinvent

Ethanol, 99.7% in
H2O, from biomass,
at distillation/CN S Ethanol, 99.7% in
H2O, from biomass,
at distillation/CN U

Ecoinvent

Table 29 (cont’d)

Bioethanol and coproducts
from switchgrass.

Land use change, harvesting,
transport to refinery, drying and
palletizing, biorefinery
functions. Also includes
cultivation, seeding, fertilizer,
herbicides, lime and direct
emissions.

266

USA (Minnesota)

Life cycle
assessment of a biorefinery concept
producing
bioethanol,
bioenergy, and
chemicals from
switchgrass

USA (Oregon)

water consumption during corn
production

Unspecified

Ecological impacts of corn
based ethanol - water
consumption in 81
spatially explicit Minnesota
watersheds.

2011

Measuring
ecological impact of
water consumption
by bioethanol using
life cycle impact
assessment

2010

International
Journal of LCA 2011

Ethanol from Tall Fescue
grass straw by various
methods: dilute acid,
dilute alkali, hot water,
steam explosion.

Grass seed production, straw
collection and transportation,
electricity and process energy.
Conversion to ethanol: Biomass
Preparation, pretreatment,
conditioning (or hydrolysis),
fermentation, distillation, waste
water treatment, energy
recovery from biogas.

International Journal
of LCA Jan 2012

Life cycle
assessment of
energy and GHG
emissions during
ethanol production
from grass straws
using various
pretreatment
processes

International Journal
of LCA Jan 2010

Table 29 (cont’d)

Brazil

Corn ethanol represents
dry milling process, and
Soybean oil was made
using the crushing process.
GHG only reported
category. Data is at the
county level, 40 counties in
the US corn belt were
chosen.

Direct land use change is
included. It is measured and the
change in soil carbon organic
material. Includes impacts
associated with the biomass,
bio-refining, upstream
processes.

267

USA

Soil preparation, cane
plantation, chemical application,
harvesting, fuel ethanol process,
and energy co-generation

2009

Functional Unit is 10,000
Km covered by car of a
specific size engine, but
data is broken into phases.

USA

Regional variations
in GHG emissions of
bio-based products
in the United States
- Corn based ethanol
and soybean oil

Agriculture: Seeding, energy,
minerals, pesticides, fertilizers,
inorganics and water (land use
excluded). Cellulosic stage:
pretreatment, hydrolyze
fermentation, distillation,
dehydration and evaporation.

2009

Life Cycle
Assessment of fuel
ethanol from sugar
cane in Brazil

Different allocation
methods are used to
assess sensitivity of result
to allocation method.
GHG, acidification,
eutrophication,
photochemical oxidation,
ecotoxicity, abiotic
depletion and ozone layer
depletion are reported.

2009

Allocation issues in
LCA methodology: a
case study of corn
stover based fuel
ethanol

International
International Journal of
International Journal of Life
Journal of LCA May
LCA Sept 2009
Cycle Assessment 2009
2009

Table 29 (cont’d)

2008

Thailand

Reports ethanol as E85,
but process contribution
data is available for
cassava production and
ethanol conversion.

cassava farming: land prep,
planting, crop maintenance,
harvesting. Chip processing:
chipping, dun dying, packing,
milling, mixing and liquifying,
fermentation, distillation,
dehydration

2008

Thailand

GHG and particulates
reported

Fertilizer, fuel, pesticides,
drying, extraction, refining

2008

Comparative LCA of
two biofuels ethanol from sugar
beet and rapeseed
methyl ester

Sugar cane production: land
prep, new planting (once per
crop rotation), crop
maintenance, harvesting. Sugar
milling, fermentation,
distillation, dehydration

International Journal of
Life Cycle Assessment
March 2008

Life cycle
assessment of fuel
ethanol from
cassava in Thailand

Functional unit is gas
equivalent consumed by a
new passenger car to
travel a specific distance.
Sugar factory and ethanol
data is primary.

International
Journal of Life
Cycle Assessment
May 2008

Life Cycle
Assessment of fuel
ethanol from cane
molasses in
Thailand.

