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TRANSCRIPT
January 2017
GaBi Databases
Upgrades & Improvements
2017 Edition
3
Contents
1. Introduction to the upgrade of databases available with GaBi ....................... 5
2. GaBi Databases 2017 Edition ............................................................................. 6
About changes in the GaBi datasets 6
New datasets 8
Important changes this year 9
Inventories for electricity, thermal energy and steam 11
Inventories for primary energy carriers 24
Inventories for organic and inorganic intermediates 25
Inventories for metal processes 27
Inventories plastic processes 28
Inventories for end-of-life processes 28
Inventories for electronic processes 28
Inventories for renewable materials processes 31
Inventories for construction materials and processes 35
Inventories for textile processes 39
Inventories for US regional processes 40
3. Industry data in GaBi ........................................................................................ 45
4. General continuous improvements ................................................................. 52
Documentation / Naming 52
Sorting 53
LCIA / Method 53
New Objects 57
Bugs and improvements in various GaBi databases 58
References ............................................................................................................... 63
Annex: “Version 2016” datasets – Recommendations ........................................ 65
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Index of tables
Table 2- 1: Energy carrier mix for electricity generation – selected EU countries ............................. 14 Table 2- 2: Energy carrier mix for electricity generation – countries with significant changes........... 15 Table 2- 3: JIRA issues organic and intermediates .......................................................................... 27 Table 2- 4: JIRA issues for metal processes .................................................................................... 27 Table 2- 5: Die size to housing ratio (left) ........................................................................................ 30 Table 2- 6: Die size in updated processes (right) ............................................................................. 30 Table 2- 7: JIRA issues for renewable processes ............................................................................ 32 Table 2- 8: JIRA issues for construction processes ......................................................................... 36 Table 2- 9: JIRA issues for textile processes ................................................................................... 40 Table 2- 10: JIRA issues for US regional processes ........................................................................ 41
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1. Introduction to the upgrade of databases available with
GaBi
In total, over 30 employees of thinkstep were involved in the upgrade of several thousand unit
processes and aggregated LCI datasets. The invested time, knowledge and dedication of our
employees resulted in the new GaBi Databases 2017 Edition with more than 10,780 LCI process
datasets (584 of which are new). These data sets are built from a total of about 30,000 unit
processes in thinkstep’s central database - the by far largest process-based database available
worldwide and with the best coverage of industries and countries.
The process of continuous upgrades to the GaBi Databases is in part at least a result of the
parallel content and team structure within thinkstep, which is illustrated in the figure below.
Figure 1- 1: Content and team structure for the GaBi databases
In the GaBi Databases, process documentation is directly integrated in the datasets. Additional
information about the modelling principles applied to all datasets can be found in the document
GaBi Database and Modelling Principles1.
This present document covers relevant changes in the upgraded LCI datasets of the GaBi
Databases. The document addresses both methodology changes and changes in technology, if
any, and is structured by material or topic, e.g. electricity, metals, plastics, renewables. In
general, all thinkstep related datasets have been upgraded, in all cases by updating the energy
1 http://www.gabi-software.com/international/support/gabi/gabi-modelling-principles/
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mix and other background data, and often also by updates in the foreground data due to
changed technologies.
Methodological changes are not automatically endorsed by thinkstep, but introduced if
necessary: methodological changes are only useful if these changes or improvements are
supported by relevant best practise cases, evolving or edited standards or relevant stakeholder
initiatives with a respective practice acceptance.
2. GaBi Databases 2017 Edition
About changes in the GaBi datasets
“Facts do not cease to exist because they are ignored” Aldous Huxley.
thinkstep has introduced the annual upgrade of the GaBi databases for three reasons:
To keep your results as up-to-date and close to the evolving supply chains as possible;
including automated upgrades of your valued work to the most current state.
To avoid disruptive changes that would be caused by multi-year intervals that are often
surprising and hard to communicate and interpret.
To keep track on necessary methodological changes and to implement them promptly.
thinkstep databases are based on technical facts and are internationally accepted and broadly
applied. Standardized methods are used as a preference, which are established in industry,
science and regulatory authorities.
Changes in datasets are often the result of many effects in the supply chain. But “technical”
reasons should be carefully separated from methodological reasons. Necessary methodological
adoptions due to evolving standards, knowledge and frameworks may be useful; however GaBi
databases do not undertake methodological trials in its databases, as these are used for
decision support at thinkstep’s clients.
Changes in the environmental profile of the datasets from the predecessor GaBi Databases to
the most recent GaBi Databases may therefore be attributed to one or more of the following
factors:
Upgrade of the foreground and/or background systems. The market situation,
applied or newly available technology creates different impacts. The environmental
profile for the supply of energy carriers or intermediates may be subject to short-term
changes and affects the environmental profile of virtually all materials and products by
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varying degrees. For example a change of energy carrier mix or efficiency for electricity
supply changes the environmental profile of all materials or products created using that
electricity supply.
Improvements and changes in the technology of the production process.
Improvements or developments in production processes might achieve for example
higher energy efficiency, through the reduction of material losses and process emissions.
Sometimes, the technology is subjected to higher quality requirements that are defined
further downstream at the products (e.g. more end-of pipe measures to reduce
emissions, higher desulphurization of fuels) and improved use phase performance. In
addition, certain production routes might have been phased out, changing the production
mix of a certain material, substance or energy. A frequently changing and quite dynamic
example is the electricity grid mix datasets, as some countries try to reduce or phase-
out certain types of energy or fuels in the electricity supply mix, which require the
introduction of alternative sources of fuels and energy.
Further standardization and the establishment of regulative modelling
approaches. Modelling of realistic technology chains has always been the core focus of
the GaBi database. Some topics have attracted more attention, such as water and waste.
Further harmonisation and improvement in the LCA methodology and feedback from
clients and employees have enhanced the modelling approach for the GaBi Databases.
Detailed information is given in the document GaBi Database and Modelling Principles2.
Methodological adoptions are carried out extremely carefully, passing through multiple
levels of reviews by thinkstep experts responsible for standardization, technology
knowledge and quality assurance. This internal review process was audited within the
continuous improvement process by our external verification partner. GaBi database
updates and upgrades focus on reliability through consistency to ensure clients system
models and results are not jeopardised due to random methodological changes.
Correcting mistakes in the data. No man-made system is error-free. GaBi databases
are systematically quality-assured, for each release. Moreover, the GaBi databases are
regularly used by thinkstep’s many in-house consultants, so that next to reports of
possible errors from clients, thinkstep has an efficient in-house field control mechanisms.
Nevertheless, errors may be identified, in new data sets or from updates. These are
systematically tracked and solved towards the next release. The changes are
documented in this present document, also in the chapter on “Bugs and improvements”
(chapter 4.5).
2 http://www.gabi-software.com/international/support/gabi/gabi-modelling-principles/
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The degree of influence of each of these factors is specific to each process and cannot be
generalised for all cases nor can a single factor be highlighted. However as technological
excellence is a core value of thinkstep data, the focus is to update and apply ALL RELEVANT
AND IMPORTANT improvements and changes in technology and the supply chain and THE
NECESSARRY AND ESTABLISHED improvements and changes in the methodology.
Supply chain modelling of a single material involves hundreds or even thousands of single
operations. Therefore even opposing effects (improvements of some processes and higher
impacts of other processes along the chain) may occur.
GaBi systems leading to a single aggregated dataset consist of multiple datasets within one
supply chain. This means users could find many reasons for changes within a single supply
chain. GaBi models must be able to reflect in first instance the necessary complexity of the
reality, in order to be able to provide realistic data. Reduction of complexity is only credible, if
the reality of the supply chains is still mirrored adequately. The change analysis is a time
consuming but important process within thinkstep and the results are documented in this report.
However, the relevance of changes in the GaBi database related to the users own systems is
highly dependent on the goal and scope in the specific user application. This means the same
dataset may lead to significant changes for a certain user, whereas in another users system the
changes might be irrelevant. To shorten the time for users to reflect on the relevancy of the GaBi
databases changes for their own systems, the analyst function of GaBi Software may support in
an effective way. To guide users to the relevant changes in their models due to changes in
external factors and GaBi background data upgrades, thinkstep provides additionally this
present document “Gabi Databases Upgrades and Improvements” in addition to the document
“GaBi Database and Modelling Principles” and over 5000 interlinked electronical documentation
files supplied with thinkstep databases.
The following sections will address the most relevant changes in the GaBi Databases for the
different areas.
New datasets
In this year’s update 584 new datasets are available as part of the maintenance. If you have special
requests for datasets which are not available in the databases, we are additionally able to create
datasets via our “data-on-demand” service. Please contact us for further information.
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Professional database 250
Extension database II: energy 108
Extension database Ia: organic intermediates 10
Extension database Ib: inorganic intermediates 9
Extension database XI: electronics 26
Extension database XII: renewable materials 2
Extension database XVII: full US 36
Extension database XIV: construction materials 47
Extension database XX: food & feed 7
Extension database XXI: India 89
Total amount of new datasets 584
Important changes this year
Regionalized impact assessment is a comparatively new field in practical LCA work. Many
methods that are published are applicable to specific and detailed modelling situations (e.g.
agricultural cultivation on a specific field in a specific region), but were never transferred to
generic LCA databases. GaBi 2017 databases are ground-breaking in this respect.
Land use regionalization
With the 2017 release of GaBi databases, the assessment of land use has made a big step
forward. Regionalization is a very important topic for land use assessment and has now been
implemented in mining and agricultural resources datasets which cover the most important
sectors of land occupation and transformation. 63 countries were selected based on their
economic significance and coverage in the GaBi database. All EU-28 countries are included in
alignment with the Product Environmental Footprint (PEF) methodological guidelines of the
European Commission. For other countries, please use the un-regionalized flows and indicate
to us your needs, so that thinkstep can expand the list of countries in the upcoming years
accordingly.
Datasets from other data providers published in GaBi currently do not use regionalized flows.
Land use assessment is still possible for these datasets as well, but only using un-regionalized
flows with global Characterization Factors. As a consequence, the interpretation of land use
results comparing thinkstep datasets with datasets from other providers needs to be done with
caution. thinkstep believes that regionalization is a very important topic for land use assessment
and will work towards a common use of regionalization in the future, also for third-party data.
On the basis of the ILCD flow list, a mapping/conversion of all land use flows of different method
developers and dataset providers into a common set of flows was possible. With this, in GaBi
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now the parallel assessment of land use is possible for the different LCIA methods LANCA,
PEF/ILCD recommendation, ReCiPe, UBP, Impact 2002+ and EPS. The practitioners that have
assessed land use before will recognise that the land use folders “hemeroby” and
“hemeroby ecoinvent” are no longer there, since they have been merged with the other
land use folders “Occupation” and “Transformation”.
Land use is regarded as a resource category. Therefore, the flows for both occupation and
transformation are located at the input side of processes and balance view. This is also true for
the “transformation to” flows. As a consequence of this convention, the Characterization Factors
of the “transformation from” and the “transformation to” have a different algebraic sign (one is
positive, the other negative).
LANCA (developed by thinstep’s cooperation partner LBP of University Stuttgart, and
implemented with thinkstep) is a regionalized method and uses regionalized flows in the GaBi
processes that are marked as “ts”, indicating thinkstep as the data source.
Water regionalization
The focus this year was on datasets that are known to be the most significant contributors to
water consumption in almost all product systems: energy and agricultural materials. This means
that all energy and agricultural datasets use country specific flows instead of the unspecified
flows (e.g. “Groundwater, regionalized, DE” instead of “Groundwater”). However, that also
means that all other datasets still use the non-regionalized (unspecified) flows, since at this early
implementation stage it was not possible to implement regional flows into every available
dataset. As all datasets will have some energy datasets used as background datasets, every
dataset in GaBi will comprise some regionalized and some non-regionalized flows. In the impact
assessment phase, different options are implemented to characterize these unspecified flows.
The interpretation of the results needs to take this into account. To understand the quantitative
contribution of the un-regionalised flows to your specific system, we recommend using the
Balance Tool for comparing the amounts.
Applying these method to generic databases is tricky in terms of the technical implications but
also in terms of data availability. For some datasets, the specific region is unknown. For others,
it is explicitly intended to represent regional averages (e.g. fertilizers in the EU). Others will
represent averages, but with specific regional context (e.g. for generation of hydropower, several
dams in specific water sheds from the country average).
For further details and limitations please refer to the document “Introduction to Water
Assessment in GaBi Software”.
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Please note, quantities marked outdated cannot be used to evaluate water and land use
anymore, since the regionalized flows are not implemented in them. This concerns the following
quantities:
Recipe V1.05 (Land Use and Water Depletion)
Recipe V1.07 (Land Use and Water Depletion)
UBP2006
EI-99 (Land conversion)
Inventories for electricity, thermal energy and steam
Relevant changes in energy carrier mix for electricity generation after the upgrade
In the GaBi databases 2017, the reference year is 2013 for all electricity grid mixes and energy
carrier mixes (hard coal, crude oil and natural gas), as this is the latest year for which consistent
statistics of the IEA and other relevant international sources are available. One exception are
electricity grid mixes in the Extension Module XVII: Full US (electricity grid mixes for US sub
grids and sub-regions under eGRID) which have been updated from reference year 2010 to
2012 using the most recent version of eGRID (eGRID 2012, published in October 2015).
Relevant changes in the life cycle inventory (LCI) of the upgraded national grid mix datasets
occur for a couple of countries due to changes in the energy carriers that were used for electricity
generation, as well as changes in the amount of imported electricity and the country of origin of
these imports. The changes in the LCI data sets reveal the following trends:
An ongoing trend in some countries, to increase the share of renewable energies in their
electricity generation, which is for example observable for Spain, Greece, Germany,
Ireland, Italy, Lithuania or Romania.
Annual fluctuation in electricity generation from hydropower (availability of water for
electricity generation) due to climate conditions. In 2013, lower water availability for
hydropower compared to 2012 resulted in higher shares of fossil fuels for example in
Brazil, Finland, Latvia and Sweden. In contrast, higher water availability in Croatia,
Greece, Italy, Romania, Slovenia and Spain resulted in distinct higher electricity output
from hydro power plants.
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Incremental electricity demand in transition countries is predominantly covered by the
use of coal. In India production rose by 6% from 1127 TWh in 2012 to 1193 TWh, about
70% of the incremental electricity was covered by coal. In Indonesia 75% of the
production increase from 196 TWh to 216 TWh was covered by coal. China increased
its production by 10% or 453 TWh (approx. 70% of the total German production in 2013)
from 4,994 to 5,447 TWh. The incremental electricity was mainly produced by hard coal
(287 TWh), additional 48 TWh were produced by hydropower, 45 TWh by wind power,
39 TWh from coal gases and 34 TWh from other sources.
The following three figures present the development of the energy carrier mix for electricity
generation in Germany, the European Union and the United States between 2000 and 2013.
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Figure 2- 1: Development grid mix in Germany (left) and EU-28 (right)
Figure 2- 2: Development grid mix United States
Compared to 2012, the use of renewable energy sources for electricity generation in Germany
has increased from 23.9% in 2012 to 25.1%3 in 2013. Main driver for the increase in renewable
energies were electricity from photovoltaic. The generation of electricity from photovoltaic has a
share of 4.9% at the total generation (4.2% in 2012). The share of wind power remained stable
at around 8%. Use of natural gas for power generation further decreased from 12.4% in 2012 to
10.9% in 2013.
