gabi databases 2019 edition · 2019. 2. 14. · gabi databases february 2019 . 2019 edition please...

GaBi Databases

February 2019

2019 Edition

Please read this document carefully, as it contains:

- Important information regarding changes in the databases

- Details on changes in process datasets and on cross-cutting changes

- Information on new datasets

- Information on discontinued datasets

Upgrades & improvements

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About this document

This document covers relevant changes in the upgraded LCI datasets of the GaBi Databases. The document will address both methodology changes and changes in technology, if any, and is struc-tured by material or topic, e.g., electricity, metals, plastics, renewables. It also covers newly added datasets to the database.

In the Annex you will find the list of datasets that are no longer updated.

thinkstep uses a professional issue tracking software (JIRA), so the issue numbers in the tables are issue numbers from this software.

Key changes and affected datasets

In the following paragraphs, you will find a short summary of the most important changes that

took place in this year’s upgrade.

Important database-wide changes made in the 2019 database edition include:

- Energy update: all energy-related datasets, such as electricity, thermal energy, fuels and

the like, have been updated in line with the latest available, consistent international energy

trade and technology data. Please see Chapters 2.6 and 2.7 for more information.

- Land use: Land use for underground mining has been implemented in the database.

- Capital goods: Building infrastructure is now implemented for waste water treatment, com-

plementing the situation for the various types of renewable and fossil power plants and for

waste incineration. Separate datasets for transport infrastructure and production sites are

available, which now allow users to calculate the impact of building infrastructure consist-

ently throughout the entire life cycle.

- Halogenated substances: Since the use of certain halogenated substances has been

banned following the implementation of the Montreal Protocol, the following emissions are

not present anymore in the updated thinkstep datasets: Halon (1301), R 11 (trichlorofluo-

romethane), R 114 (dichlorotetrafluoroethane) and R 12 (dichlorodifluoromethane) and R

22 (chlorodifluoromethane). Particularly R22, which has been removed, has the profound

effect of reducing the remaining, already greatly reduced ODP impacts by several orders

of magnitude for most datasets. This consequently further reduces the impact results for

ODP for many datasets in the database.

- Primary energy correction of wood and wood-based datasets: when using an economic al-

location along the life cycle, primary energy needs to be adjusted to guarantee a proper energy

balance. For wood and wood-based datasets, the primary energy has been adjusted accordingly,

based on the fresh mass of the related material considering the upper calorific value (measured

at oven-dry status and upscaled linearly for water content). The related elementary flow docu-

menting the renewable primary energy is the flow “Primary energy from solar energy.”

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Additionally, the following issues resulted in noteworthy changes, which we would like to high-

light:

- Update of the quantity “Price”: The quantity “Price” was updated using mainly Euro-

stat data, and some unit conversion errors have been corrected. This leads to certain

changes in the database, where economic allocation on these specific flows is used.

However, concerning the update of GaBi datasets, the change due to this price update

was mostly moderate to low. For a complete list of the changes, please see the table

in Annex III of this document.1

- BF Steel water balance: The topic of water assessment in LCA generally made a

significant step toward better definition and standardization of methods and character-

ization. Therefore, the water balance of BF steel could be improved. To close the water

balance of the blast furnace steel route, rain water was added and the blue water con-

sumption double checked and corrected in relationship to the most recent water ele-

mentary flows. Previously too, a lot of water was being emitted compared to the water

entering production. This has now been corrected. This change led to an improvement

toward a correct water balance and blue water consumption (for some steel sheet and

steep pipe datasets, the blue water consumption changed from negative to positive

values, from about -0.7 kg to 1.4 kg per kg of product).

- Infrastructure and land use information for waste incineration: For waste incinera-

tion, building (as capital good) and land use information was added. This addition

slightly increases the impact of all waste incinerations and introduces land use values

for this step. In waste incineration for ferro metals, another effect appears that de-

creases the (overall very small) impact of GWP by another 30%. Ferro metals do not

cause CO2 emissions because of the lack of substantial embodied C and only during

the consumption of auxiliary energy to run the plant. Therefore, the crediting of the

recovered iron components is influencing the overall low absolute GWP value. The

1 Any update will overwrite prices that have been changed by clients. Should this affect you and to avoid this in the future, please always create own quantities for your needs.