International Journal for
LCA June 2008

Table 29 (cont’d)

268

269

Brazil

Both sugarcane and palm
tree are crops with high
biofuel yields. The joint
production of these crops
enhances the sustainability
of ethanol. For the
purpose of this study,
three sugarcane mills in
Sao Paulo State and one
palm oil refinery in Para
State were surveyed
(Brazil). Results showed
that fossil fuel use and
greenhouse gas emissions
decreased when the joint
production system was
compared to the
traditional sugarcane
ethanol production
system. As a result, energy
efficiency increased. 

2012 (publication)

Life cycle
assessment of
sugarcane ethanol
and palm oil
biodiesel joint
production

Journal of Industrial Ecology Sept. 2012

Table 29 (cont’d)

270

Australia

Ethanol from wheat, sugar
beet, and willow. Methyl
ester from rape and
methane from corn.

Thailand

Agricultural crop
based biofuels resource efficiency
and environmental
performance
including direct land
use changes

GHG emissions and Direct land
use changes.

Northern Europe

Journal of cleaner
production Vol 37

For the purpose of this
study, three sugarcane
mills in Sao Paulo State
and one palm oil refinery
in Para State were
surveyed (Brazil).

2012

Effect of biogas
utilization and plant
co-location on life
cycle greenhouse
gas emissions of
cassava ethanol
production

Cassava farming: Land prep,
seed planting, fertilizer,
weeding, harvesting. Cassava
chip production: weight and
measure of harvested roots,
chopping and drying, transport
to ethanol plant. Fuel
production: pre-treatment,
liquefying, saccharification.

2011

Journal of cleaner
production Vol 39

Land use change, milling and
production, fermentation,
additional cane growing (for
scenarios 3 and 4).

Journal of Cleaner
Production Vol 19

Bioproduction from
Australian
sugarcane: an
environmental
investigation of
product
diversification in an
agro-industry

2012

Table 29 (cont’d)

271

USA

Dewatering of ethanol 95% from
Scandinavian wood

China

Ethanol from Scandinavian
softwood chips

Sweden

Journal of Cleaner
Production Vol 16 (2007)

Ethanol, 99.7% in
H2O, from wood, at
distillation/SE S Ethanol, 99.7% in
H2O, from wood, at
distillation/SE U

2009 (published)

Life cycle inventory
and energy analysis
of cassava based
fuel ethanol in china

Results showed that
fossil fuel use and
greenhouse gas emissions
decreased when the joint
production system was
compared to the
traditional sugarcane
ethanol production
system. As a result, energy
efficiency increased.

Cassava cultivation and
treatment: seed production,
field prep and plough, sowing,
fertilizer, weed control,
harvesting, pilling and slicing,
insolation and packing.
Conversion from dry chips:
Milling, mixing, liquification and
saccharification, fermentation,
distilling, separation,
rectification and dehydration.
Denaturing included.

Improvements in
Life Cycle Energy
Efficiency and
Greenhouse Gas
Emissions of CornEthanol

2007

Journal of Industrial
Ecology, Feb. 2009

Energy used for feedstock
production and harvesting,
including fossil fuels for field
operations and electricity for
grain drying and irrigation.

Ecoinvent

Table 29 (cont’d)

Conversion of wood chips into
synthetic gas. Includes drying
and further comminution of
wood chips (down to size
30x30x30mm), fluidized bed
gasification, treatment of the
resulting syngas to remove
impurities and contaminants.

1995-2004

Switzerland

Composition (%mol) of the
resulting gas is 15.5% H2,
39.2% CO, 34.9% CO2,
8.7% CH4, and 1.7 CnHm
on a nitrogen and water
free basis. Nitrogen
content is 50.4%. Density
is 1.15 kg/Nm3. Lower
heating value of the gas is
5.4 MJ/Nm3. Production
of 1 Nm3 syngas.

Conversion of wood chips into
synthetic gas. Includes drying
and further comminution of
wood chips (down to size
30x30x30mm), fluidized bed
gasification, treatment of the
resulting syngas to remove
impurities and contaminants.