The substitution of natural gas for power generation is an ongoing trend in the European Union
also in 2013. The share of power generation from natural gas dropped from 17.8% in 2012 to
15.7% in 2013 (22.8% in 2010). The natural gas was mainly substituted by generation from
3 50% of electricity from waste is accounted as renewable energy
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renewable energies. The generation from renewable energy carriers increased from 24.4% in
2012 to 27.4% in 2013. The increase was partly driven by higher water availability for power
generation in 2013, but also by an increased generation from wind power and photovoltaic.
Main change in the grid mix of the U.S. electricity generation was a lower generation from natural
gas (down from 29.5% in 2012 to 26.9% in 2013) and higher production from hard coal (increase
from 36.1% in 2012 to 37.6% in 2013) and an increase of generation from wind power from 3.3%
in 2012 to 3.9% in 2013.
In the following tables the energy carrier mix for 2012 and 2013 are displayed for selected,
noteworthy countries, or those with important changes.
Table 2- 1: Energy carrier mix for electricity generation – selected EU countries
[%] France Germany Great Britain Italy Poland Spain
2012 2013 2012 2013 2012 2013 2012 2013 2012 2013 2012 2013
Nuclear 75.5 74.1 15.8 15.4 19.4 19.7 0.0 0.0 0.0 0.0 20.7 20.0
Lignite 0.0 0.0 25.6 25.5 0.0 0.0 0.3 0.3 33.3 34.1 1.0 0.9
Hard coal 3.4 3.8 18.5 19.3 39.4 36.4 16.2 15.3 49.7 49.6 17.5 13.6
Coal gases 0.5 0.5 1.6 1.7 0.3 0.3 1.7 1.2 1.1 1.2 0.3 0.5
Natural gas 3.9 3.0 12.4 10.9 27.5 26.6 43.2 37.7 3.9 3.2 24.7 20.1
Heavy fuel oil 0.8 0.4 1.2 1.1 0.8 0.6 6.3 5.4 1.3 1.1 5.2 4.9
Biomass (solid) 0.3 0.3 1.9 1.8 1.9 2.9 0.9 1.3 5.9 4.8 1.1 1.3
Biogas 0.2 0.3 4.4 4.7 1.6 1.7 2.6 3.9 0.3 0.4 0.3 0.3
Waste 0.8 0.7 1.8 1.9 1.1 1.2 1.5 1.6 0.0 0.0 0.5 0.4
Hydro 11.4 13.3 4.4 4.6 2.3 2.1 14.7 18.9 1.5 1.8 8.1 14.5
Wind 2.6 2.8 8.1 8.2 5.4 7.9 4.5 5.2 2.9 3.7 16.6 19.0
Photovoltaic 0.7 0.8 4.2 4.9 0.3 0.6 6.3 7.5 0.0 0.0 2.8 2.9
Solar thermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3 1.5
Geothermal 0.0 0.0 0.0 0.0 0.0 0.0 1.9 2.0 0.0 0.0 0.0 0.0
Peat 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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[%] Brazil China India Japan Russia USA
2012 2013 2012 2013 2012 2013 2012 2013 2012 2013 2012 2013
Nuclear 2.9 2.6 2.0 2.0 2.9 2.9 1.5 0.9 16.6 16.3 18.7 19.1
Lignite 1.4 1.6 0.0 0.0 14.0 14.3 0.0 0.0 6.1 5.9 2.2 2.1
Hard coal 0.2 1.0 75.2 74.2 56.9 58.4 25.8 28.5 9.2 8.9 36.1 37.6
Coal gases 1.0 1.2 0.6 1.2 0.1 0.1 3.5 3.7 0.4 0.4 0.1 0.1
Natural gas 8.5 12.1 1.7 1.7 8.3 5.5 38.4 38.4 49.1 50.0 29.5 26.9
Heavy fuel oil 3.5 4.7 0.1 0.1 2.0 1.9 17.5 14.3 2.6 0.8 0.8 0.9
Biomass (solid) 6.3 7.0 0.7 0.7 1.6 1.7 2.9 3.1 0.0 0.0 1.0 1.1
Biogas 0.1 0.1 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.3 0.3
Waste 0.0 0.0 0.2 0.2 0.1 0.1 0.8 0.8 0.3 0.3 0.5 0.5
Hydro 75.2 68.6 17.5 16.9 11.2 11.9 8.1 8.1 15.6 17.2 7.0 6.7
Wind 0.9 1.2 1.9 2.6 2.5 2.8 0.5 0.5 0.0 0.0 3.3 3.9
Photovoltaic 0.0 0.0 0.1 0.3 0.2 0.3 0.7 1.4 0.0 0.0 0.2 0.3
Solar thermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Geothermal 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 0.0 0.4 0.4
Peat 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
Table 2- 2: Energy carrier mix for electricity generation – countries with significant changes
[%] Chile Denmark Finland Latvia Portugal Romania
2012 2013 2012 2013 2012 2013 2012 2013 2012 2013 2012 2013
Nuclear 0.0 0.0 0.0 0.0 32.8 33.3 0.0 0.0 0.0 0.0 32.8 33.3
Lignite 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Hard coal 36.3 29.9 29.9 41.1 10.1 15.1 36.3 29.9 29.9 41.1 10.1 15.1
Coal gases 0.0 0.0 0.0 0.0 0.7 0.7 0.0 0.0 0.0 0.0 0.7 0.7
Natural gas 18.4 20.9 20.9 9.8 9.6 9.6 18.4 20.9 20.9 9.8 9.6 9.6
Heavy fuel oil 8.8 9.7 9.7 1.0 0.4 0.3 8.8 9.7 9.7 1.0 0.4 0.3
Biomass (solid) 7.0 7.1 7.1 8.8 15.3 16.2 7.0 7.1 7.1 8.8 15.3 16.2
Biogas 0.0 0.0 0.0 1.1 0.2 0.2 0.0 0.0 0.0 1.1 0.2 0.2
Waste 0.0 0.0 0.0 4.6 0.8 1.0 0.0 0.0 0.0 4.6 0.8 1.0
Hydro 28.9 32.0 32.0 0.0 24.1 18.1 28.9 32.0 32.0 0.0 24.1 18.1
Wind 0.6 0.5 0.5 32.0 0.7 1.1 0.6 0.5 0.5 32.0 0.7 1.1
Photovoltaic 0.0 0.0 0.0 1.5 0.0 0.0 0.0 0.0 0.0 1.5 0.0 0.0
Solar thermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Geothermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Peat 0.0 0.0 0.0 0.0 32.8 33.3 0.0 0.0 0.0 0.0 32.8 33.3
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The following list summarises countries with significant changes in the energy carrier mix for
electricity generation:
Brazil (BR) 3% incremental power consumption, mainly supplied from natural gas as
well as lower electricity output from hydro power stations, resulted in a drop of
hydropower from 75.2% in 2012 to 68.6% in 2013. The share of combustible, fossil fuels
increased from 9.8% in 2011 to 14.6% in 2012 and 20.6% in 2013.
Chile (CL) An ongoing trend to produce incremental electricity from coal increased
the share of coal in the electricity grid mix from 29.9% in 2011 to 36.3% in 2012 and
41.5% in 2013.
Croatia (HR) Due to higher water availability for power generation, the share of
electricity from hydropower increased from 45.5% in 2012 to 60.4% in 2013.
Consequently, the share of fossil fuels decreased from 50.5% to 34.8%.
Denmark (DK) Lower generation from the existing wind power installation (decrease
from 33.4% in 2012 to 32.0% in 2013) and lower generation from natural gas power
stations (decrease from 13.6% in 2012 to 9.8% in 2013) was compensated by higher
generation from coal (increase from 34.4% in 2012 to 41.1% in 2013).
Finland (FI) A considerable decrease in electricity output from hydropower (drop from
24.1% to 18.1% resulted in an increasing share of electricity from coal (up from 10.1%
in 2012 to 15. 1% in 2013.
Greece (GR) The grid mix in Greece was mainly changed by higher output from
hydropower (increase from 7.5% to 11.2%), considerable higher generation from
photovoltaic (increase from 2.8% to 6.4%) and wind power. Consequently, the share of
renewable energies increased from 17% in 2012 to 25.2% in 2013 and generation from
fossil fuels (mainly lignite) dropped from 82.9% to 74.7%.
Italy (IT) The share of electricity from renewable resources increased from 31.6% to
39.4%. The increase was driven by higher generation from existing hydro power stations
(14.7% in 2012 vs. 18.9% in 2013) as well as increasing capacity of photovoltaic and
biomass generation. The additional electricity from renewable resources replaced mainly
electricity from natural gas (drop from 43.2% to 37.7%).
Latvia (LV) Distinct lower output from hydro power station resulted in a decrease of
hydropower from 60.1% in 2012 to 46.9% in 2013. The lower output from hydro power
stations was mainly compensated by electricity from natural gas (33.3% in 2012 vs. 43%
in 2013).
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Lithuania (LT) The share of power generation from renewable resources increased
from 35.2% in 2012 to 45.9% in 2013. Wind power increased from 11.2% to 13.3%,
biomass from 8.6% to 10.7% and hydropower from 19.5% to 23.6%.
Portugal (PT) Higher water availability for hydropower, increased the share of
hydropower from 14.3% in 2012 to 28.8% in 2013. In addition, the share of wind power
further increased from 22.0% in 2012 to 23.2% in 2013. Generation from fossil fuels
dropped from 55.7% in 2012 to 40.2% in 2013.
Romania (RO) Generation from lignite dropped from 37.2% in 2012 to 28.1% in 2013.
The electricity was mainly substituted by hydro and wind power, increasing the share of
electricity from renewable resources from 25.7% in 2012 to 34.8% in 2013.
Spain (ES) Higher water availability for power generation increased the share of
hydropower from 8.1% in 2012 to 14.5% in 2013. In addition, generation from wind
increased considerably from 16.6% to 19.0%, increasing the overall generation from
renewable resources from 30.5% in 2012 to 39.8% in 2013.
Development GWP and other impact categories for electricity grid mix datasets
In order to give you a quantitative indication of the extent of changes on impact level, the
following figures illustrate the absolute primary energy demand (PED), as well as global warming
potential (GWP4), acidification potential (AP4), eutrophication potential (EP4) and photochemical
ozone creation potential (POCP4) per kWh of supplied electricity in Germany, the European
Union and the United States. In the 2017 edition databases, the emission factors for the
combustion of fuels in power plants have been kept unchanged compared to the 2016 edition,
with exception of the eGRID sub regions (Extension Module XVII: Full US - electricity grid mixes
for US sub grids and sub-regions under eGRID) for which new data from eGRID 2012 was
available. Therefore, the results are mainly influenced by the changes in the energy grid mix as
well as by changes in the power plant efficiencies and supply chains.
In Germany, the GWP for the electricity mix remained stable with 611 g CO2-eq./kWh in 2013
compared to 606 g CO2-eq./kWh in 2012. Although the electricity production from renewables
has grown by 5%, it has mainly substituted nuclear power with a low carbon intensity. The
increase in renewable PED is driven by the increase of electricity from renewable energy
sources. Changes in AP, EP and POCP are low.
4 CML 2001, Updated January 2016
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The increasing share of electricity from renewable resources increased considerably from 24.4%
to 27.4% in the EU, resulting in a 5% lower GWP for electricity (443 g CO2-eq./kWh in 2013 vs.
465 g CO2-eq./kWh in 2012). Changes in AP and EP are low and mainly influenced by the
changes in the grid mix. The POCP decreased by 9% due to the decreased production of
electricity from natural gas and coal.
In the U.S., the GWP remains unchanged at 613 g CO2-eq./kWh. AP und EP have been
increased by 4% due to higher use of coal in 2013.
Figure 2- 3: PED, GWP, EP, POCP and AP of electricity grid mixes DE, EU-28 and US
19
The following figures present the percentile changes of the greenhouse gases for the upgraded
electricity grid mixes in the GaBi Professional database and the Extension module Energy
compared to the 2012 data, as well as the absolute greenhouse gas emissions per kWh in the
2017 edition databases (reference year 2013).
Figure 2- 4: Changes in GWP of electricity grid mix datasets in GaBi Professional 2017 Edition
Figure 2- 5: Absolute GWP of electricity grid mix datasets in GaBi Professional 2016 & 2017 Edition
For most cases, the changes in the national electricity grid mix datasets are related to the
upgraded energy carrier mix or imports:
20
Belgium (BE) The GWP decreased compared to 2012 from 244 g CO2-eq./kWh to
220 g CO2-eq./kWh (relative decrease of 10%), mainly due to higher output from nuclear
power stations.
Cyprus (CY) The GWP in Cyprus has been reduced from 881 g CO2-eq./kWh in 2012
to 791 g CO2-eq./kWh in 2013. Reasons are the build-up of wind power capacities (share
increased from 3.9% to 5.4%) and a higher efficiency of fuel oil power plants (from 36%
to 39%).
Denmark (DK) The carbon intensity of the electricity supply in Denmark has risen
from 310 g CO2-eq./kWh in 2012 to 432 g CO2-eq./kWh in 2013. The reason for the large
40% increase in greenhouse gases per supplied kWh electricity is partly related to higher
generation from hard coal to substitute generation from natural gas and to compensate
lower generation from wind power installations. The most important aspect is that in
2012, Denmark has imported 35% of its electricity supply and thereof 90% from Sweden
and Norway with very low greenhouse gas emissions per unit of electricity. In 2013,
Denmark has imported only 26% of its supply and thereof 50% from Germany with
611 g CO2-eq./kWh compared to 38 g CO2-eq./kWh for Norway and 59 g CO2-eq./kWh
for Sweden.
Finland (FI) Compared to 2012, the GWP per supplied unit of electricity in Finland
has increased by 18% from 223 g CO2-eq./kWh in 2012 to 263 g CO2-eq./kWh in 2013.
The increase is related to lower electricity output from hydro power plants, mainly
compensated by electricity from hard coal.
Malta (MT) GWP for the electricity supply in Malta has decreased from 1,231 g CO2-
eq./kWh in 2012 to 995 g CO2-eq./kWh in 2013. The reasons are an increase of the
efficiency for used fuel oil power plants from 31% to 37% (gross) and decreased net
losses.
Norway (NO), Sweden (SE) The high relative GWP increase for Sweden and Norway
is a result of the high sensitivity of changes in the energy carrier mix on electricity grid
mixes with low carbon intensities. In both countries, a lower output of electricity from
hydro power stations resulted in a slight increasing usage of combustible, fossil fuel for
electricity generation.
Portugal (PT) The decreased GWP (396 g CO2-eq./kWh in 2013 compared to 500 g
CO2-eq./kWh in 2013) per produced unit of electricity is related to the higher output from
hydro power stations, reducing generation from coal and natural gas.