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effect is caused by slight change in the supply chain logistics of the recovered—and

thus credited—iron ore and certain regionalization effects of the applied waste water

treatment. - Waste water treatment plant update: Due to our cooperation with End-of-Life experts

from TH Bingen, this year the waste water treatment plants received an update and

correction. With new advancements in measuring technology leading to new data from

a relevant scientific paper, diffuse emissions to air could now be included. Infrastructure

and land use were added to the datasets for consistency purposes, however they do

not significantly influence the overall results. Electricity consumption and the most im-

portant emissions to water could now be matched with the latest DWA (Deutsche Ver-

einigung für Wasserwirtschaft, Abwasser und Abfall e.V.) statistics for German waste

water treatment plants in 2016. Additionally, the calculation of the sludge output of the

pre-thickening and dewatering was recalculated and consistently reduced, as prior as-

sumptions could be identified as being too conservative. In the supply chain, a waste

water plant is generally of rather low absolute significance. Therefore, even slight

changes in details cause high relative changes in the waste water plant as such. The

sludge update leads to a relative decrease of about 80% of the GWP. When applicable,

regional waste water treatment plants are now used in the country-specific datasets.

This change especially improves the precision of US-specific datasets in the EP cate-

gory, where impacts increase due to the regional differentiation.

- Use of global copper dataset: Copper is produced and traded globally. In order to be

more representative, the GLO: copper mix dataset will replace the German copper mix

dataset. This substitution mainly influences the AP and PM impact categories, and de-

pending on the amount of copper used, the impacts will increase moderately to sub-

stantially (up to a maximum of 150% for AP and 140% for PM).

- German cement update: The German cement datasets have been updated using in-

formation from VDZ (Year 2015).2 The main changes were done in the clinker produc-

tion, such as with the fuel mixture and emissions update. The resulting decrease can

be seen mainly when looking at Portland Cement: here AP decreases by about 40%,

POCP by about 30% and EP by about 15%.

- Update of ships: Emission factors were updated and now use consistently data ac-

cording to the IMO GHG report 2014.3 The fuel consumption calculation related to DWT

was updated and new discrete fuel consumption values from IMO GHG report 2014

2 VDZ Umweltdaten 2015

3 Third IMO Greenhouse Gas Study 2014

https://www.vdz-online.de/fileadmin/gruppen/vdz/3LiteraturRecherche/Umweltdaten/VDZ-Umweltdaten_2015.pdf

http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf

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are now used. Impacts decrease slightly overall, except for the category related to par-

ticle emissions, where the impacts increase up to 90% because of the updated emis-

sion factors.

- US trucks update: Relevant emission factors from EPA MOVES have been used to

update the US truck datasets. Apart from updating existing emission factors in these

US datasets, additional ones, such as Benzene and Nitrogen monoxide for VIUS da-

tasets and benzene and ammonia for SmartWay datasets, have been added. CO2 and

SO2 calculations remain unchanged, because they are calculated from fuel consump-

tion and are not based on emission factors from MOVES.

- End-of-Life update of building technology: End-of-Life for building technology da-

tasets were updated, as the amount and kind of used steel in the initial production was

harmonized with the related amount of steel and stainless steel in EOL for more appro-

priate crediting concerning the different recovered steel types.

- Aluminium: For datasets from Brazil and Ukraine, the ingot production was updated.

Recently available and updated IAI data was used.

- Gypsum mining: Dust emissions were added to the mining step. Now the gypsum

mining - like all other mineral mining processes in GaBi - is also inventoried with the

related dust emissions consistently.

- Update of Australian EPDs: 45 new EPD datasets from FWPA in Australia are now

available in addition to over 100 updated ones in the Professional database.