1995-2004

Switzerland

Synthetic gas, from
wood, at fluidized
bed gasifier/CH S Synthetic gas, from
wood, at fluidized
bed gasifier/CH U

Ecoinvent

Synthetic gas, from
wood, at fixed bed
gasifier/CH S Synthetic gas, from
wood, at fixed bed
gasifier/CH U

Composition (%mol) of the
resulting gas is 28.4% H2,
40.6% CO, 23.6% CO2,
5.9% CH4, and 1.5 CnHm
on a nitrogen and water
free basis. Nitrogen
content is 47.6%. Density
is 1.15 kg/Nm3. Lower
heating value of the gas is
5.4 MJ/Nm3. Production
of 1 Nm3 syngas.

Ecoinvent

Table 29 (cont’d)

272

Materials, process energy,
water, transport,

273

Switzerland

"CARBON SEQUESTRATION
should be accounted for
after the product is built in
its LCA model, and should
be included depending on
the use of end of life fate
of that product."(USLCI)

1995-2004

Production of synthetic gas from
wood chips. 50% fixed
gasification, 50% fluidized
gasification.

North America

Soy biodiesel,
production, at plant

Ecoinvent

Synthetic gas,
production mix, at
plant/CH S

Wood gasification in CH is
limited to the fixed bed
pilot plant experience
(Pyroforce, Xylowatt).
Average composition (%
mol) is 22.0% H2, 39.9%
CO, 29.3% CO2, 7.3% CH4,
1.6% CnHm on a nitrogen
and water free basis.
Nitrogen content is 49.0%.
Density is 1.15 kg/Nm3.

USLCI

Table 29 (cont’d)

Denmark

Rape seed production, storage,
processing, transport, energy
requirements, co-products.

Costa Rica

International Journal
of LCA 2012

Life cycle
assessment of
biodiesel in Costa
Rica

Specific Denmark case,
reports 6 impact
categories.

2012

Potential for
production and use
of rapeseed
biodiesel. Based on
a comprehensive
real-time LCA case
study in Denmark
with multiple
pathways

University of Applied
Sciences Northwestern
Switzerland (FHNW)

Table 29 (cont’d)

274

Primary data was obtained
for the crop portion of the
data only.

2001-2005

Argentina

Goal to provide baseline
information about algae
biodiesel. Data was taken
from USLCI and literature
sources. Many
assumptions /
substitutions were made
to estimate an LCA for
Algae biodiesel. Open
pond growth assumed.
Soybean processing data
used to estimate the
conversion of algal lipids
into biodiesel, and algae
meal was assumed to have
the same ethanol yield as
wheat straw.

Algae growth, harvesting,
separation, processing,
transportation and distribution
included. Partial treatment of
wastewater and natural gas
requirements also included.

Time of publications

Unspecified

Life Cycle Analysis of
Algae Biodiesel

International Journal
of LCA March 2009

Life cycle
assessment of
soybean based
biodiesel production
in Argentina

Only CO2, CH4, and N2O were
considered in the global
warming calculation. Land use
change except for direct deforestation is excluded. Storage
and drying is excluded.
Agriculture, extraction and
refining, trans esterification
included.

Sander and Murthy 2010 –
International Journal of LCA

Table 29 (cont’d)

275

276

Brazil

Compares conventional
diesel with biodiesel
production from winter
rape crop. LCA evaluates
the energy balance and
the environmental
impacts.

Thailand

Journal of Industrial
Ecology July 2012

A life cycle
assessment of
biodiesel production
from winter rape
grown in Southern
Europe

Land use change, palm oil
cultivation and harvesting,
transport, milling , biodiesel
conversion, by product
processing, on site waste
management, and use of
biodiesel in a vehicle included.

Southern Europe

Food, Fuel and
Climate Change: Is
palm based
biodiesel a
sustainable option
for Thailand?

Uses an indicator called
"net feedstock balance"
that describes the physical
supply of feedstock for the
long term. This is a "well to
wheels" study, but unit
process data is available
for land use change
scenarios and how they
affect GHG emissions. Only
100 year GWP reported.