21
Romania (RO) The GWP in Romania has decreased from 640 g CO2-eq./kWh in 2012
to 502 g CO2-eq./kWh in 2013 for various reasons. Water availability for power
generation from hydro power stations was higher in 2013 compared to 2012, increasing
the share of hydropower from 21% in 2012 to 26% in 2013. Increasing share of electricity
from wind power and photovoltaic (4.5% in 2012 compared to 8.4% in 2013). The
additional electricity from renewable resources has substituted electricity from lignite,
reducing the share from 37.2% to 28.1%. In addition, the efficiency of natural gas power
plants has been increased from 42% to 54% according to IEA statistics.
Spain (ES) Similar to Portugal, the GWP per supplied unit of electricity in Spain has
considerably decreased (from 427 g CO2-eq./kWh in 2012 to 340 g CO2-eq./kWh in
2013) due to higher output from hydro power stations and additional increase of
production capacities from renewable resources (mainly wind power).
Figure 2-6 illustrates the GWP of the electricity supply in selected countries over the last five
years. Compared to 2008, the GWP in Germany has been reduced by 2%, in the EU by 9%. In
the U.S., the partial substitution of electricity from hard coal by electricity from natural as well as
a higher share of electricity from renewables has decreased the GWP per kWh of supplied
electricity by 8%. In some of the EU Member States, relevant GWP reductions have been
achieved over the last five years, e.g. Portugal -27%, Spain -24%, Romania -23%, Czech
Republic -18%, Denmark -17%.
Figure 2- 6: Development GWP for electricity supply in selected countries
The following two figures illustrate the relative and absolute changes of the GWP for the
electricity grid mix datasets in the extension module Energy.
22
Figure 2- 7: Changes in GWP electricity grid mix datasets in GaBi Extension module Energy 2017
Figure 2- 8: Absolute GWP of electricity grid mix datasets in GaBi Extension module Energy 2016 & 2017
23
Figure 2- 9: Development GWP for electricity supply in selected countries
Brazil (BR) The increasing GWP (283 g/kWh in 2012, 318 g/kWh in 2013) is a
consequence of the lower share of electricity from hydropower and higher electricity
generation from natural gas.
India (IN) The decreasing GWP in India from 1388 g/kWh in 2012 to 1183 g/kWh
despite stable energy mix is related to a relevant increase in coal power plant
efficiencies.
Further developments in electricity datasets
Changes in electricity data sets from specific fuels:
Power plant efficiencies, calculated based on IEA statistics, can significantly vary between the
reference years. The following reasons are considerations for variations over time:
final or periodic shutdown of specific power plants,
different share between CHP and direct production over time (e.g. different heat demand
over time),
technology measures to increase efficiency
irregular usage over time (e.g. used as reserve capacity),
rounding effects (if little fuel is used),
24
correction of statistical errors
a combination of several of the factors listed above
Inventories for primary energy carriers
In the GaBi databases 2017 Edition the reference year is 2013 for all energy carrier supply mixes
(e.g. hard coal, crude oil and natural gas). The changes of the energy carrier processes after
the upgrade are described in the following.
Primary energy carrier processes after the upgrade
Relevant changes in the environmental impact categories of the crude oil mix data sets are
related to the update of the country-specific crude oil mixes (mix of domestic production and
imports). In general, changing shares of crude oil imports from countries with low environmental
impacts (e.g. Norway) or high environmental impacts (e.g. Nigeria) affect the impacts. Except of
the crude oil mix of Ireland, the crude oil mixes show minor changes:
Crude oil mix of Ireland (IE) increasing shares of crude oil from Norway (22% in 2012,
43% in 2013) and Denmark (0% in 2012, 18% in 2013) and decreasing shares of crude
oil from Algeria (34% in 2012, 14% in 2012) and Nigeria (33% in 2012, 8% in 2013) result
in significant lower impact categories (e.g. GWP and AP -40%).
Changing shares of imports from countries with low environmental impacts (e.g. Norway) or high
environmental impacts also affect the environmental impacts of the natural gas mixes.
Furthermore, the transportation method of the imported natural gas (e.g. by tanker as liquefied
natural gas (LNG) or via pipeline) plays an important role. Hence, the LNG supply chain,
including gas liquefaction, LNG transport and regasification, has been updated. The following
natural gas mixes show notable changes in the impacts:
Natural gas mix of Belgium (BE) lower impacts (e.g. GWP: -19%) due to a decreasing
share of LNG from Qatar (23% in 2011, 8% in 2012) and increasing shares of natural
gas from Germany (0% in 2012, 7% in 2013) and Netherlands (34% in 2012, 45% in
2013).
Natural gas mix of Brazil (BR) decreasing share of domestic natural gas production
and increasing shares of natural gas via LNG transport result in higher impact categories
(e.g. GWP: +17%).
Natural gas mix of Japan (JP) The update of the LNG supply chain results in an
increase of the environmental impacts for Japan (e.g. GWP: +19%).
25
Natural gas mix of Romania (RO) Lower impacts (e.g. GWP: -16%) due to an
increasing share of domestic production and decreasing share of gas from Russia.
Natural gas mix of Taiwan (TW) The environmental impacts increase for Taiwan (e.g.
GWP: +15%) due to the update of the LNG supply.
Changes in the impacts of the lignite and hard coal mixes are due to the update of the country-
specific lignite and hard coal mixes (mix of domestic production and imports). The mixes show
minor changes with the exception of the hard coal mix of Romania:
Hard coal mix of Romania (HR) higher impacts (e.g. GWP: +34%) due to a decreasing
share of LNG from Qatar (21% in 2012, 14% in 2013) and an increasing share of natural
gas from Norway (21% in 2012, 29% in 2013).
The environmental impacts of the fuel mixes (diesel and gasoline, at refinery and filling station)
change considerably due to the following updates and improvements:
Creation of more detailed data sets of the biodiesel supply (palm and soy oil),
Update of biodiesel and bioethanol feedstock mixes,
Update of the country-specific mixes (domestic production and imports) of diesel and
gasoline,
Update of the country-specific blending quota of biofuels and
Update of the crude oil mixes.
Except of the Brazilian gasoline mix, the fuels show minor changes:
Gasoline mix of Brazil (BR) substantial impact changes are mostly related to the
supply chain of bioethanol from sugar cane.
Changes in the environmental impacts of the other refinery products, like aromatics or heavy
fuel oil, are related to changes in the background system (e.g. crude oil supply).
Inventories for organic and inorganic intermediates
Possible updates and upgrades of technologies may happen on 3 different levels, as explained
initially. In the upgraded organic and inorganic intermediates’ datasets, in most cases multiple
effects can be observed:
Due to possible breakthrough technologies (improvements in the foreground system of the
existing technology), due to changed situations in a production or consumption mix of different
26
technologies providing the same product, and last but not least due to changes and updates in
the background system of resources and energy supply.
The needed information to check and update the technologies and supply chains are based on
the knowhow of our engineers as well as on information shared by our customers that are active
in the chemical sector. The provided documentation of GaBi datasets serves as viable basis to
discuss supply chain aspects and demands.
Our experts use scientific and engineering knowhow (e.g. thermodynamic laws, the mass- and
energy conservation, stoichiometric balances, combustion calculation and alike) as basis to
maintain and update chemical LCA data. All chemical technologies were checked in this sense.
In relation to possible breakthrough technologies no major new technologies or significant
process improvements on existing technologies were identified by thinkstep experts in this
year’s upgrade.
Changes in the background system mainly relate to:
Upgraded distribution on primary, secondary and tertiary fossil resource extraction like
oil and gas
Upgraded market share of imported fossil resources
Upgraded distribution of the type of resources used (oil, gas and coal, etc.)
Increased amount of renewable feedstock and energy supply
Changes in the energy sector and supply chain are in most cases the drivers for overall
improvement throughout several impact categories. The intermediates are directly influenced by
the upgraded performance of the energy supply and the important resource, crude oil and
natural gas.
10 datasets were added to the Extension database Ia: organic intermediates. Among those are
two datasets for flame retardants HBCD (DE and EU-28). To the Extension database Ib:
inorganic intermediates 9 datasets for chlorine production in different countries and technologies
were added.
As to specific changes to the organic and inorganic intermediates, please see the details and
explanations in the following table that documents the entries in the JIRA system that thinkstep
uses to track and correct and changes, improvements and bugs:
27
Table 2- 3: JIRA issues organic and intermediates
JIRA
Tracking
Number
Issue
Category
Item Description Change in
results
Affects Extension
module
GC-1841 Improvement DE: Isononanol
dataset
The isononanol production
process has been updated.
The upstream process for
Isononanol was changed
from Butene-1 to Isooctene.
All indicators
except ADP
Elements
(+17%) are
reduced due to
this change.
-20% to -40%
for ADP fossil,
AP, EP, GWP
and POCP
-40% to -60%
for ODP
Extension database
Ia: organic
intermediates
GC-3472 Documentation/
Naming
Documentation:
DE: Methanol
from natural gas
(integrated
technologies)
Sentence added in
technology description.
Does not
change the
results.
Extension database
Ia: organic
intermediates
GC-4033 Improvement Harmonize FR:
Carbon
monoxide
allocation
The allocation of the data
set "FR: Carbon monoxide"
was changed from mass to
energy (net calorific value)
to be consistent with the
applied allocation in the
other country data sets.
Due to the
change of the
used allocation,
the LCIA
impacts
decrease by
about -65% in
all categories.
Extension database
Ib: inorganic
intermediates
GC-4511 Documentation/
Naming
Documentation:
Hexamethylene
diamine (HMDA)
- route of
Adiponitrile
In the documentation of all
country specific HMDA
datasets the production
route of Adiponitrile is now
specified.
Does not
change the
results.
Extension database
Ia: organic
intermediates
Inventories for metal processes
All data and models have been checked by thinkstep metals experts regarding technological
upgrades and were identified as representative for their technology descriptions.
As to specific changes to the metal processes, please see the details and explanations in the
following table that documents the entries in the JIRA system that thinkstep uses to track and
correct and changes, improvements and bugs:
Table 2- 4: JIRA issues for metal processes
JIRA
Tracking
Number
Issue Category Item Description Change in results Affects
Extension
module
GC-3535 Improvement Harmonize slag
waste treatment
for Chinese
antimony
After discussion with our internal
experts, it was decided to
harmonize the end of life
treatment of the slag produced
by the blast furnace during the
antimony metal production. The
slag treatment is now deposited
in an inert matter landfill, it used
Changes the
results as follows:
- from -1% to -4%
for ADP elements,
AP; POCP
- from -30% to -
35% for: ADP
fossil; GWP;
Primary energy
Extension
database V:
nonferrous
metals
28
to be modelled as vitrified and
macro encapsulated.
total and non-
renewable
- From -60% to -
99% for : EP; ODP;
Primary energy
renewable
GC-3573 Improvement Country specific
credit for steel
slag
Credits for blast furnace slag
have been harmonized using
country specific cement.
Does not change
the results.
Extension
database III:
iron and steel
Inventories plastic processes
The environmental profile of polymers is largely influenced by the monomer impacts. thinkstep
experts checked whether the polymerisation technologies are still representative.
To our knowledge no completely new process designs in polymerisation are in industrial use
compared to last year. The polymerisation technologies in the GaBi Databases are considered
representative. This is supported by our experience within the chemistry and polymer industry.
Inventories for end-of-life processes
All data and models have been checked by thinkstep end-of-life experts regarding technological
upgrades and were identified as representative for their technology descriptions in 2016.
Inventories for electronic processes
In this year’s upgrade, 26 new datasets for ICs (Integrated Circuits) were added. ICs in our extension
database moved from being representative components to specific ones.The foreground data of these
ICs was revised. The most important parameter (die size) has now specific values instead of a range.
Also, an extensive quality check has been performed on the underlying semiconductor technology
nodes manufacturing models.
The die sizes of most ICs have been updated. Previously, die size was estimated with an assumption
of die to package size ratio per package type (e.g. BGA, TSOP etc.). With the current update, we
applied a more valid approach. The updated die sizes are based on literature review and publically
available die sizes for some ICs. Where no information was found, thinkstep experts estimated the die
size with the thermal die pad size, because it provides more concrete realistic values of a true, existing
die size than the representative approach. Also the developments in IC industry over time is better to
be tracked and reflected in the more specific modelling approach, which is also important due to the
rapid cycles of Moore’s Law. See figure below for a representation of the thermal die pad inside an IC
package. This assumption for estimation of die size is conservative as the thermal pad size is usually
slightly bigger than the die itself.
29
Figure 2-1: Section and Isometric Cut Away View of two IC Packages 5
Because of these significant changes, this year’s upgrade includes:
New components for most existing original ICs (with new GUIDs)
o New materials added according to materials declarations collected in 2016
o Updated die sizes for each component, not based on package size ratio
Original components have „based on models 2004-2014“ added in their names
5 http://www.analog.com/media/en/technical-documentation/application-notes/AN-772.pdf
http://www.statschippac.com/~/media/Files/Package%20Datasheets/TQFP.ashx
30
Table 2- 5: Die size to housing ratio (left)
Table 2- 6: Die size in updated processes (right)
The following changes took place in semiconductor manufacturing technology nodes:
Improved representation of yield losses (line yield, cut die yield per wafer, wafer yield modelled
separately and specific to each node type)
Improved assessment of waste abatement processes, particularly acid waste neutralization
and ammonia treatment resulting in changes to input and output flows for ammonia and H2O2,
H2SO4, NH4OH and NaOH;
Corrections to water accounting
Additional process chemical inputs added, where new data became available for propylene
glycol monomethyl ether (PGME), propylene glycol monomethyl ether acetate (PGMEA),
phosphine, cresol, ethyl lactate, helium and ozone
Additional NMVOC emissions added to outputs
Bare silicon wafer included in the semiconductor manufacturing of each technology node
Adjustment to H2SO4 consumption during Piranha Clean process
Correction to N2 consumption in Hi-k Etch process
31
Figure 2-2 Die size ratio Database 2017 version vs. previous die size
Inventories for renewable materials processes
The datasets including renewable materials (e.g. crops cultivation) are modelled with a
comprehensive agricultural model. The model considers local and regional aspects of climate,
soil and farming practices on the technical side. Further it takes into account international
guidelines, current scientific literature and available databases on the methodological side. The
thinkstep agriculture and farming experts maintain and enlarge the model frequently, becoming
one of the most advanced LCA models related to this topic.
As part of the 2017 upgrade the agrarian and renewable processing datasets have been
reviewed and updated based on the most recent information identified by the thinkstep experts
considering the aspects previously mentioned. The documentation of some datasets was also
improved.
This year’s update focussed on:
- Apple plantation in China
- Cork cultivation in Europe
32
- Seeds grain and oil seed with global applicability
On the methodological side, the background data of the agrarian model used in all crop datasets
has been improved. Information about the NH4-N content of the organic fertilizer were
consistently adapted.
The carbon balance was harmonized in all the foreground and background systems where
renewable materials are involved, especially if economic allocation has been used. The primary
energy data has been harmonized and corrected in all the wood datasets where an allocation
based on a different reference than mass has been applied.
As to specific changes to the renewable materials processes, please see the details and
explanations in the following table that documents the entries in the JIRA system that thinkstep
uses to track and correct and changes, improvements and bugs
Table 2- 7: JIRA issues for renewable processes
JIRA
Tracking
Number
Issue
Category
Item Description Change in results Affects
Extension
module
GC-2535 New dataset New dataset:
"Cashew nut
(mass allocation,
factory gate,
ready-to-eat)"
A new dataset for ready-to-eat
cashew nuts is now available.