Further details and the related rationale are provided in Chapters 2 ff.

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Authors:

Dipl.-Ing. Steffen Schöll [email protected]

Dipl.-Geoökol. Ulrike Bos [email protected]

MSc. Morten Kokborg [email protected]

Prof. Dr.-Ing. Thilo Kupfer [email protected]

Dr.-Ing. Martin Baitz [email protected]

Dr. Lionel Thellier [email protected]

Dipl.-Ing. Alexander Stoffregen [email protected]

Dipl.-Ing. Jasmin Hengstler [email protected]

Dr.-Ing. Marc-Andree Wolf [email protected]

______________________________________________________________________________________________________________________________________________

thinkstep AG Hauptstr. 111 – 113, 70771 Leinfelden-Echterdingen, Germany Phone: +49 711 341 817-0 Fax: +49 711 341 817-25 E-mail: [email protected] Websites: www.thinkstep.com www.gabi-software.com _______________________________________________________________________________________________________________________________________________

mailto:[email protected]










http://www.thinkstep.com/

http://www.gabi-software.com/

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List of Contents

List of Figures ....................................................................................................... 9

List of Tables ........................................................................................................ 9

Abbreviations ...................................................................................................... 10

1 Introduction to the upgrade of databases available with GaBi .......... 11

2 GaBi Databases 2019 Edition ............................................................... 12

2.1 Principles ........................................................................................................... 12

2.2 Reasoning behind this document ....................................................................... 13

2.3 Regionalization .................................................................................................. 14

2.3.1 Land Use ........................................................................................................... 14

2.3.2 Regionalized Water ........................................................................................... 14

2.4 LCIA Method and factor updates and corrections .............................................. 14

2.4.1 ISO 14067 for GHG reporting ............................................................................ 14

2.4.2 Environmental Footprint (EF) ............................................................................. 14

2.4.3 Single substances.............................................................................................. 15

2.5 New datasets ..................................................................................................... 17

2.6 Inventories for electricity, thermal energy and steam ......................................... 18

2.7 Inventories for primary energy carriers ............................................................... 32

2.8 Organic and inorganic intermediates.................................................................. 36

2.9 Inventories for metal processes ......................................................................... 41

2.10 Inventories plastic processes ............................................................................. 43

2.11 Inventories for End-of-life processes .................................................................. 44

2.12 Inventories for electronic processes ................................................................... 46

2.13 Inventories for renewable processes.................................................................. 48

2.14 Inventories for transport processes .................................................................... 51

2.15 Inventories for construction processes ............................................................... 54

2.16 Inventories for US regional processes ............................................................... 58

3 Industry data in GaBi ............................................................................. 62

4 General continuous improvements ...................................................... 67

4.1 Editorial ............................................................................................................. 67

4.2 LCIA Methods, Normalisation and Weighting factors ......................................... 68

4.3 Fixing and improvements of cross cutting aspects ............................................. 71

References .......................................................................................................... 75

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Annex I: “Version 2018” discontinued datasets – Explanations and Recommendations ................................................................................. 76

Annex II: EPDs with expired validity ................................................................. 82

Annex III: Price quantity changes ..................................................................... 84

Annex IV: Biogenic carbon content quantity changes .................................... 88

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List of Figures

Figure 1: Process structure in GaBi databases 11

Figure 2: Development grid mix in Germany (left) and EU-27 (right) [Eurostat 2018] 19

Figure 3: Development grid mix United States [EIA 2017] 19

Figure 4: PED, GWP, EP, POCP and AP of electricity grid mixes DE, EU-27 and US 24

Figure 5: Changes in GWP of electricity grid mix datasets in GaBi Professional 2019 Edition 25

Figure 6: Absolute GWP of electricity grid mix datasets in GaBi Professional 2018 & 2019 Edition 25

Figure 7: Development GWP for electricity supply in selected countries 28

Figure 8: Changes in GWP electricity grid mix datasets in GaBi Extension Module Energy 2019 28