The life cycle
assessment of
biodiesel from palm
oil (“dende”) in the
Amazon

2012

Journal of Biomass
andBioenergy,
Jan. 2012

Palm oil as a promising
source of biodiesel in the
Amazon

Journal of Biomass and
Bioenergy May 2012

Table 29 (cont’d)

277

Brazil and Europe

2009

Biogenic greenhouse
gas emissions linked
to the life cycles of
biodiesel derived
from European
rapeseed and
Brazilian soybeans

Journal of Cleaner Production
2009

Table 29 (cont’d)

Transport

Infrastructure

Waste Treatment

Machines

Extraction

Process Energy

Drying

Fermentation

Refining

Direct Emissions

Storage

Water

Land Use

A

Name

Materials

Table 30 – Chemical Data Summary Inputs

B

C

D

E

F

G

H

I

J

K

L

M

N

Ethanol, denatured, forest
residues, thermochemical

1

1

1

1

Ethanol, denatured, corn
stover, biochemical

1

1

1

1

Ethanol, denatured, wheat
straw, biochemical

1

1

1

1

ethanol, denatured, mixed
feedstocks, at conversion
facility, 2022

1

Ethanol, denatured,
switchgrass, biochemical

1

1

1

1

278

Table 30 (cont’d)
A

B

C

D

E

F

G

H

I

J

K

Ethanol, denatured, corn
dry mill

Corn, in distillery
Ethanol, 95% in H2O, from
corn, at distillery/US S Ethanol, 95% in H2O, from
corn, at distillery/US U
Ethanol, 95% in H2O, from
grass, at fermentation
plant/CH S - Ethanol, 95%
in H2O, from grass, at
fermentation plant/CH U
Ethanol, 95% in H2O, from
potatoes, at distillery/CH S
- Ethanol, 95% in H2O,
from potatoes, at
distillery/CH U

1

1

1

1

1

1

1

279

1

L

M

N

Table 30 (cont’d)
A

B

C

D

E

F

G

Ethanol, 95% in H2O, from
rye, at distillery/RER S Ethanol, 95% in H2O, from
rye, at distillery/RER U

1

1

I

J

K

1

Ethanol, 95% in H2O, from
sugar beet molasses, at
distillery/RER S - Ethanol,
95% in H2O, from sugar
beet molasses, at
distillery/RER U

H

1

Ethanol, 95% in H2O, from
sugar beets, at
fermentation plant/CH S Ethanol, 95% in H2O, from
sugar beets, at
fermentation plant/CH U

1

1

1

280

1

L

M

N

Table 30 (cont’d)
A
Ethanol, 95% in H20, from
sugar cane, at
fermentation plant/BR S Ethanol, 95% in H20, from
sugar cane, at
fermentation plant/BR U

B

1

C

D

E

F

1

G

1

Ethanol, 95% in H20, from
sugarcane molasses, at
sugar refinery/BR S Ethanol, 95% in H20, from
sugarcane molasses, at
sugar refinery/BR U

1

1

Ethanol, 95% in H20, from
sweet sorghum, at sugar
refinery/CN S - Ethanol,
95% in H20, from sweet
sorghum, at sugar
refinery/CN U

1

1

281

H

I

J

K

1

L

M

N

Table 30 (cont’d)
A
Ethanol, 95% in H20, from
whey, at sugar refinery/CH
S - Ethanol, 95% in H20,
from whey, at sugar
refinery/CH U

B

1

C

D

E

F

1

G

H

1

Ethanol, 95% in H20, from
wood, at sugar refinery/CH
S - Ethanol, 95% in H20,
from wood, at sugar
refinery/CH U

1

1

1

Ethanol, 99.7% in H2O,
from biomass, at
distillation/BR S - Ethanol,
99.7% in H2O, from
biomass, at distillation/BR
U

1

282

J

K

1

1

Ethanol, 95% in H20, from
wood, at sugar refinery/SE
S - Ethanol, 95% in H20,
from wood, at sugar
refinery/SE U