New datasets Extension
database XX:
food & feed
GC-2536 New dataset New dataset:
Dried shea
kernels
A new dataset "GLO: Dried
shea kernels" is now available.
New datasets Extension
database XII:
renewable
materials
GC-3272 Bug Sawmill -
Moisture content
The moisture content in the
saw mill model was corrected.
Moisture water content as
absolute dry is 1% based on
literature information.
Does not change
the results.
All
GC-3446 Bug Slaughterhouse:
SO2 emissions
SO2 emissions in the
slaughterhouse model were
corrected, the emissions are
now lower.
Slaughterhouse
products now have
a lowered impact in
categories using
SO2.
Extension
database XII:
renewable
materials
Extension
database XVII:
full US
Extension
database XVI:
seat covers
33
GC-3450 Bug Water balance in
US orange
cultivation
The water balance of US:
Orange cultivation was
corrected.
Impact decreased
when evaluating
water quantities.
Extension
database XX:
food & feed
GC-3547 Bug Carbon balance:
Glucose syrup
The carbon balance of glucose
syrup processes was corrected.
Changes the
results only when
evaluating GWP.
Extension
database XX:
food & feed
GC-3612 Bug Slaughterhouse:
Price for bovine
blood
The price for bovine blood used
in the BR model now uses the
same price as the EU and US
model. This was done based
on literature sources. The old
price used for bovine blood is
0.06€/kg, the new price is
9.6€/kg.
The documentation of the
bovine blood process was
improved, it is now clearly
stated that: the prices for
bovine blood are estimation
based on the price for
processed blood, it is assumed
that the blood is properly
collected in the slaughterhouse
facilities and it is a valuable by-
products
Impacts changes
are very high for
the bovine blood
process, this is due
to the change of
prices from 0.06
€/kg to 9.6 €/kg.
The results are in
range with the
bovine blood
produced in US and
EU.
For by-products
such as semi-bone
beef, boneless
beef, salted hide
and fresh hide,
most of the LCIA
categories are
reduced by 15%.
Extension
database XII:
renewable
materials
GC-3932 Improvement Cotton fibres
dataset
- The carbon content of the
product was estimated with
42%
- The name of the process and
plan were adapted. New name
is DE: Cotton fibres (from
recycled clothes)
- The documentation of the
process was adapted, so it is
clear that the carbon uptake is
considered "The carbon uptake
(carbon stored in the product) is
considered in the dataset"
A new p-agg process was
added to Extension database
XII: Renewable materials 2017
(newly added processes only)
By correcting the
CO2 biogenic
uptake GWP
including biogenic
carbon changes
accordingly to the
uptake
Extension
database XII:
renewable
materials
34
GC-4036 Bug Water output
flow in Coconut
Oil
The water flow was changed to
"Water (river water from
techno-sphere, waste water).
The flow used before "Turbined
water to river“ is a turbined
water flow that is to be used
exclusively by the hydro-power.
Does not change
the results.
Extension
database XX:
food & feed
GC-4063 Bug Corn process
used in grains
milling
If corn grains are milled, the
water content of grains should
be around 12%. When the
water content is higher the
grains cannot be stored or
transported because fungi or
bacteria can grow in the grains.
Therefore the corn grains
cultivation with 25% water
content was exchanged with
the corn grains cultivation with
12%.
EU-28: Corn bran (corn wet
mill) (economic allocation)
EU-28: Corn bran (corn wet
mill) (mass allocation)
EU-28: Corn oil (corn wet mill)
(economic allocation)
EU-28: Corn oil (corn wet mill)
(mass allocation)
EU-28: Corn steep liquor (corn
wet mill) (economic allocation)
EU-28: Corn steep liquor (corn
wet mill) (mass allocation)
EU-28: Gluten feed (corn wet
mill) (economic allocation)
EU-28: Gluten feed (corn wet
mill) (mass allocation)
EU-28: Gluten meal (corn wet
mill) (economic allocation)
EU-28: Gluten meal (corn wet
mill) (mass allocation)
US: Corn bran (corn wet mill)
(economic allocation)
US: Corn bran (corn wet mill)
(mass allocation)
US: Corn oil (corn wet mill)
(economic allocation)
US: Corn oil (corn wet mill)
By applying this
change the most
common impact
categories changed
by 5% - 15%.
The GWP including
biogenic carbon
changed according
to the carbon
content of the
product
Extension
database XX:
food & feed
35
(mass allocation)
US: Corn steep liquor (corn wet
mill) (economic allocation)
US: Corn steep liquor (corn wet
mill) (mass allocation)
US: Dried starch (corn wet mill)
(mass allocation)
US: Gluten feed (corn wet mill)
(mass allocation)
US: Gluten meal (corn wet mill)
(mass allocation)
GC-4107 New dataset New dataset:
Amino acid from
Evonik
Five new datasets for amino
acids from Evonik are now
available in the Extension
database XX: food & feed:
US: Biolys®
HU: ThreAMINO®
SK: ValAMINO®
SK: TrypAMINO®
BE: MetAMINO®
New datasets Extension
database XX:
food & feed
GC-4236 Documentation/
Naming
Documentation:
"EU-28:
Rapeseed meal"
The included datasets now
show the correct processes.
Does not change
the results.
Extension
database XX:
food & feed
GC-4335 Bug Output flow in
dataset "DE:
wheat bran
(wheat mill)
(economic
allocation)"
The output flow is now wheat
bran instead of wheat white
flour.
Does not change
the results.
Extension
database XX:
food & feed
Inventories for construction materials and processes
Foreground data and models have been checked by thinkstep construction experts regarding
technological upgrades and passed. Identified technology improvements were updated in the
database. In total 31 EPDs datasets have been included in the Extension database XIV:
construction materials.
Further changes leading back to the background system (energy, intermediates) are responsible
for the remaining differences between GaBi Databases 2016 and 2017 for construction.
As to specific changes to the construction materials and processes, please see the details and
explanations in the following table that documents the entries in the JIRA system:
36
Table 2- 8: JIRA issues for construction processes
JIRA
Tracking
Number
Issue
Category
Item Description Change in results Affects
Extension
module
GC-3176 Documentation/
Naming
Included
datasets and
flowchart of
some
construction
database
processes
Information included datasets
and flowchart of construction
database processes was
improved.
Does not change
the results.
Extension
database XIV:
construction
materials
GC-3290 Bug Gypsum amount
in Portland
cement
The amount of gypsum has
been corrected. Now the mass
balance is correct again. 5% of
gypsum are now correctly used
instead of 5.1%
Very small changes
in ADP elements.
Extension
database XIV:
construction
materials
GC-3454 Improvement Bonding agent
for corkboard
A bonding agent (6%) was
added.
Impacts generally
increase. GWP
increases by about
10%.
Extension
database XIV:
construction
materials
GC-3556 New dataset New EPD
dataset:
Concrete paving
stone -SLG
A new dataset "DE: Concrete
paving stone (with facing
concrete, grey)" from SLG for
surface of roads and
pavements is now available
New datasets Extension
database XIV:
construction
materials
GC-3593 New dataset Recycling
potential for
steel profile and
stainless steel
sheet
New processes for Recycling
potential of steel profiles were
created.
New datasets Extension
database XIV:
construction
materials
GC-3600 New dataset New EPD
dataset:
Oriented Strand
Board
(Kronoply)
New dataset available:
"EU-28: Oriented Strand Board
(OSB) (4,5% Humidity) -
Kronoply (A1-A3)"{33b800a7-
ceb2-4340-b604-
1e1cc74cc737}
New datasets Extension
database XIV:
construction
materials
GC-3604 New dataset New EPD
dataset:
Laminate
Flammex -
Egger
New EPD dataset for Laminate
Flammex from Egger is now
available.
New datasets Extension
database XIV:
construction
materials
GC-3605 New dataset New EPD
dataset:
Coloured
laminate - Egger
New EPD dataset for Coloured
laminate from Egger is now
available.
New datasets Extension
database XIV:
construction
materials
37
GC-3606
GC-3607
GC-3608
New dataset New EPD
datasets: Cut,
dried and planed
lumber - Egger
New EPD datasets for Cut,
dried and planed lumber from
Egger are now available
New datasets Extension
database XIV:
construction
materials
GC-3645 New dataset New datasets:
Recycling
potential
15 new datasets for recycling
potential of steels for DE, UA
and CN are now available.
German datasets consider
modules C4 and D.
New datasets Extension
database XIV:
construction
materials
GC-3799 New dataset New EPD
dataset:
Laminate from
ALLOC
A new EPD dataset "EU-28:
High pressure laminate (HPL)"
is now available.
New datasets Extension
database XIV:
construction
materials
GC-3800 New dataset New EPD
dataset:
Laminate from
MeisterWerke
Schulte GmbH
A new dataset "EU-28: Direct
pressure laminate (DPL) (1m²)"
from MeisterWerke Schulte
GmbH is now available.
New datasets Extension
database XIV:
construction
materials
GC-3950 Improvement Transport
processes
including fuel
The processes "EU-28:
Articulated lorry transport incl.
fuel",
"EU-28: Lorry transport incl.
fuel" and "EU-28: Small lorry
transport incl. fuel" now use a
consumption mix of Euro 0 - 5
trucks instead of only Euro 3.
Changes are most
dominant in
Acidification and
Eutrophication,
both decrease by
about 30%. POCP
decreases by about
40%.
Extension
database XIV:
construction
materials
GC-3951 Bug Update "EU-28:
Barge incl. fuel"
The process "EU-28: Barge
incl. fuel" now uses the correct
base process "barge".
The impacts
decrease by about
10%.
Extension
database XIV:
construction
materials
GC-3954 Bug DE: Double
glazing unit
(EN15804 A1-
A3): Process
output flow
reference
The process's output flow is
"Insulation glass composite".
The reference unit is 1 sqm, but
the conversion factor to mass
was set to 0.0667 kg/m2. This
has now been corrected to 15
kg/m2.
Does not change
the results.
Extension
database XIV:
construction
materials
GC-3965 New dataset New EPD
datasets: Steel
screws - EJOT
EPD Datasets for Steel screws,
stainless steel screws and bi-
metal screws are now
available.
New datasets Extension
database XIV:
construction
materials
GC-3987 Documentation/
Naming
Documentation:
Sand 0/2
In the documentation spelling
mistakes were corrected.
Does not change
the results.
Extension
database XIV:
construction
materials
38
GC-4185 New dataset New EPD
datasets - Glass
from Schott
EPD datasets including
production, installation and end
of life for safety glass,
insulating glass and laminated
glass from Schott are now
available.
New datasets Extension
database XIV:
construction
materials
GC-4192 New dataset New EPD
dataset: Smoke
Damper from
Wildeboer
A new EPD dataset for smoke
control damper is now
available.
New datasets Extension
database XIV:
construction
materials
GC-4256 Improvement Lightweight
block density
and reference
values per m3
The lightweight concrete
datasets have been
harmonized. Naming has been
adapted to correctly reflect the
dataset. Additionally for the
concrete elements, the weight
now correctly takes water into
consideration. A table with the
correct input materials was
added to the processes, as well
as a table with the density
classes.
Does not change
the results.
Extension
database XIV:
construction
materials
GC-4332 Brick and
roofing tile
datasets
Improvement Using updated information, the
energy input was increased for
brick production, and lowered
for roofing tiles production.
Facing bricks: GWP
increases by about
30%, in China by
about 70%. Roofing
tile: GWP
decreases by about
20%
Extension
database XIV:
construction
materials
GC-4458 New dataset New EPD
dataset: Sand-
lime brick from
Kalksandsteinve
rband e.V.
A new EPD dataset for sand-
lime brick from
Kalksandsteinverband e.V. is
now available.
New datasets Extension
database XIV:
construction
materials
GC-4489 Bug Parquet sealing
dataset: amount
of applied
coating
An error in the formula that
governs the amount of
sealing/coating used was
corrected. Now for the density
instead of 1 g/cm³ 1000 kg/m³
are used.
This affects the following
datasets:
DE: Multi layer parquet
(EN15804 A1-A3)
The changes are
visible in the multi-
layer and strip
parquet datasets:
* plus 1-2% for AP,
EP and POCP
* plus 7-9% for
ADP fossil
* plus 5% for GWP
(reduction of credits
Extension
database XIV:
construction
materials
39
EU-28: Multi layer parquet
(EN15804 A1-A3)
BR: Strip parquet
UA: Strip parquet
CN: Strip parquet
DE: Strip parquet (EN15804
A1-A3)
EU-28: Strip parquet (EN15804
A1-A3)
due to incorporation
of renewable
carbon)
GC-4569 Sorting Reference year
and validity of
EPD processes
The validity year of the EPDs
were checked and if necessary
adjusted. EPD datasets where
the validity expired were moved
to the folder "EPDs with expired
validity".
Does not change
the results.
Extension
database XIV:
construction
materials
GC-4664 Bug Turk Ytong EPD:
input flow
correction
The input flow "Quartz sand
(silica sand; silicon dioxide)
[Non renewable resources]" is
no longer a valuable substance
flow.
Does not change
the results.
Extension
database XIV:
construction
materials
Inventories for textile processes
No major technology changes are identified in the foreground system. Therefore the data is
representative for the current situation. Changes are mainly induced by the background system,
such as electricity grid mixes.
Many different chemicals are used in textile finishing industry processes; most in relatively small
amounts. The datasets represent the production of those textile chemicals focusing on the use
in the textile industry. The datasets are an estimation and suitable within the scope of textile
production, but may not be used outside this scope. The datasets may not cover all relevant
process steps / technologies over the supply chain of the represented cradle-to-gate inventory
and have a medium to low overall data quality. The inventories are primarily based on secondary
data.
As to specific changes to the textile processes, please see the details and explanations in the
following table that documents the entries in the JIRA system:
40
Table 2- 9: JIRA issues for textile processes
JIRA
Tracking
Number
Issue
Category
Item Description Change in results Affects
Extension
module
GC-4455 Improvement Consumption
Automotive part
dataset
The dataset "Consumption
Automotive part" has been
renamed to "Mass-induced
fuel consumption of
automotive part (NEDC)". The
sulphur content of the fuel has
been adapted. Density of the
fuel is now taken directly from
the corresponding flow. The
parameter mva now has
recommended default values
in the comment.
This is a unit process.
Depending on the user
settings, results will
change.
Extension
database
XVI: seat
covers
GC-4491 Documentation/
Naming
Documentation:
Sheep wool
yarn
In the documentation tabs
"general comment" and "usage
advice", additional comments
were added to explain the user
that the data set "Sheep wool
yarn (from New Zealand (NZ)
sheep wool)" should only be
used along the production
chain, e.g. of a car.
Does not change the
results.
Extension
database
XV: textile
finishing
Extension
database
XVI: seat
covers
Inventories for US regional processes
The datasets in the US extension database have been checked by thinkstep experts on their
technological validity and passed. 36 new datasets were added to the Extension database XVII:
full US.