Figure 9: Absolute GWP of electricity grid mix datasets in GaBi Extension module Energy 2018 & 2019 29

Figure 10: Development GWP for electricity supply in selected countries 29

Figure 11: Absolute GWP of electricity grid mix datasets in GaBi Extension module Full US 2018 & 2019 30

Figure 12: Changes in GWP electricity grid mix datasets in GaBi Extension Module Full US 2019 31

Figure 13: 26 eGRID subregions 32

List of Tables

Table 1: Energy carrier mix for electricity generation—selected EU countries (calculated based on [IEA 2018]) 20

Table 2: Energy carrier mix for electricity generation—selected non-EU countries (calculated based on [IEA 2018]) 20

Table 3: Energy carrier mix for electricity generation—countries with significant changes (calculated based on [IEA 2018]) 21

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Abbreviations

AP Acidification Potential ADP Abiotic Depletion Potential BAT Best Available Technique B2B Business-to-Business B2C Business-to-Customer CHP Combined Heat and Power Plant CML Centrum voor Milieuwetenschappen (Institute of Environmental Sciences) EF Environmental Footprint EP Eutrophication Potential EPS Environmental Priority Strategies (LCIA method) EPD Environmental Product Declaration GWP Global Warming Potential ILCD International Reference Life Cycle Data System LCA Life Cycle Assessment LCI Life Cycle Inventory LCIA Life Cycle Impact Assessment ODP Ozone Depletion Potential PED Primary Energy Demand POCP Photochemical Ozone Creation Potential UBP Umweltbelastungspunkte (Ecological Scarcity Method) For chemical elements, the IUPAC nomenclature is applied. Country codes use the ISO 3166-1 alpha 2 2-letter code, plus a few 3-letter codes for regions, such as RER for Europe, RNA for North America and GLO for global. The different combinations of the European Union, reflecting its growth over time, are identified by the prefix EU and the Number of Member States (potentially plus “EFTA” when including the countries of the European Free Trade Association, i.e., Iceland, Liechtenstein, Norway and Switzerland).

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1 Introduction to the upgrade of databases available with GaBi

In total, around 50 thinkstep employees were involved in the upgrade of several thousand unit pro-cesses and aggregated LCI datasets. The invested time, knowledge and dedication of our employees resulted in the new GaBi Databases 2019 Edition, with about 12,500 plans and processes of the regular Professional and Extension Databases, plus more than 2,000 processes as Data-on-Demand-only datasets.

The process of continuous upgrades to the GaBi Databases is enabled and supported with domain expertise along the team structure within thinkstep, which is illustrated in the figure below.

Figure 1: Process structure in GaBi databases

In the GaBi Databases, process documentation is directly integrated in the datasets. Additional infor-mation about the modelling principles applied to all datasets can be found in the document GaBi Da-tabases and Modelling Principles.4 Furthermore, specific modelling information on specific topics can equally be accessed on the GaBi Software website.

This document covers relevant changes in the upgraded LCI datasets of the GaBi Databases. The document will address 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 da-tasets have been upgraded.

4 http://www.gabi-software.com/index.php?id=8375

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Methodological changes do not automatically imply endorsement by thinkstep, but have been intro-duced when necessary. Methodological changes are only useful if these changes or improvements are supported by relevant best practise cases, evolving or edited standards or by relevant stakeholder initiatives with a respective practice approval.

2 GaBi Databases 2019 Edition “Facts do not cease to exist because they are ignored.” – Aldous Huxley

2.1 Principles thinkstep introduced the annual upgrade of the GaBi databases for three reasons:

• To keep your results as up-to-date and close to evolving supply chains as possible, including automated upgrades of your valued work in alignment with the most current state.

• To avoid disruptive changes caused by multi-year intervals that are often hard to communicate and interpret and that prolong the time that user results are affected by known data errors.