I

L

M

N

Table 30 (cont’d)
A

B

C

D

E

F

G

H

Ethanol, 99.7% in H2O,
from biomass, at
distillation/CH S - Ethanol,
99.7% in H2O, from
biomass, at distillation/CH
U

1

Ethanol, 99.7% in H2O,
from biomass, at
distillation/CN S - Ethanol,
99.7% in H2O, from
biomass, at distillation/CN
U

1

Ethanol, 99.7% in H2O,
from biomass, at
distillation/RER S - Ethanol,
99.7% in H2O, from
biomass, at distillation/RER
U

1

Ethanol, 99.7% in H2O,
from biomass, at
distillation/US S - Ethanol,
99.7% in H2O, from
biomass, at distillation/US
U

1

283

I

J

K

L

M

N

Table 30 (cont’d)
A

B

1

1

Life cycle assessment of a
bio-refinery concept
producing bioethanol,
bioenergy, and chemicals
from switchgrass

1

1

Allocation issues in LCA
methodology: a case study
of corn stover based fuel
ethanol

1

Life cycle assessment of
energy and GHG emissions
during ethanol production
from grass straws using
various pretreatment
processes

C

D

E

F

1

G

H

1

I

J

K

L

M

1

Measuring ecological
impact of water
consumption by bioethanol
using life cycle impact
assessment

1

1

284

1

1

1

1

N

Table 30 (cont’d)
A

B

C

D

E

F

G

H

I

J

Life Cycle Assessment of
fuel ethanol from sugar
cane in Brazil

1

1

Regional variations in GHG
emissions of bio-based
products in the United
States - Corn based ethanol
and soybean oil

1

1

1

1

Life Cycle Assessment of
fuel ethanol from cane
molasses in Thailand.

1

1

1

1

1

Life cycle assessment of
fuel ethanol from cassava
in Thailand

1

1

1

1

1

1

Comparative LCA of two
biofuels - ethanol from
sugar beet and rapeseed
methyl ester

1

1

1

1

285

K

L

M

N

1

1

1

Table 30 (cont’d)
A

B

C

D

E

F

G

1

1

1

1

H

I

J

K

L

M

N

Life cycle assessment of
sugarcane ethanol and
palm oil biodiesel joint
production
Bioproduction from
Australian sugarcane: an
environmental
investigation of product
diversification in an agroindustry

1

Effect of biogas utilization
and plant co-location on
life cycle greenhouse gas
emissions of cassava
ethanol production

1

Agricultural crop based
biofuels - resource
efficiency and
environmental
performance including
direct land use changes

1

2

1

1

1

1

286

Table 30 (cont’d)
A

B

C

D

E

F

G

H

I

J

1

1

1

1

K

L

1

Improvements in Life Cycle
Energy Efficiency and
Greenhouse Gas Emissions
of Corn-Ethanol

Life cycle inventory and
energy analysis of cassava
based fuel ethanol in china

1

Ethanol, 99.7% in H2O,
from wood, at
distillation/SE S - Ethanol,
99.7% in H2O, from wood,
at distillation/SE U
Synthetic gas, from wood,
at fixed bed gasifier/CH S Synthetic gas, from wood,
at fixed bed gasifier/CH U

1

1

1

287

1

M

N

Table 30 (cont’d)
A

B

C

D

E

F

H

1

Synthetic gas, from wood,
at fluidized bed gasifier/CH
S - Synthetic gas, from
wood, at fluidized bed
gasifier/CH U

G

1

I

J

K

L

M

1

Synthetic gas, production
mix, at plant/CH S
Soy biodiesel, production,
at plant

1

1

Potential for production
and use of rapeseed
biodiesel. Based on a
comprehensive real-time
LCA case study in Denmark
with multiple pathways

1

1

1

1

1

Life cycle assessment of
biodiesel in Costa Rica

288

1

1

N

Table 30 (cont’d)
A

B

C

D

1

Life Cycle Analysis of Algae
Biodiesel

1

1

1

1

G
1

1

1

F
1

Life cycle assessment of
soybean based biodiesel
production in Argentina

E

1

H

I

J

K

L

M

N

1

1

The life cycle assessment of
biodiesel from palm oil
(“dende”) in the Amazon
Food, Fuel and Climate
Change: Is palm based
biodiesel a sustainable
option for Thailand?