As to specific changes to the US regional processes, please see the details and explanations in
the following table that documents the entries in the JIRA system that thinkstep uses to track
and correct and changes, improvements and bugs:
41
Table 2- 10: JIRA issues for US regional processes
JIRA
Tracking
Number
Issue
Category
Item Description Change in results Affects
Extension
module
GC-1448 New dataset New datasets:
Hardwood
veneer from
AHEC
Three new datasets for
hardwood veneer datasets
from AHEC are now available.
New datasets Extension
database XVII:
full US
GC-2615 New dataset New EPD
datasets:
Structural steel
fabrication
(AISC)
Two new EPD datasets "US:
Fabricated hot-rolled
structural steel sections" and
"US: Fabricated steel plate"
from the American Institute of
Steel Construction (AISC) are
now available.
New datasets Extension
database XVII:
full US
GC-2630 New dataset New EPD
datasets: Steel
deck and steel
joist (SDI and
SJI)
Two new EPD datasets from
the Steel Deck Institute „US:
Steel deck - Steel Deck
Institute (SDI) (A1-A3)" and
from the Steel Joist Institute
"US: Steel joist - Steel Joist
Institute (SJI) (A1-A3)" are
now available.
New datasets Extension
database XVII:
full US
GC-3467 Improvement US Carbon
monoxide -
synthetic gas
route change
For the US carbon monoxide
production, the synthesis gas
route was changed by
replacing the feed-stock
source, Synthesis gas
(CO:H2 = 1:1) from light fuel
oil was replaced by synthesis
gas (H2: CO = 3: 1) from
natural gas via steam
reforming. As all other country
specific carbon monoxide
data using synthesis gas
based on natural gas, the US
one is now consistent to the
other country data sets.
Steam reforming is the
common method to produce
hydrogen: carbon monoxide
Due to this change, the
common impact
methods has been
changed as follows:
EP is reduced by ca. -
10%
AP is reduced by ca. -
50%
GWP is increased by ca.
120%
POCP is increased by
ca. 20%
Primary energy demand
is increased by ca. 45%
Note: thinkstep noticed
an inconsistency in the
Synthesis gas (CO:H2 =
Extension
database XVII:
full US
42
mixtures for manufacturing
important base chemicals and
other industrial applications,
using predominantly natural
gas as feedstock.
1:1) from light fuel oil
route, which was also
fixed, see further
information in this
document in the entry
GC-4586. The
environmental impact for
syngas from light fuel oil
has been increased in
parallel to this issue
within the GaBi
Database Update. But
this explains why the
change of the synthesis
gas route shows difficult
results for the 2016 US
carbon monoxide versus
the 2017 US carbon
monoxide version.
GC-3690 New dataset New EPD
datasets: Steel
EPDs from
CMC
Five new EPD datasets for
steel production were added
to Extension database XVII:
Full US 2017
New datasets Extension
database XVII:
full US
GC-3694 Documentation/
Naming
Region code
RNA instead of
NA
The nation code for the
processes "Aluminum
specialty product- CISCA (A1-
A3 & A5)" and "Steel specialty
product- CISCA (A1-A3 &
A5)" was changed from NA to
RNA.
Does not change the
results.
Extension
database XVII:
full US
GC-4068 Documentation/
Naming
Aluminum
naming for US
datasets
Naming was changed to
aluminum for US datasets
Does not change the
results.
Extension
database XVII:
full US
GC-4287 Improvement AA can
datasets
The processes:
"US: Can manufacturing"
{03146d40-8dd6-4a76-809c-
cd23d70f9b8e} and "RNA:
Secondary Aluminum Ingot
(from clean can scrap)"
For the process
Secondary ingot and
can manufacturing the
impacts generally
decrease.
Extension
database XVII:
full US
43
{19d111e1-07a7-4c13-afbc-
139b9a2cc0cd}
were updated.
The process "US: Aluminum
can sheet rolling" {72eba6dd-
dcd0-4651-9a11-
a6dc6a6bee96} is a new
dataset which replaces the
dataset:
"US: Aluminum Can sheet
rolling" {4bca7cfb-e1ee-4443-
b4e6-f64d609706ea}, which
was moved to the Version
2016 folder.
GC-4333 Bug Hardwood
datasets in
Extension
database XVII:
full US
The allocation method applied
in the models was not be
changed (price allocation).
Since a price allocation can
skew carbon dioxide uptake
and primary energy demand,
a carbon and primary energy
correction was applied.
57 plans were modified
* carbon balance was
corrected
* primary energy demand
value was corrected
* the carbon content of the
wood as well as the water
content were added to the
flow properties
Changes results when
using Global warming
potential and primary
energy.
Extension
database XVII:
full US
GC-4344 Documentation/
Naming
Documentation:
lumber datasets
Transport description was
changed to the following:
"Transports within the process
chain are accounted here.
Transport from the forest to
the sawmill and from thereon
to thermal treatment plant is
done by truck, transport from
the thermal treatment plant to
the port is done by truck and
by train. The distances
Does not change the
results.
Extension
database XVII:
full US
44
represent the industry
average distances for the
hardwood logs and green
lumber in US."
GC-4604
Bug Missing flow
connection on
plan "Electricity,
at grid, Eastern
US"
The "Electricity, at grid,
Eastern US" {B7A128DD-
5A68-4E6F-909C-
546A65BC01DD} had a
missing flow connection. This
is now corrected.
Does not change the
results.
Extension
database XVII:
full US
45
3. Industry data in GaBi
Despite the fact, that several associations have updated their data, some associations did not
update this year. Since they have an own cycle for upgrading their data, these processes
cannot be updated by thinkstep in the yearly upgrade without permission. thinkstep must
keep these processes identical to those in the GaBi Databases 2016 Edition until the
associations decide to update and make them available in our system. However, several new
association datasets use the GaBi database to reach global customers.
New sources of industry data added in GaBi Databases 2017 Edition:
From APEAL (http://www.apeal.org):
RER: Steel tinplate
From Cobalt Development Institute (CDI) (http://www.thecdi.com/):
GLO: Cobalt, refined (metal)
GLO: Cobalt, refined (metal) ORIGINAL
From EVONIK (http://www.evonik.com)
(Note: Only available in the Extension database XX: food & feed 2017)
US: Biolys®
BE: MetAMINO®
HU: ThreAMINO®
SK: TrypAMINO®
SK: ValAMINO®
From International Zinc Association (IZA) (http://www.zinc.org)
GLO: Special high grade zinc
From PU Europe (http://www.pu-europe.eu)
EU-28: Aromatic Polyester Polyols (APP) production mix
From Forest and Wood Products Australia (FWPA) (http://www.fwpa.com.au/)
AU: Energy recovery from hardwood timber, green, dressed, untreated (EN 15804 C3)
AU: Energy recovery from hardwood timber, green, dressed, untreated (EN 15804 D)
46
AU: Energy recovery from hardwood timber, green, rough-sawn, untreated (EN 15804 C3)
AU: Energy recovery from hardwood timber, green, rough-sawn, untreated (EN 15804 D)
AU: Energy recovery from hardwood timber, kiln-dried, dressed, untreated (EN 15804 C3)
AU: Energy recovery from hardwood timber, kiln-dried, dressed, untreated (EN 15804 D)
AU: Energy recovery from hardwood timber, kiln-dried, rough-sawn, untreated (EN 15804 C3)
AU: Energy recovery from hardwood timber, kiln-dried, rough-sawn, untreated (EN 15804 D)
AU: Energy recovery from MDF, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 C3)
AU: Energy recovery from MDF, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 D)
AU: Energy recovery from MDF, moisture resistant (MR), E1, melamine coated, 25 mm (EN 15804 C3)
AU: Energy recovery from MDF, moisture resistant (MR), E1, melamine coated, 25 mm (EN 15804 D)
AU: Energy recovery from MDF, standard, E1, melamine coated, 18 mm (EN 15804 C3)
AU: Energy recovery from MDF, standard, E1, melamine coated, 18 mm (EN 15804 D)
AU: Energy recovery from MDF, standard, E1, melamine coated, 25 mm (EN 15804 C3)
AU: Energy recovery from MDF, standard, E1, melamine coated, 25 mm (EN 15804 D)
AU: Energy recovery from particleboard, flooring (tongue & groove), 19 mm (EN 15804 C3)
AU: Energy recovery from particleboard, flooring (tongue & groove), 19 mm (EN 15804 D)
AU: Energy recovery from particleboard, flooring (tongue & groove), 22 mm (EN 15804 C3)
AU: Energy recovery from particleboard, flooring (tongue & groove), 22 mm (EN 15804 D)
AU: Energy recovery from particleboard, flooring (tongue & groove), 25 mm (EN 15804 C3)
AU: Energy recovery from particleboard, flooring (tongue & groove), 25 mm (EN 15804 D)
AU: Energy recovery from particleboard, MR, E1, melamine coated, 16 mm (EN 15804 C3)
AU: Energy recovery from particleboard, MR, E1, melamine coated, 16 mm (EN 15804 D)
AU: Energy recovery from particleboard, MR, E1, melamine coated, 18 mm (EN 15804 C3)
AU: Energy recovery from particleboard, MR, E1, melamine coated, 18 mm (EN 15804 D)
AU: Energy recovery from particleboard, standard, E1, melamine coated, 16 mm (EN 15804 C3)
AU: Energy recovery from particleboard, standard, E1, melamine coated, 16 mm (EN 15804 D)
AU: Energy recovery from particleboard, standard, E1, melamine coated, 18 mm (EN 15804 C3)
AU: Energy recovery from particleboard, standard, E1, melamine coated, 18 mm (EN 15804 D)
AU: Energy recovery from plywood, exterior, A-bond, 7 mm (bracing) (EN 15804 C3)
AU: Energy recovery from plywood, exterior, A-bond, 7 mm (bracing) (EN 15804 D)
AU: Energy recovery from plywood, exterior, A-bond, 9 mm (structural) (EN 15804 C3)
AU: Energy recovery from plywood, exterior, A-bond, 9 mm (structural) (EN 15804 D)
AU: Energy recovery from plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (EN 15804 C3)
AU: Energy recovery from plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (EN 15804 D)
AU: Energy recovery from plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (EN 15804 C3)
AU: Energy recovery from plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (EN 15804 D)
AU: Energy recovery from plywood, formply, A-bond, 17 mm (formwork) (EN 15804 C3)
47
AU: Energy recovery from plywood, formply, A-bond, 17 mm (formwork) (EN 15804 D)
AU: Energy recovery from plywood, interior, C-bond, 9 mm (joinery) (EN 15804 C3)
AU: Energy recovery from plywood, interior, C-bond, 9 mm (joinery) (EN 15804 D)
AU: Energy recovery from softwood timber, kiln-dried, dressed, untreated (EN 15804 C3)
AU: Energy recovery from softwood timber, kiln-dried, dressed, untreated (EN 15804 D)
AU: Energy recovery from softwood timber, kiln-dried, rough-sawn, untreated (EN 15804 C3)
AU: Energy recovery from softwood timber, kiln-dried, rough-sawn, untreated (EN 15804 D)
AU: Hardwood timber, green, dressed, untreated (EN 15804 A1-A3)
AU: Hardwood timber, green, rough-sawn, untreated (EN 15804 A1-A3)
AU: Hardwood timber, kiln-dried, dressed, untreated (EN 15804 A1-A3)
AU: Hardwood timber, kiln-dried, rough-sawn, untreated (EN 15804 A1-A3)
AU: Landfill of hardwood timber, green, dressed, untreated (NGA) (EN 15804 C4)
AU: Landfill of hardwood timber, green, dressed, untreated (NGA) (EN 15804 D)
AU: Landfill of hardwood timber, green, dressed, untreated (typical) (EN 15804 C4)
AU: Landfill of hardwood timber, green, dressed, untreated (typical) (EN 15804 D)
AU: Landfill of hardwood timber, green, rough-sawn, untreated (NGA) (EN 15804 C4)
AU: Landfill of hardwood timber, green, rough-sawn, untreated (NGA) (EN 15804 D)
AU: Landfill of hardwood timber, green, rough-sawn, untreated (typical) (EN 15804 C4)
AU: Landfill of hardwood timber, green, rough-sawn, untreated (typical) (EN 15804 D)
AU: Landfill of hardwood timber, kiln-dried, dressed, untreated (NGA) (EN 15804 C4)
AU: Landfill of hardwood timber, kiln-dried, dressed, untreated (NGA) (EN 15804 D)
AU: Landfill of hardwood timber, kiln-dried, dressed, untreated (typical) (EN 15804 C4)
AU: Landfill of hardwood timber, kiln-dried, dressed, untreated (typical) (EN 15804 D)
AU: Landfill of hardwood timber, kiln-dried, rough-sawn, untreated (NGA) (EN 15804 C4)
AU: Landfill of hardwood timber, kiln-dried, rough-sawn, untreated (NGA) (EN 15804 D)
AU: Landfill of hardwood timber, kiln-dried, rough-sawn, untreated (typical) (EN 15804 C4)
AU: Landfill of hardwood timber, kiln-dried, rough-sawn, untreated (typical) (EN 15804 D)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (NGA) (EN 15804 C4)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (NGA) (EN 15804 D)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (typical) (EN 15804 C4)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (typical) (EN 15804 D)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (NGA) (EN 15804 C4)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (NGA) (EN 15804 D)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (typical) (EN 15804 C4)
AU: Landfill of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (typical) (EN 15804 D)
AU: Landfill of MDF, standard, E1, melamine coated, 18 mm (NGA) (EN 15804 C4)
AU: Landfill of MDF, standard, E1, melamine coated, 18 mm (NGA) (EN 15804 D)
48
AU: Landfill of MDF, standard, E1, melamine coated, 18 mm (typical) (EN 15804 C4)
AU: Landfill of MDF, standard, E1, melamine coated, 18 mm (typical) (EN 15804 D)
AU: Landfill of mDF, standard, E1, melamine coated, 25 mm (NGA) (EN 15804 C4)
AU: Landfill of MDF, standard, E1, melamine coated, 25 mm (NGA) (EN 15804 D)
AU: Landfill of MDF, standard, E1, melamine coated, 25 mm (typical) (EN 15804 C4)
AU: Landfill of MDF, standard, E1, melamine coated, 25 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 19 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 19 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 19 mm (typical) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 19 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 22 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 22 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 22 mm (typical) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 22 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 25 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 25 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, flooring (tongue & groove), 25 mm (typical) (EN 15804 C4)
AU: Landfill of particleboard, flooring (tongue & groove), 25 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, MR, E1, melamine coated, 16 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, MR, E1, melamine coated, 16 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, MR, E1, melamine coated, 18 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, MR, E1, melamine coated, 18 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, standard, E1, melamine coated, 16 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, standard, E1, melamine coated, 16 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, standard, E1, melamine coated, 16 mm (typical) (EN 15804 C4)
