gabi databases 2017 edition€¦ · gabi 2017 databases are ground-breaking in this respect. land...

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

4

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

5

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/

6

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

7

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/

8

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.

9

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

10

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”.

11

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.

12

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.

13

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

14

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

15

[%] 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

16

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).

17

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

18

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


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

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


Dried shea

kernels

A new dataset "GLO: Dried

shea kernels" is now available.


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


EU-28: Gluten feed (corn wet


EU-28: Gluten meal (corn wet


EU-28: Gluten meal (corn wet


US: Corn bran (corn wet mill)


US: Corn bran (corn wet mill)

(mass allocation)

US: Corn oil (corn wet mill)


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


US: Corn steep liquor (corn wet


US: Dried starch (corn wet mill)

(mass allocation)

US: Gluten feed (corn wet mill)

(mass allocation)

US: Gluten meal (corn wet mill)

(mass allocation)


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®


database XX:

food & feed


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


Extension

module


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


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.


database XIV:

construction

materials


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}


database XIV:

construction

materials


dataset:

Laminate

Flammex -

Egger

New EPD dataset for Laminate

Flammex from Egger is now

available.


database XIV:

construction

materials


dataset:

Coloured

laminate - Egger

New EPD dataset for Coloured

laminate from Egger is now

available.


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


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.


database XIV:

construction

materials


dataset:

Laminate from

ALLOC

A new EPD dataset "EU-28:

High pressure laminate (HPL)"

is now available.


database XIV:

construction

materials


dataset:

Laminate from

MeisterWerke

Schulte GmbH

A new dataset "EU-28: Direct

pressure laminate (DPL) (1m²)"

from MeisterWerke Schulte

GmbH is now available.


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


datasets: Steel

screws - EJOT

EPD Datasets for Steel screws,

stainless steel screws and bi-

metal screws are now

available.


database XIV:

construction

materials


Naming

Documentation:

Sand 0/2

In the documentation spelling

mistakes were corrected.

Does not change

the results.

Extension

database XIV:

construction

materials

38


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.


database XIV:

construction

materials


dataset: Smoke

Damper from

Wildeboer

A new EPD dataset for smoke

control damper is now

available.


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


dataset: Sand-

lime brick from

Kalksandsteinve

rband e.V.

A new EPD dataset for sand-

lime brick from

Kalksandsteinverband e.V. is

now available.


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


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


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


Extension

module

GC-1448 New dataset New datasets:

Hardwood

veneer from

AHEC

Three new datasets for

hardwood veneer datasets

from AHEC are now available.


database XVII:

full US


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.


database XVII:

full US


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.


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.


datasets: Steel

EPDs from

CMC

Five new EPD datasets for

steel production were added

to Extension database XVII:

Full US 2017


database XVII:

full US


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


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


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)

http://www.apeal.org/

http://www.thecdi.com/

http://www.evonik.com/

http://www.zinc.org/

http://www.pu-europe.eu/

http://www.fwpa.com.au/

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, 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, 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, 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, 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


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


/Naming

Technical

purpose

documentation

field improvement

The technical purpose data field

was improved for selected

datasets

Does not change

the results.

All


/Naming

Harmonize

glucose flow

information

Information in the flows were

updated.

Does not change

the results.

All


/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


/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


/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


/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


/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


/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


/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


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.


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.


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


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


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


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


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


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


results

Affects

Extension

module


datasets: PU

thermal insulation

boards

Three new EPD datasets for

thermal insulation boards from

IVPU are now available.

New datasets Professional

database


Steel tinplate

from APEAL

A new dataset "RER: Steel

tinplate" from APEAL is now

available.


database


datasets:

Australian wood

products

In total 207 EPD datasets for

Australian Wood products

(including production and end of

life) are now available.


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.


database


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.