1

A life cycle assessment of
biodiesel production from
winter rape grown in
Southern Europe
Biogenic greenhouse gas
emissions linked to the life
cycles of biodiesel derived
from European rapeseed
and Brazilian soybeans

1

289

1

1

Data Country

Includes

Production of 1 kg granulate
modified starch, highly aggregated
background data used.

Only highly aggregated data were available,
therefore emissions from energy
consumption, waste water treatment and
raw material inputs have been subtracted
resulting in a difference in NMVOC
emissions. Production of input material corn
starch and fossil components (plasticizers
and complexing agents), transports of input
materials, energy consumption in the
processing and packaging at plant as well as
waste treatment.

Europe

Data Years

Details

Production of 1 kg PLA, based on
data from world's largest PLA
plant. The inventories include the
LCI data from the report of the
producer NatureWorks. Only
aggregated data are reported.

Only highly aggregated data were available,
therefore emissions from energy
consumption, waste water treatment and
raw material inputs have been subtracted
resulting in a difference in NMVOC
emissions. Maize production, energy use,
transport and waste water treatment.
Infrastructure has been added. Wind power
used to offset CO2 emissions.

USA (Nebraska)

Polylactide,
granulate, at
plant/GLO S Polylactide,
granulate, at
plant/GLO U

Ecoinvent

Modified
starch, at
plant/RER S Modified
starch, at
plant/RER U

Ecoinvent

Name

Data
Availability

Table 31 – Polymer Data Summary

290

Heating, agitating, rolling, mixing, extruding,
pelletizing, cooling.

USA (Nebraska)

Thermoplastic processing of NTP
derived from bloodmeal into
pellets.

Materials, waste disposal, process energy,
water

291

USA

USLCI

An ecoprofile of
thermoplastic
protein derived
from blood
meal Part 2:
thermoplastic
processing

Materials, waste disposal, electricity, natural
gas, water, transport.

New Zealand

Soy-based
resin, at plant

" CARBON SEQUESTRATION
should be accounted for after the
product is built in its LCA model,
and should be included depending
on the use of end of life fate of
that product"(USLCI).

Polylactide
Biopolymer
Resin, at plant

2012

USLCI

Data has been peer reviewed by
Dr. I. Boustead from Boustead
Consulting, UK. Data only
represents Ingeo polylactide (PLA)
resin production by NatureWorks
LLC in Blair Nebraska and cannot
be used for PLA production in
general. Final review report is
attached to the Data Module
Report.

International Journal of
LCA 2012

Table 31(cont’d)

Study goal: identify major
contributors to the environmental
impact of sugar cane-based LDPE
and to compare the
environmental performance of
sugar cane LDPE produced in
Brazil and used in Europe with the
performance of fossil-based LDPE
produced and used in Europe.
Land Use Change (LUC) emissions
were also accounted for.

Land use change, ethanol production,
polymerization, and transport of polymer by
cargo ship.

Data summary table references: [3, 7, 9, 10, 13, 17, 21, 24-27, 29-32, 35-94]

292

USA (shipped to Singapore)

Corn production, wet milling, fermentation
and recovery, blown film extrusion, ship
transport, road transport

Brazil

This study is for Singapore, but the
PHA bags are manufactured in the
US and transported vs. the PE
bags which are manufactured in
Singapore, this difference means
there is a long transport step
included for the PHA bags but not
the PE bags.

2012 (published)

A Comparative
Life Cycle
Assessment
Study of PE
Based on Sugar
Cane and Crude
Oil

International Journal of LCA
March 2010

Environmental
impacts of
conventional
plastic and biobased carrier
bags part 1: life
cycle
production

Journal of Industrial Ecology,
June 2012

Table 31(cont’d)

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293

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