AU: Landfill of particleboard, standard, E1, melamine coated, 16 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, standard, E1, melamine coated, 18 mm (NGA) (EN 15804 C4)
AU: Landfill of particleboard, standard, E1, melamine coated, 18 mm (NGA) (EN 15804 D)
AU: Landfill of particleboard, standard, E1, melamine coated, 18 mm (typical) (EN 15804 C4)
AU: Landfill of particleboard, standard, E1, melamine coated, 18 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, moisture resistant (MR), E1, melamine coated, 16 mm (typical) (EN 15804 D)
AU: Landfill of particleboard, moisture resistant (MR), E1, melamine coated, 16 mm (typical)(EN 15804 C4)
AU: Landfill of particleboard, moisture resistant (MR), E1, melamine coated, 18 mm (typical) (EN 1580 C4)
AU: Landfill of particleboard, moisture resistant (MR), E1, melamine coated, 18 mm (typical) (EN 15804 D)
AU: Landfill of plywood, exterior, A-bond, 7 mm (bracing) (NGA) (EN 15804 C4)
AU: Landfill of plywood, exterior, A-bond, 7 mm (bracing) (NGA) (EN 15804 D)
AU: Landfill of plywood, exterior, A-bond, 7 mm (bracing) (typical) (EN 15804 C4)
49
AU: Landfill of plywood, exterior, A-bond, 7 mm (bracing) (typical) (EN 15804 D)
AU: Landfill of plywood, exterior, A-bond, 9 mm (structural) (NGA) (EN 15804 C4)
AU: Landfill of plywood, exterior, A-bond, 9 mm (structural) (NGA) (EN 15804 D)
AU: Landfill of plywood, exterior, A-bond, 9 mm (structural) (typical) (EN 15804 C4)
AU: Landfill of plywood, exterior, A-bond, 9 mm (structural) (typical) (EN 15804 D)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (NGA) (EN 15804 C4)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (NGA) (EN 15804 D)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (typical) (EN 15804 C4)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (typical) (EN 15804 D)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (NGA) (EN 15804 C4)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (NGA) (EN 15804 D)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (typical) (EN 15804 C4)
AU: Landfill of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (typical) (EN 15804 D)
AU: Landfill of plywood, formply, A-bond, 17 mm (formwork) (NGA) (EN 15804 C4)
AU: Landfill of plywood, formply, A-bond, 17 mm (formwork) (NGA) (EN 15804 D)
AU: Landfill of plywood, formply, A-bond, 17 mm (formwork) (typical) (EN 15804 C4)
AU: Landfill of plywood, formply, A-bond, 17 mm (formwork) (typical) (EN 15804 D)
AU: Landfill of plywood, interior, C-bond, 9 mm (joinery) (NGA) (EN 15804 C4)
AU: Landfill of plywood, interior, C-bond, 9 mm (joinery) (NGA) (EN 15804 D)
AU: Landfill of plywood, interior, C-bond, 9 mm (joinery) (typical) (EN 15804 C4)
AU: Landfill of plywood, interior, C-bond, 9 mm (joinery) (typical) (EN 15804 D)
AU: Landfill of softwood timber, kiln-dried, dressed, untreated (NGA) (EN 15804 C4)
AU: Landfill of softwood timber, kiln-dried, dressed, untreated (NGA) (EN 15804 D)
AU: Landfill of softwood timber, kiln-dried, dressed, untreated (typical) (EN 15804 C4)
AU: Landfill of softwood timber, kiln-dried, dressed, untreated (typical) (EN 15804 D)
AU: Landfill of softwood timber, kiln-dried, rough-sawn, untreated (NGA) (EN 15804 C4)
AU: Landfill of softwood timber, kiln-dried, rough-sawn, untreated (NGA) (EN 15804 D)
AU: Landfill of softwood timber, kiln-dried, rough-sawn, untreated (typical) (EN 15804 C4)
AU: Landfill of softwood timber, kiln-dried, rough-sawn, untreated (typical) (EN 15804 D)
AU: MDF, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 A1-A3)
AU: MDF, moisture resistant (MR), E1, melamine coated, 25 mm (EN 15804 A1-A3)
AU: MDF, standard, E1, melamine coated, 18 mm (EN 15804 A1-A3)
AU: MDF, standard, E1, melamine coated, 25 mm (EN 15804 A1-A3)
AU: Particleboard, flooring (tongue & groove), 19 mm (EN 15804 A1-A3)
AU: Particleboard, flooring (tongue & groove), 22 mm (EN 15804 A1-A3)
AU: Particleboard, flooring (tongue & groove), 25 mm (EN 15804 A1-A3)
AU: Particleboard, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 A1-A3)
50
AU: Particleboard, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 A1-A3)
AU: Particleboard, standard, E1, melamine coated, 16 mm (EN 15804 A1-A3)
AU: Particleboard, standard, E1, melamine coated, 18 mm (EN 15804 A1-A3)
AU: Plywood, exterior, A-bond, 7 mm (bracing) (EN 15804 A1-A3)
AU: Plywood, exterior, A-bond, 9 mm (structural) (EN 15804 A1-A3)
AU: Plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (EN 15804 A1-A3)
AU: Plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (EN 15804 A1-A3)
AU: Plywood, formply, A-bond, 17 mm (formwork) (EN 15804 A1-A3)
AU: Plywood, interior, C-bond, 9 mm (joinery) (EN 15804 A1-A3)
AU: Recycling of hardwood timber, green, dressed, untreated (EN 15804 C3)
AU: Recycling of hardwood timber, green, dressed, untreated (EN 15804 D)
AU: Recycling of hardwood timber, green, rough-sawn, untreated (EN 15804 C3)
AU: Recycling of hardwood timber, green, rough-sawn, untreated (EN 15804 D)
AU: Recycling of hardwood timber, kiln-dried, dressed, untreated (EN 15804 C3)
AU: Recycling of hardwood timber, kiln-dried, dressed, untreated (EN 15804 D)
AU: Recycling of hardwood timber, kiln-dried, rough-sawn, untreated (EN 15804 C3)
AU: Recycling of hardwood timber, kiln-dried, rough-sawn, untreated (EN 15804 D)
AU: Recycling of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 C3)
AU: Recycling of MDF, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 D)
AU: Recycling of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (EN 15804 C3)
AU: Recycling of MDF, moisture resistant (MR), E1, melamine coated, 25 mm (EN 15804 D)
AU: Recycling of MDF, standard, E1, melamine coated, 18 mm (EN 15804 C3)
AU: Recycling of MDF, standard, E1, melamine coated, 18 mm (EN 15804 D)
AU: Recycling of MDF, standard, E1, melamine coated, 25 mm (EN 15804 C3)
AU: Recycling of MDF, standard, E1, melamine coated, 25 mm (EN 15804 D)
AU: Recycling of particleboard, flooring (tongue & groove), 19 mm (EN 15804 C3)
AU: Recycling of particleboard, flooring (tongue & groove), 19 mm (EN 15804 D)
AU: Recycling of particleboard, flooring (tongue & groove), 22 mm (EN 15804 C3)
AU: Recycling of particleboard, flooring (tongue & groove), 22 mm (EN 15804 D)
AU: Recycling of particleboard, flooring (tongue & groove), 25 mm (EN 15804 C3)
AU: Recycling of particleboard, flooring (tongue & groove), 25 mm (EN 15804 D)
AU: Recycling of particleboard, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 C3)
AU: Recycling of particleboard, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 D)
AU: Recycling of particleboard, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 C3)
AU: Recycling of particleboard, moisture resistant (MR), E1, melamine coated, 18 mm (EN 15804 D)
AU: Recycling of particleboard, standard, E1, melamine coated, 16 mm (EN 15804 C3)
AU: Recycling of particleboard, standard, E1, melamine coated, 16 mm (EN 15804 D)
51
AU: Recycling of particleboard, standard, E1, melamine coated, 18 mm (EN 15804 C3)
AU: Recycling of particleboard, standard, E1, melamine coated, 18 mm (EN 15804 D)
AU: Recycling of plywood, exterior, A-bond, 7 mm (bracing) (EN 15804 C3)
AU: Recycling of plywood, exterior, A-bond, 7 mm (bracing) (EN 15804 D)
AU: Recycling of plywood, exterior, A-bond, 9 mm (structural) (EN 15804 C3)
AU: Recycling of plywood, exterior, A-bond, 9 mm (structural) (EN 15804 D)
AU: Recycling of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (EN 15804 C3)
AU: Recycling of plywood, flooring (tongue & groove), A-bond, 15 mm (residential) (EN 15804 D)
AU: Recycling of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (EN 15804 C3)
AU: Recycling of plywood, flooring (tongue & groove), A-bond, 25 mm (commercial) (EN 15804 D)
AU: Recycling of plywood, formply, A-bond, 17 mm (formwork) (EN 15804 C3)
AU: Recycling of plywood, formply, A-bond, 17 mm (formwork) (EN 15804 D)
AU: Recycling of plywood, interior, C-bond, 9 mm (joinery) (EN 15804 C3)
AU: Recycling of plywood, interior, C-bond, 9 mm (joinery) (EN 15804 D)
AU: Recycling of softwood timber, kiln-dried, dressed, untreated (EN 15804 C3)
AU: Recycling of softwood timber, kiln-dried, dressed, untreated (EN 15804 D)
AU: Recycling of softwood timber, kiln-dried, rough-sawn, untreated (EN 15804 C3)
AU: Recycling of softwood timber, kiln-dried, rough-sawn, untreated (EN 15804 D)
AU: Softwood timber, kiln-dried, dressed, untreated (EN 15804 A1-A3)
AU: Softwood timber, kiln-dried, rough-sawn, untreated (EN15804 A1-A3)
52
4. General continuous improvements
As to changes in context of various other, continuous improvements that are not related to
single process datasets, please see the following tables that document the entries in the JIRA
system:
Documentation / Naming
JIRA
Tracking
Number
Issue
Category
Item Description Change in
results
Affects
Extension
module
GC-1857 Documentation
/Naming
Waste
incineration
plants -
parameter setting
clarification
For waste incineration datasets
using selective catalytic reduction
for NOx removal, the
documentation now clarifies that
SCR instead of SNCR is used.
Does not change
the results.
All
GC-2819 Documentation
/Naming
Technical
purpose
documentation
field improvement
The technical purpose data field
was improved for selected
datasets
Does not change
the results.
All
GC-3548 Documentation
/Naming
Harmonize
glucose flow
information
Information in the flows were
updated.
Does not change
the results.
All
GC-3595 Documentation
/Naming
Documentation:
Vulcanisation of
synthetic rubber
Vulcanisation of synthetic rubber
(without additives): "Without
additives" was to the dataset
name added and in the
technology description the
following sentence was added:
No additives are considered in
the final product in order to give
final user the full liberty to build
his own composition.
Does not change
the results.
Extension
database X:
machining
processes
GC-3683 Documentation
/Naming
Links in datasets
to homepage
As links in datasets referring to
the thinkstep homepage are now
general links:
http://www.gabi-
software.com/support/gabi/gabi-
modelling-principles/
http://www.gabi-
software.com/support/gabi/
Does not change
the results.
All
GC-3886 Documentation
/Naming
Documentation:
Styrene-
Butadiene
Rubber (SBR)
Mix
The documentation of the
Styrene-Butadiene-Rubber
process has been improved.
Does not change
the results.
Professional
database
GC-4096 Documentation
/Naming
Documentation:
Hydrogen
(Europipeline)
Documentation is now clearer.
Sentence
deleted:
"Besides the production of
hydrogen in steam reforming
processes and by electrolysis of
water …“
Does not change
the results.
Professional
database
GC-4274 Documentation
/Naming
Naming:
Polypropylene
The unit process "Polypropylene
(PP) reinforced with talc 20 -
Does not change
the results.
Extension
database X:
53
(PP) reinforced
with talc 20 - 40%
injection moulded
part
40% injection moulded part" has
been renamed to "Polypropylene
(PP) injection moulded part". The
valid part weight range was
added in the general comment.
machining
processes
GC-4574 Documentation
/Naming
Quantities C_wt
and Water_wt in
the flow
properties of
wood plans
The carbon content as well as
the water content of the flows
were added to the flow properties
in all the plans where the carbon
balance was corrected.
Does not change
the results.
all
GC-4674 Documentation
/Naming
Naming: Weight
and payload in
process name of
transport
processes
Truck datasets now have the
norm, payload capacity and
gross weight in the name, e.g.
"Truck, Euro 3, 12-14t gross
weight / 9,3t payload capacity"
Does not change
the results.
Professional
database
Sorting
JIRA
Tracking
Number
Issue
Category
Item Description Change in
results
Affects
Extension
module
GC-4709 Sorting Specular stone:
not a mineral
resource
The flow Specular stone
{BBCF76BB-DDE2-47C9-BDB7-
E109FE36FF38} is now correctly
a valuable substance and not a
mineral resource. Will not have
any impact on results unless
used as a direct resource input in
foreground modelling.
Does not change
the results.
Professional
database
LCIA / Method
JIRA
Tracking
Number
Issue Category Item Description Change in results Affects
Extension
module
GC-4377 Characterization
factor uranium
flows
Bug Uranium and nuclear energy
flows now have characterization
factors for CML 2016 Abiotic
Depletion Potential (ADP) and in
some cases also for ReCiPe
1.08. It is expected that this ADP
potential is minor in comparison
with ADP potentials from other
resource consumptions.
Will only have an
effect if operating
with
energy/electricity
grids that are
highly dependent
on nuclear energy.
All
GC-4453 Regionalized
rain water flows
correction
Bug Rain water in inputs and outputs
were removed from the quantities
WSI and AWARE quantities.
The rain water
input flow had a
Characterisation
Factor (CF) of 0 in
SP 30 so its
removal did not
change the results.
The output flows of
rain water going to
river or lake had a
CF, but these flows
are not used in any
thinkstep process.
All
54
Therefore also no
changes of results.
If however a
practitioner has
used the flows in
the foreground
system of a LCA
study, the results
would change,
most visibly in
studies where
renewable
resources are
involved.
GC-4464 Characterization
factors for
'Nitrogen (as
total N)' to
freshwater and
salt water
Bug The characterization factors for
the flows to freshwater and
seawater of 'Nitrogen (as total N)'
are now harmonized, specifically
for the CML and PEF impact
categories. Nitrate (as total N) to
freshwater and seawater was not
fully characterized. This has
been updated with values of
nitrogen emission.
The corrected flows were mostly
used in USLCI unit processes.
Since however the TRACI
methodology was correctly
characterized this does not
represent any change.
The flows are now identical to
standard Nitrogen flows. The
standard nitrogen flow was
updated with EDIP 2003 values.
No changes when
using USLCI and
TRACI
methodology.
All
GC-4485 New output flows
of water scarcity
Improvement 70 new flows for water scarcity
(extreme scarcity...low scarcity)
were created and characterized.
Does not change
the results.
All
GC-4507 Characterisation
factors in water
assessment
methods for
water to turbine
Improvement Characterisation factors for the
new "to turbine" flows given for
all water methods that are not
outdated.
Does not change
the results.
All
GC-4554 Impacts
ILCD/PEF:
Eutrophication
marine midpoint
(v1.09)
Bug Two Cyanide flows were
removed from the quantity.
Two Ecoinvent long term flows
added to the quantity:
Nitrogen ecoinvent long-term to
fresh water {42eeb663-34dc-
4394-8a07-e4d8e45a7e2d}
Nitrogen oxides ecoinvent long-
term to air {9115356e-a534-
4329-9ec6-d9208720241b}
The change of
results coming
from the cyanide
flows is barely
visible, since the
amount of cyanide
emissions is very
small compared to
the other flows that
are characterised
here.