• To keep track of necessary methodological changes and implement them promptly.

thinkstep’s databases are based on technical facts and are internationally accepted and broadly ap-plied. We preferably use standardized methods established by industry, science and regulatory au-thorities. New methods are applied when they have proven to be based on a relevant standard, on broadly and internationally accepted approaches or when enforced by relevant regulations.

Changes in datasets are often the result of many effects in supply chains. 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 on the basis of databases that aim to reflect technological reality.

Changes in the environmental profile of the datasets, from the predecessor GaBi Databases to the most recent GaBi Databases, may be attributed to one or more of the following factors:

• Upgrade of the foreground and/or background systems. The market situation or newly available technologies result in changed impacts. The environmental profile for the supply of energy carriers or intermediates may be subject to short-term changes and affect the environ-mental profile of virtually all materials and products to a varying extent. For example, a change of the energy carrier mix or of the efficiency for electricity supply, changes the environmental profile of all materials or products 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 or a reduction of material losses and of process emissions. Sometimes, the technology is sub-jected to higher quality requirements that are defined further downstream at the final product-level (e.g., more end-of-pipe measures to reduce emissions, stricter desulphurization of fuels) and improved use phase performance. In addition, certain production routes might have been phased out, have changed the production mix of a material, substance or energy. A frequently

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changing and quite dynamic example are the electricity grid mix datasets, as some countries 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. Further harmonization and improvement in the LCA methodology and feedback from clients and employees have enhanced the modelling approach for the GaBi Databases. Detailed in-formation is given in the document GaBi Databases and Modelling Principles.5 Methodological adoptions are carried out extremely carefully, passing through multiple levels of reviews by thinkstep experts responsible for standardization, technology knowledge and quality assur-ance. This internal review process was audited within the continuous improvement process by our external verification partner DEKRA. GaBi database updates and upgrades focus on reli-ability through consistency to ensure clients system models and results are not jeopardized due to random methodological changes.

The degree of influence of each of these factors is specific to each process and cannot be generalized for all cases, nor can a single factor be highlighted. However, as technological excellence is a core value of thinkstep data, our focus is to update and apply ALL RELEVANT AND IMPORTANT improve-ments 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 pro-cesses along the chain) may occur.

2.2 Reasoning behind this document GaBi systems—e.g., leading to a single aggregated dataset—consists of multiple datasets within one supply chain. This means, users could identify 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 to provide realistic data. Reduction of complexity is only credible if the reality of the supply chains is still ade-quately mirrored. 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 user’s 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 one user, whereas in another user’s system, the changes might be irrelevant. To shorten the time for users to reflect on the relevancy of the GaBi database changes for their own systems, the analyst function of GaBi Software may support you in an effective way. As a means of guiding users to the relevant changes in their models that are due to changes in external factors and GaBi background data upgrades, thinkstep also provides the present document “GaBi Databases 2019 Edition - Upgrades and Improvements” in addition to the document “GaBi Databases

5 http://www.gabi-software.com/index.php?id=8375

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and Modelling Principles,” complemented by close to 14,000 interlinked electronical documentation files of the processes supplied with thinkstep databases.

The following sections will address the most relevant changes in the GaBi Databases for the different areas.

2.3 Regionalization

2.3.1 Land Use For land use assessment, the regionalization in mining and renewable resources datasets (agricultural and wood biomass) as well as incineration, which cover the most important sectors of land occupation and transformation, was further implemented and harmonized. Land use inventory flows for all culti-vation and forestry processes as well as mining and waste incineration processes were checked and updated, and missing information has been integrated. Land occupation [m²*a] and land transfor-mation [m²] inventory information for mining processes were improved through updating the produc-tion quantity and size of open pit mines and implementing land use flows for underground mining. Furthermore, the occupied land use mining area was separated into “dump site”, “mineral extraction site” and industrial area.” Thus, a more complete and further differentiated evaluation of land use impacts is possible.

2.3.2 Regionalized Water The regionalization of water flows was further implemented and expanded to the waste water treament plants. The input and output water flows (input: ground water, river water, lake water; output: pro-cessed water to groundwater, processed water to lake, processed water to river) are regionalized by the country of the waste water treatment plant.