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


database XI:

electronics

GC-4622 New dataset Additional

transport and

energy datasets

New datasets for regions are

now available (transports,

electricity, thermal energy).


database II:

energy

58


Cobalt dataset

from CDI

The dataset "GLO: Cobalt,

refined (metal)" from the CDI

(Cobalt Development Institute) is

now available.


database

Bugs and improvements in various GaBi databases

JIRA

Tracking

Number

Issue

Category


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


CHP, technology mix


flue gas

cleaning|production

mix, at power

plant|1kV - 60kV

{a6a5307e-c7e2-

4652-b49c-

2e420c6474e5}




67


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


CHP, technology mix


flue gas

cleaning|production

mix, at power

plant|1kV - 60kV

{7edf130d-6aca-

4ced-91c5-

dc6f4a15f826}




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}




US Electricity grid

mix – MISO agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{417f8c6e-5f72-

46b2-8003-

d2917d6a4c21}




US

Electricity grid

mix – MISO

(direct)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{7efc72ca-fd35-

4640-8177-

22c9dea8056c}




US

Electricity grid

mix – MISO

(indirect)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{5133b2ab-007f-

4e3f-9749-

923e0a81bb3e}




US

Electricity grid

mix – MROW

(without MISO)

agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{7775fa36-2c06-

44a6-ae0b-

3dc667aeaca6}




68



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}




US

Electricity grid

mix – MROW

(without MISO)

(direct)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{2ba17680-609c-

4ffc-a828-

03f3db065792}




US

Electricity grid

mix – MROW

(without MISO)

(indirect)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{bf02b93e-2e09-

497f-8896-

6c5f024a82ef}




US Electricity grid

mix – NYISO agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{fede8c88-969e-

4347-8ba1-

c8aa9a356877}




US

Electricity grid

mix – NYISO

(direct)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{7d286e2c-f52e-

4a8a-b4c0-

f22e76cc5b4d}




US

Electricity grid

mix – NYISO

(indirect)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{330f5642-8fab-

495d-b18c-

10984fb1b3ae}




US Electricity grid

mix – PJM agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{f9738a8a-dd78-

4845-b0af-

3ea6446eaae0}




69



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}




US

Electricity grid

mix – PJM

(indirect)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{5fa99c49-f2ef-

43c3-9bc8-

9b0ee8b4e1d3}




US

Electricity grid

mix – RFCW

(without MISO

and PJM)

agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{a8c5e505-42f5-

4984-b7d8-

6c1e1c35c694}




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}




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}




US

Electricity grid

mix – SRTV

(without MISO)

agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{84f11faf-8e53-

4a55-89e8-

6d7d04869579}




US

Electricity grid

mix – SRTV

(without MISO)

(direct)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{01a0e9e6-35ea-

4328-aab3-

503f9b1cbc97}




70



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}




US

Electricity grid

mix – SRVC

(without PJM)

agg ts

AC, technology

mix|consumption

mix, to

consumer|<1kV

{dd999fe0-f9a2-

43a3-845a-

c1e374c90b96}




US

Electricity grid

mix – SRVC

(without PJM)

(direct)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{b0562f1a-6490-

41fb-a773-

33a70e5eba46}




US

Electricity grid

mix – SRVC

(without PJM)

(indirect)

agg ts

AC, technology

mix|consumption

mix, at

consumer|<1kV

{a8052d6f-9758-

45d0-8447-

c3715e1d834d}




US

Electricity grid

mix 1kV-60kV -

MISO

agg ts

AC, technology

mix|consumption

mix, to

consumer|1kV -

60kV

{ef64dda8-0cab-

48e9-9a09-

8166456ad634}




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}




US

Electricity grid

mix 1kV-60kV -

NYISO

agg ts

AC, technology

mix|consumption

mix, to

consumer|1kV -

60kV

{4c97783d-9f76-

4942-8e7f-

a3736aa70ab1}




71



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


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


HU

Process steam

from hard coal

90%

agg ts

technology mix


flue gas

cleaning|production

mix, at heat

plant|MJ, 90%

efficiency

{138cb3e6-bf41-

43ff-9697-

ed9acef6c591}

If relevant, please


from thinkstep for


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


from thinkstep for


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


from thinkstep for


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


from thinkstep for


72



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}




HU

Process steam

from hard coal

95%

agg ts

technology mix


flue gas

cleaning|production

mix, at heat

plant|MJ, 95%

efficiency

{8d5b0bd7-2fdf-

458d-a3a6-

67ecc159cbd3}




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}




gabi databases 2017 edition€¦ · gabi 2017 databases are ground-breaking in this respect. land...

Documents