The Addition of the
two long term
Ecoinvent flows
are not visible in
thinkstep datasets
and Ecoinvent 3.1
datasets (since
both are not used
in these
databases). In
Ecoinvent 2.2
All
55
small increases of
results can
however happen in
the landfill
processes.
GC-4570 Characterization
factors CML
2001 ADP
(elements)
Bug Phosphorus minerals, precious
metal ore (R.O.M.) have been
implemented with correct values.
Processes where
the flows
phosphorous
minerals, precious
metal ore (R.O.M.)
are used decrease
in impact.
All
GC-4580 Characterization
factor of NMVOC
unspecified
Bug POCP value for the CML
quantities was originally
calculated from substance
composition (see below:
result=0.364 ethene eq.) as no
specific value was calculated by
CML. Since now a specific value
as a non-baseline value has
been provided by CML as 0.15kg
ethene-eq., this has been
implemented
Original substance mix "world"
(32,5% Pentane, 20% Propane,
17,9% Butane, 9% Hexane, 6,6%
Xylene, 6,4% Ethene, 3,9%
Toluene, 1,7% Ethane, 0,6%
Formaldehyde).
A change in results
should only be
observed if
emissions of
NMVOC
unspecified take
place.
All
GC-4618 Regionalized
water resources
in UBP 2013
Improvement Complete rework of the "UBP
2013 water resources" quantity.
UBP2013 now has regionalized
flows.
In addition to this we found from
the method report that in contrast
to the Excel table provided by the
method developer, the UBP
method for water is a
"consumption" method and not a
"use" method. The
implementation of UBP in Excel
uses estimated conversion
factors from use to consumption,
as in Ecoinvent 2.2 only the input
flows were used. In Ecoinvent
3.X this is no longer necessary
and we have therefore
implemented GaBi and Ecoinvent
consistently now.
Fundamental
changes in all
results when using
UBP2013.
All
GC-4619 Characterization
factor for
Ecoinvent flow
"water, turbine
use, unspecified
natural" origin in
UBP 2013
Bug The Ecoinvent flow for water,
turbine use, unspecified natural
origin now has the correct
characterization factor in the
UBP 2013 water resource
quantity.
Major changes in
all Ecoinvent
processes (roughly
factor 1000), since
the water used in
turbines for the
production of
electricity uses a
very big amount of
water.
All
56
GC-4640 Characterization
factor for "Water,
unspecified
natural origin"
corrected in blue
water use
Bug The characterization factor for
the flow "Water, unspecified
natural origin" was changed from
2000 to 1000.
Changes visible in
all Ecoinvent
processes. But in
most cases the
changes are small,
due to the fact that
the more specific
flows (river, lake,
well, cooling,
turbined) are used
more often.
Especially in the
energy and
renewables
processes of
Ecoinvent the flow
is rarely used, and
these two sectors
usually dominate
the water results.
All
GC-4641 Characterization
factor in WSI and
AWARE water
quantities
Bug In Ecoinvent flows that have m³
as characteristic unit in the WSI
and AWARE quantities, there
was factor 1000 deviation in the
characterization factor.
Changes are
highly relevant for
all Ecoinvent
processes, results
can change by a
factor of 1000.
All
GC-4646 Characterization
factors for Lake
water extreme
scarcity in WSI
Bug Characterisation factors for the
flow "Water (lake water, extreme
scarcity)“ in the 3 WSI (water
scarcity index) was corrected.
They now correctly use the char.
factors for extreme scarcity
instead of unspecified scarcity.
The flow is not
used in thinkstep
datasets. If
however a
practitioner has
used this flow in
the foreground
system of a study,
the results will be
affected and will be
higher (+20% for
the quantity WSI,
high
characterization
factor for
unspecified water,
+factor 10 for the
quantity WSI, low
characterization
factor for
unspecified water).
All
GC-4657 Ecoinvent output
water flows
characterization
for
"consumption"
type of water
methods
Bug The Ecoinvent water output flows
are now characterised in the
AWARE and WSI quantities.
They are now correctly
subtracted from the input flows,
leading to lower impacts.
Changes strongly
visible if the
AWARE or WSI
quantities were
applied to
Ecoinvent 3.1
processes.
Since the flows are
not used in
Ecoinvent 2.2 and
thinkstep
processes, the
results of these
processes do not
change.
All
57
New Objects
JIRA
Tracking
Number
Issue
Category
Item Description Change in
results
Affects
Extension
module
GC-2446 New dataset New EPD
datasets: PU
thermal insulation
boards
Three new EPD datasets for
thermal insulation boards from
IVPU are now available.
New datasets Professional
database
GC-2763 New dataset New dataset:
Steel tinplate
from APEAL
A new dataset "RER: Steel
tinplate" from APEAL is now
available.
New datasets Professional
database
GC-2920 New dataset New EPD
datasets:
Australian wood
products
In total 207 EPD datasets for
Australian Wood products
(including production and end of
life) are now available.
New datasets Professional
database
GC-3347 New dataset New dataset: IZA
Special High
grade Zinc
There is a new dataset from IZA
for Special high grade zinc
available. The previous dataset
has been moved to the folder
Version 2016.
New datasets Professional
database
GC-3803 New dataset New dataset:
Eco-profile of
Aromatic
Polyester Polyols
from PU Europe
The process "EU-28: Aromatic
Polyester Polyols (APP)
production mix" was added to the
professional database. This
makes two previous processes
now outdated:
"RER: Aromatic Polyester Polyol
(APP) (European average,
including flame retardant)"
{2beb6388-450a-4ae4-ba73-
8905b9b8949a}
"RER: Aromatic Polyester Polyol
(APP) (European average,
without flame retardant)"
{d275a93a-ca34-4b32-9769-
57cb3ffdc199}
The new process replaces the
one without flame retardant. No
new data was created for the
dataset with flame retardant.
New datasets Professional
database
GC-4218 New dataset New: EPS 2015
method
The Swedish EPS 2015dx
method is now available.
New datasets All
GC-4553 New dataset Extension
database XI:
electronics: ICs
and
Semiconductors
26 new datasets for ICs and
Semiconductors including a new
"technology node" are now
available in the Extension
database XI: electronics
New datasets Extension
database XI:
electronics
GC-4622 New dataset Additional
transport and
energy datasets
New datasets for regions are
now available (transports,
electricity, thermal energy).
New datasets Extension
database II:
energy
58
GC-4701 New dataset New dataset:
Cobalt dataset
from CDI
The dataset "GLO: Cobalt,
refined (metal)" from the CDI
(Cobalt Development Institute) is
now available.
New datasets Professional
database
Bugs and improvements in various GaBi databases
JIRA
Tracking
Number
Issue
Category
Item Description Change in
results
Affects
Extension
module
GC-810 Improvement Zircon oxide flow Zircon oxide flow was merged
with Zirconium oxide flow.
Does not change
the results.
all
GC-3092 Improvement Biofuel data sets
(palm oil and soy
oil) in diesel
mixes
Update of biofuel supply chain
using soy methyl ester (SME)
and Palm oil methyl ester (PME)
for diesel mixes of all affected
countries.
Significant
reductions in
almost all impact
categories of the
diesel mixes
Professional
database
GC-3416 Improvement Update electricity
supply in PV
manufacturing
Update and improvement of
electricity grid mix from
photovoltaic data sets
Impacts increase All
GC-3527 Improvement Electrolyte
copper price
update
In the economic quantity “Price”,
GUID: {BCF81C0A-9FE9-4B88-
A25B-99E0DD64DDAA}, the
market price for electrolytic
copper was updated to latest
figures. Based on information
from Worldbank
(http://www.worldbank.org/en/res
earch/commodity-markets),
copper is now defined with 6,81$.
This figure is a baseline 10 years
average, based on years 2005-
2014.
In the following flows, the copper
price was updated:
Copper (99.999%; electrolyte
copper) {c4e9f33b-d3a7-4283-
8f96-96215e6fff76}
Copper {f077601f-8265-4a6f-
85d8-545d49705652}
Copper by-product {3ca984d0-
714e-4112-af67-34723ab924db}
Copper (98%; blister copper)
{ba47fa07-5822-43d2-b254-
bf8c7c7fe6c6}
Copper cathode (>99.99 Cu)
{cb8d4db7-af9b-4652-8031-
a6c28d1c5d5c}
For the blister copper, 98% of the
6,81$ was taken as basis.
If the GaBi user
created copper
production models
where economic
allocation is
applied by using
the quantity “Price”
{BCF81C0A-9FE9-
4B88-A25B-
99E0DD64DDAA},
there might be a
redistribution in the
allocated
environmental
burdens of the
main product (e.g.
copper) and the
co-products. This
is especially
relevant if the
prices of the co-
products are not
updated in the
model of the GaBi
user.
All
GC-3581 Improvement Particle size of
dust emissions
from power plants
The PM10/PM2.5 emissions are
more and more in the focus in
urban areas (transport, energy
combustion). For comparisons of
alternative propulsion systems
(FCEV, battery electric vehicles
etc.) with conventional engines it
is essential that the PM2.5/PM10
Changes the
results when look
at particulate
matter.
All
59
emissions are meaningful within
the electricity grid mixes and the
material data sets which are
relevant for the vehicles (mostly
steel, aluminium and other
metals (e.g. catalysts or for
batteries)). The existing models
were adapted to make it more
possible to distinguish the
particle emissions in different
countries.
GC-3640 Bug Harmonize
valuable
substance flow
used in Timber
teak process
(CN/BR)
The valuable substance output
flow was exchanged to Timber
teak (12% moisture / 10.7%
H2O).
Does not change
the results.
Professional
database
GC-3643 Bug CAS code of
aluminium III
flows
Two sets of identical flows of the
aluminium (III) ion for emissions
to freshwater and seawater were
merged. Furthermore, the CAS
number was changed from
elementary aluminium (007429-
90-5) to the aluminium (III) ion
(022537-23-1) for emissions to
sea water, fresh water and
industrial soil.
Does not change
the results.
All
GC-3812 Bug ERASM dataset:
waste steel scrap
input
The dataset "EU-28: C12-15
Alcohol (petro) Ethoxylate, 3
moles EO(No. 11 - Matrix) had
steel scrap as a waste flow in the
input. This was corrected.
Does not change
the results.
Professional
database
GC-3848 Bug Usage of
Ecoinvent flow
"Barite to sea
water"
Flows remained as fragments in
thinkstep processes, even
though they are not used in any
unit process. The flow has been
deleted from all thinkstep
processes, except third party
datasets (such as industry data
or EPDs).
Does not change
the results.
Professional
database
GC-3963 Bug Blue water
consumption in
refinery products
The water balance of refinery
models was corrected.
Changes in water
categories "blue
water
consumption" and
"total freshwater
consumption", they
are no longer
negative
All
GC-3944 Improvement Update of refinery
emission factors
Refinery emission factors and
water use and consumption were
updated.
Significant
differences before
and after, ranging
from -90% to
+175% in Impact
categories and
changes of about
+555% for water
categories due to
update of refinery
emission factors
All
60
and water use and
consumption
GC-4225 Bug Open carbon
dioxide as
valuable
substance flow in
input of
processes
The following datasets had a
very small open CO2 input. The
CO2 was needed for welding and
the corresponding process was
added in the model.
"GLO: Hydrogen from steam
reforming (centralised) - for partly
aggregation"
"GLO: Hydrogen from decentral
electrolysis - for partly
aggregation"
"GLO: Hydrogen from steam
reforming (decentralised) - for
partly aggregation"
"GLO: Hydrogen dispensing - for
partly aggregation"
Does not change
the results.
Extension
database II:
energy
GC-4266 Improvement Corrugated board
from FEFCO
Update of the FEFCO datasets of
corrugated board and the paper
grades for their production.
Representative year is now 2014
(instead of 2011).
Changes in comparison to
previous version:
- System subdivision by FEFCO,
so that now only for the Kraftliner
process (and its by-products tall
oil and turpentine) an allocation
is needed.
- Transports covered in more
detail
- Water content of the products
(8%) now explicitly accounted for
in all calculations
- Because of user requests (cut-
off approach used or mandatory
for reporting, e.g. EN15804) we
have now incorporated the
carbon and primary energy
uptake for the waste paper input.
The carbon uptake now matches
the carbon content and the
primary energy now matches the
without the need for waste paper
modelling
The LCA results
are mostly lower
compared to the
older version
because of
increased
efficiency in the
paper mill. For
some impacts, e.g.
SO2 emissions in
Kraftliner
production or
toxicity,
improvements in
measuring specific
emissions lead to
changes compared
to unspecific
emissions.
Professional
database
GC-4272 Bug Water balance in
Phenol
Process water and cooling water
inputs were corrected.
The water
consumption
change between 0
and 30% due to
process water
being changed to
cooling water and
the quantity of
water vapour
increased.
Professional
database
GC-4293 Improvement Carbon correction The carbon balance was
corrected in all top level plans.
Changes affects
only GWP
including biogenic
carbon.
All
61
GC-4480 Bug Quicklime: mass
and water
balance
Water vapour output amount was
recalculated and corrected. Air
and exhaust were added to close
the mass balance.
Does not change
the results.
Professional
database
GC-4505 Bug VDA material
classification
Material classification for the
classes 3.3 and 3.4 are now
correct:
3.3 zinc alloys
3.4 nickel alloys
Does not change
the results.
All
GC-4506 Bug Carbon balance
in wood chip
datasets
The carbon balance in wood chip
datasets was corrected.
Changes results
for Global
Warming Potential.
All
GC-4543 Bug Primary energy
correction
Wood plans using economic
allocation had a skewed primary
energy output. Processes used in
the energy mixes were corrected.
Changes the
results for primary
energy.
all
GC-4586 Improvement Mass balance of
synthesis gas
processes
The light fuel oil (LFO) demand
for the production of synthesis
gas via partial oxidation was
increased based on expert input.
The available literature source
indicated a too small input
amount. Compared to the other
synthetic gas routes (steam
reforming from natural gas, coal
gasification), the mass input
amount and consequently the
energy input amount of the
synthetic gas route from LFO is
now in a realistic range. The
previous mass input amount of
LFO was only the oil used as
hydrocarbon feedstock and did
not include the amount of oil
which is burned and used in the
process as fuel source. Due to
the increase of the oil input and
the consideration of the oil as
feedstock and fuel, the CO2
emissions were accordingly
adapted/increased
Due to the change,
the common
impact categories
increases as
follows, depending
whether US or DE
data set:
AP ca. 10% to
35%
EP ca. 60% to
70%
GWP ca. 300% to
350%
POCP ca. 50% to
70%
Primary energy
demand ca. 85%
to 90%
Professional
database
Extension
database XVII:
full US
GC-4687 Bug Resource flows
merging
Several resource flows were
merged:
DOMINANT Colemanite ore Non
renewable resources
{e960e086-54f9-4822-a889-
1bacc1f65dca}
with Colemanite, in ground Non
renewable resources
{ec72c523-9e1a-466a-98c3-
e4098e90fd27}
DOMINANT Lithium ore (3%)
Non renewable resources
{eb0c0017-939b-4ee9-9bb9-
1c4f4fd97c5a}
with
Lithium ore Non renewable
resources
Does not change
the results.
All
62
{5e71e33d-4c0a-43f1-9076-
fc175117fb9b}
Lithium ore (R.O.M) Non
renewable resources
{163e002a-96cf-4d07-8984-
80c5c9858903}
DOMINANT Natural pumice Non
renewable resources
{c6d825be-a2f2-461b-a4af-
a4bac4fa634c}
with
Raw pumice Non renewable
resources
{328a0b78-2aa1-4eea-87fd-
b70d64229747}
DOMINANT Zirconium sand Non
renewable resources
{2d49f949-d1d6-4b79-8d1a-
4c34da752b3a}
With
Zircon Non renewable resources
{9ded4342-0616-4736-b6e2-
04f94b23837b}
GC-4708 Improvement "IN: Alumina":
refinery steam
input too high
The steam input in the
manufacturing was too high by a
factor of 20 and was corrected.
Impact decreased
by 50%.
Extension
database XXI:
India
GC-4380 Improvement French
technology mix of
uranium
enrichment
(centrifuge/diffusi
on)
The French technology mix of
uranium enrichment of the
modelled nuclear supply
countries was changed to 100%
centrifuge technology
This change leads
to decrease in the
ODP. This has
impact on several
processes which
also use the
French electricity
grid mix, such as
the German
electricity grid mix,
where it is used as
an import or also
on the European
mix.
all
63
References
Baitz, M. 2002 Baitz, M. (2002): Die Bedeutung der funktionsbasierten Charakterisierung von
Flächen-Inanspruchnahmen in industriellen Prozesskettenanalysen. Ein Beitrag zur
ganzheitlichen Bilanzierung. Dissertation. Aachen: Shaker (Berichte aus der
Umwelttechnik).
Beck 2010 Beck, T.; Bos, U.; Wittstock, B. (2010): LANCA – Calculation of Land Use Indicator
Values in Life Cycle Assessment; Online http://www.lbp-gabi.de/ .
Brentrup 2000 Brentrup, F.; Küsters, J.; Lammel, J.; Kuhlmann, H.; (2000): Methods to estimate
on-field nitrogen emissions from crop production as an input to LCA studies in the
Agricultural Sector. The International Journal of Life Cycle Assessment. 5(6), 349-
357.
EC 2001 European Commission: Directive 2001/80/EC on the limitation of emissions of
certain pollutants into the air from large combustion plants, 2001
EC 2009 European Commission: Directive 2009/28/EC on the promotion of the use of energy
from renewable sources and amending and subsequently repealing Directives
2001/77/EC and 2003/30/EC (Renewable Directive), April 2009
EC 2010 European Commission (2010). Directive 2010/75/EU of the European Parliament
and of the council of 24 November 2010 on industrial emissions (integrated pollution
prevention and control), November 2010
EIA 2015 U.S. Energy Information Administration: Electricity Data – Generation and thermal
out-put by energy source, total of all production types, release date January 2015,
http://www.eia.gov/electricity/data.cfm#generation
EPA 2014 U.S. Environmental Protection Agency (EPA): The Emissions and Generation
Resource integrated database (eGrid), 9th edition of eGrid with Year 2010 data,
Washington, 2014
Eurochlor 2012 Eurochlor, Chlorine Industry Review 2011-2012, 2012
Eurostat 2015 Eurostat: Energy Database - Supply, transformation, consumption - electricity -
annual data [nrg_105a], Luxembourg, 2015
FERC 2014 Federal Energy Regulatory Commission (FERC): Form No. 2014 – Annual Electric
Balancing Authority Area and Planning Area Report, 2014
IEA 2012 International Energy Agency: Electricity Information 2012, Paris, 2012
IPCC 2006 Intergovernmental Panel on Climate Change (IPCC). (2006). Guidelines for National
Greenhouse Gas Inventories, Volume 4 Agriculture, Forestry and Other Land Use,
Retrieved December 22, 2009 from:
http://www.ipccnggip.iges.or.jp/public/2006gl/vol4.html
ISO 14046 ISO/CD Life Cycle Assessment – Water Footprint – Requirements and guidelines
64
ISO 2006 International Organization for Standardization (ISO). (2006): Environmental
Management – Life Cycle Assessment – Principles and Framework. Series 14040
and 14044.
Pfister 2011 Pfister, S.; Bayer, P.; Koehler, A.; Hellweg. S. (2011): Environmental Science &
Technology 2011 45 (13), 5761-5768 Environmental Impacts of Water Use in Global
Crop Production: Hotspots and Trade-Offs with Land Use
SICAS 2008 Semiconductor Industry Association (SIA): Semiconductor International Capacity
Statistics (SICAS) 2008
UBA 2010 Handbuch Emissionsfaktoren des Straßenverkehrs, Version 3.1, Umweltbundesamt
Berlin; BUWAL / OFEFP Bern; Umweltbundesamt Wien, http://www.hbefa.net,
Berlin, Bern, Vienna / Germany, Switzerland, Austria
WaterGAP 2012 Water - a Global Assessment and Prognosis. Version 2.0. Center for
environmental systems research, University of Kassel, Germany. 2012
WSTS 2008 World Semiconductor Trade Statistics: Semiconductor Market Forecast 2008
65
Annex: “Version 2016” datasets – Recommendations
For various reasons, there are a few processes in the Databases 2017 Edition which will not continued to be maintained. These have been
moved into a folder called Version 2016 and have been given the suffix '(Version 2016)'. They are still available for clients who need to work
with them but will not be upgraded anymore, and are not part of the delivery scope for new GaBi clients. There are two reasons behind this
approach:
i) thinkstep is committed not to provide information which is not up-to-date and
ii) thinkstep wants to enable users who have used the dataset to decide if it is still appropriate in their specific goal and scope.
The small table below shows the overview of how many plans and processes are affected and to which database they belong. The additional
tables below list all affected processes along with the explanations and recommended alternatives.
Version 2016 processes 51
Professional database 9
Extension database II: Energy
Extension database XVII: Full US 30
Extension database IX: End of life 11
Extension database XII: Renewable materials 1
66
Version 2016 processes
Extension database XIV: construction
materials
Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
US Aluminum Can
sheet rolling p-agg AA
primary
production|consumpt
ion mix, at plant
{4bca7cfb-e1ee-
4443-b4e6-
f64d609706ea}
US Aluminum can
sheet rolling
p-
agg AA
primary
production|consumption mix,
at plant|0.012 inches
thickness
{72eba6dd-
dcd0-4651-
9a11-
a6dc6a6bee96}
RER
Aromatic
Polyester
Polyol (APP)
(European
average,
including flame
retardant)
agg PU Europe
technologies of four
producers|production
mix, at plant
{2beb6388-450a-
4ae4-ba73-
8905b9b8949a}
EU-28
Aromatic
Polyester
Polyols (APP)
production mix
agg
PU
Euro
pe
polycondensation|production
mix, at producer|Hydroxyl
value: 150-360, aromatic
content: 5-50%
{d2fe899e-7fc0-
49d3-a7cc-
bbf8cad5439a}
RER
Aromatic
Polyester
Polyol (APP)
(European
average,
without flame
retardant)
agg PU Europe
technologies of four
producers|production
mix, at plant
{d275a93a-ca34-
4b32-9769-
57cb3ffdc199}
EU-28
Aromatic
Polyester
Polyols (APP)
production mix
agg
PU
Euro
pe
polycondensation|production
mix, at producer|Hydroxyl
value: 150-360, aromatic
content: 5-50%
{d2fe899e-7fc0-
49d3-a7cc-
bbf8cad5439a}
US
Electricity from
biomass (solid)
(Alaska)
agg ts
AC, mix of direct and
CHP, technology mix
regarding firing and
flue gas
cleaning|production
mix, at power
plant|1kV - 60kV
{44317200-fd01-
4862-98ea-
bfd0bf99a0f0}
If relevant, please contact data
on demand from thinkstep for
alternative processes
HU Electricity from
hard coal agg ts
AC, mix of direct and
CHP, technology mix
regarding firing and
flue gas
cleaning|production
mix, at power
plant|1kV - 60kV
{a6a5307e-c7e2-
4652-b49c-
2e420c6474e5}
If relevant, please contact data
on demand from thinkstep for
alternative processes
67
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
LU
Electricity from
heavy fuel oil
(HFO)
agg ts
AC, mix of direct and
CHP, technology mix
regarding firing and
flue gas
cleaning|production
mix, at power
plant|1kV - 60kV
{7edf130d-6aca-
4ced-91c5-
dc6f4a15f826}
If relevant, please contact data
on demand from thinkstep for
alternative processes
MT Electricity from
waste agg ts
AC, CHP, dry flue
gas treatment,
without waste
collection, transport
and pre-
treatment|production
mix, at power
plant|1kV - 60kV
{d5153d61-d619-
4a48-b731-
a4033c61f8f6}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US Electricity grid
mix – MISO agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{417f8c6e-5f72-
46b2-8003-
d2917d6a4c21}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – MISO
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{7efc72ca-fd35-
4640-8177-
22c9dea8056c}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – MISO
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{5133b2ab-007f-
4e3f-9749-
923e0a81bb3e}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – MROW
(without MISO)
agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{7775fa36-2c06-
44a6-ae0b-
3dc667aeaca6}
If relevant, please contact data
on demand from thinkstep for
alternative processes
68
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
US
Electricity grid
mix – MROW
(without MISO)
agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{7775fa36-2c06-
44a6-ae0b-
3dc667aeaca6}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – MROW
(without MISO)
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{2ba17680-609c-
4ffc-a828-
03f3db065792}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – MROW
(without MISO)
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{bf02b93e-2e09-
497f-8896-
6c5f024a82ef}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US Electricity grid
mix – NYISO agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{fede8c88-969e-
4347-8ba1-
c8aa9a356877}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – NYISO
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{7d286e2c-f52e-
4a8a-b4c0-
f22e76cc5b4d}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – NYISO
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{330f5642-8fab-
495d-b18c-
10984fb1b3ae}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US Electricity grid
mix – PJM agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{f9738a8a-dd78-
4845-b0af-
3ea6446eaae0}
If relevant, please contact data
on demand from thinkstep for
alternative processes
69
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
US
Electricity grid
mix – PJM
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{c97aa974-fffa-
4d49-ad09-
dcb0fbcfa084}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – PJM
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{5fa99c49-f2ef-
43c3-9bc8-
9b0ee8b4e1d3}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – RFCW
(without MISO
and PJM)
agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{a8c5e505-42f5-
4984-b7d8-
6c1e1c35c694}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – RFCW
(without MISO
and PJM)
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{e60998ba-8c79-
4003-853e-
f55217d274f9}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – RFCW
(without MISO
and PJM)
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{500c6c8a-1578-
45f7-ac3b-
6f4906f2c6ca}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – SRTV
(without MISO)
agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{84f11faf-8e53-
4a55-89e8-
6d7d04869579}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – SRTV
(without MISO)
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{01a0e9e6-35ea-
4328-aab3-
503f9b1cbc97}
If relevant, please contact data
on demand from thinkstep for
alternative processes
70
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
US
Electricity grid
mix – SRTV
(without MISO)
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{27d0e3bc-1491-
4944-a496-
917bc6c26634}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – SRVC
(without PJM)
agg ts
AC, technology
mix|consumption
mix, to
consumer|<1kV
{dd999fe0-f9a2-
43a3-845a-
c1e374c90b96}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – SRVC
(without PJM)
(direct)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{b0562f1a-6490-
41fb-a773-
33a70e5eba46}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix – SRVC
(without PJM)
(indirect)
agg ts
AC, technology
mix|consumption
mix, at
consumer|<1kV
{a8052d6f-9758-
45d0-8447-
c3715e1d834d}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
MISO
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{ef64dda8-0cab-
48e9-9a09-
8166456ad634}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
MROW
(without MISO)
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{71dd12ae-9e7b-
4b39-a811-
eb714dd4b15a}
If relevant, please contact data
on demand from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
NYISO
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{4c97783d-9f76-
4942-8e7f-
a3736aa70ab1}
If relevant, please contact data
on demand from thinkstep for
alternative processes
71
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
HU
Process steam
from hard coal
85%
agg ts
technology mix
regarding firing and
flue gas
cleaning|production
mix, at heat
plant|MJ, 85%
efficiency
{153af68e-733e-
49af-839f-
2c25c875857a}
If relevant, please
contact data on demand
from thinkstep for
alternative processes
HU
Process steam
from hard coal
90%
agg ts
technology mix
regarding firing and
flue gas
cleaning|production
mix, at heat
plant|MJ, 90%
efficiency
{138cb3e6-bf41-
43ff-9697-
ed9acef6c591}
If relevant, please
contact data on demand
from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
PJM
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{0cdc6931-ff28-
4cc1-9592-
7c5b5515f02f}
If relevant, please
contact data on demand
from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
RFCW (without
MISO and
PJM)
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{8aa7530b-d6d2-
4f2b-8a8b-
0c558cee62e8}
If relevant, please
contact data on demand
from thinkstep for
alternative processes
US
Electricity grid
mix 1kV-60kV -
SRTV (without
MISO)
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{f78481bb-c39c-
426d-8782-
a68cff6f1c92}
If relevant, please
contact data on demand
from thinkstep for
alternative processes
72
Version 2016 processes
Professional database Alternative process to be used instead
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e Details
Process GUID
Can be entered in
the search tool
Co
un
try
Pro
cess
na
me
Ty
pe
So
urc
e
Ob
ject
gro
up
Details
Process GUID
Can be entered
in the search
tool
US
Electricity grid
mix 1kV-60kV -
SRVC (without
PJM)
agg ts
AC, technology
mix|consumption
mix, to
consumer|1kV -
60kV
{9daf00b2-ac35-
459a-b7df-
992e6985272d}
If relevant, please contact data
on demand from thinkstep for
alternative processes
HU
Process steam
from hard coal
95%
agg ts
technology mix
regarding firing and
flue gas
cleaning|production
mix, at heat
plant|MJ, 95%
efficiency
{8d5b0bd7-2fdf-
458d-a3a6-
67ecc159cbd3}
If relevant, please contact data
on demand from thinkstep for
alternative processes
AR
Soy bean at
field border
(13% H2O
content) (incl.
LUC as fossil
CO2) -
outdated
agg ts
technology
mix|production mix,
at plant|Soy bean at
field border, 13%
H2O
{485e34a9-37f9-
4e2b-8c39-
61b4d4cb1901}
AR
Soy bean at
field border
(13% H2O
content)
agg ts
technology mix|production
mix, at plant|Soy bean at field
border, 13% H2O
{a27ffd4c-abf2-
4abb-afe2-
330fc088684e}
GLO
Special high
grade zinc
[OUTDATED]
agg ELCD/IZA
primary
production|productio
n mix, at plant
{fd9db252-4998-
11dd-ae16-
0800200c9a66}
GLO Special high
grade zinc
p-
agg IZA
primary production|production
mix, at plant|99.99%
{83e3e42c-
0cc9-459b-
960b-
5fbda1280237}
DE:
Incineration
PET fabric
flame
laminated (incl.
credit)
{337df6d3-3e4c-
4f95-9ca6-
29e747c4b66c}
If relevant, please contact data
on demand from thinkstep for
alternative processes