For further information regarding water assessment and how to ensure correct and coherent region-alization at the input and output side, please see documentation in “Introduction to Water Assessment in GaBi.”

2.4 LCIA Method and factor updates and corrections

2.4.1 ISO 14067 for GHG reporting

Four new quantities from ISO 14067 GWP (based on IPCC AR5) are now available. They use the same characterization values as IPCC AR5, but split into fossil, biogenic, land use and aviation.

2.4.2 Environmental Footprint (EF) The Environmental Footprint (EF) set of impact factors have been updated to version 2.0. Apart from various more specific changes, the most visible update is the introduction of three sub-impacts to climate change, namely fossil, biogenic and land use. These three impacts sum up to the total impact.

IMPORTANT NOTE:

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With release of the GaBi Databases 2019 Edition, the official EF 2.0 characterisation factors are pro-vided, as well as the mapping to the official units and official elementary flows via the ILCD export/im-port function.

EF 2.0 is the only version to be used for PEF/OEF results and to create EF data as ILCD export file. Do not use previous versions of EF characterisation factors and ILCD zip archives anymore! Earlier versions of EF/ILCD LCIA methods and flow lists have no official status and datasets developed with earlier versions may not be claimed EF-compliant. In case you have been using a previous version of EF characterisation factors, please update any created dataset by re-export, respectively re-calculate results using the EF 2.0 in GaBi (datasets created by users should also be double-checked with recent official EF documents, before claiming compliance). In case you need any support with this topic, please contact [email protected]

Additional information: EF 3.0 is in parallel online at the EC website, since December 2018. This version may be used exclusively in context of new to-be-developed PEFCRs/OEFSRs in the transition phase (and is hence not yet included in GaBi).

2.4.3 Single substances

• GWP characterization factors for the substances HCFC 142b and R 134a were corrected. • Long-term emission of halogenated substances flows carry now GWP factors: R 134a (tetra-

fluoro ethane), R 143a (trifluoroethane), R 113 (trichloro trifluoroethane), R 141b (dichloro-1-fluoroethane), R 152a (difluoroethane), R 114 (dichlorotetrafluoroethane), R 142b (chlorodi-fluoroethane), R 124 (chlorotetrafluoroethane), R 115 (chloropentafluoroethane), R 116 (hex-afluoroethane), R 125 (pentafluoroethane), Halon (1211), Halon (1301), R 13 (chlorotrifluoro-methane), R 32 (difluoromethane), Tetrafluoromethane.

• A set of characterization factors for unspecified Cresol were calculated as an average of the three isomers (ortho-, meta- and para-cresol) for ReCiPe, CML and USEtox.

• The long-term emissions of phosphorus are now available for several impact categories. • The energy flow ‘Gas, mine, off-gas, process, coal mining’ used to be characterized via the

net calorific value in primary energy categories similar to, for example, natural gas in the GaBi implementation of EF 1.8. However, the flow is not present in the official list of characterization factors from EF 1.8 and EF 2.0 and has now been removed.

• Ecoinvent provided updated calorific values for three elementary energy flows which increased by 30-50% in the Ecoinvent 3.5 database: hard coal, brown coal and peat. The primary energy characterization of these flows will increase. Crude oil and natural gas changed very little (2-3%).

• Several land use flows related to GWP were re-introduced into the GaBi database to remain consistent with the EF2.0 flow list:

o Carbon dioxide, from soil or biomass stock (long-term) o Carbon monoxide, from soil or biomass stock o Carbon monoxide, from soil or biomass stock (long-term) o Methane, from soil or biomass stock o Methane, from soil or biomass stock (long-term)

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• UK land use flows are removed, because they were already present as GB flows. The differ-ence in land use characteristics between the two (Great Britain vs United Kingdom) is assumed negligible.

• Land use flows without regional specification in the LANCA methodology were regionalized, some causing changes of 30-100% to the LANCA impacts.

• The EPD flows PERM, PENRM, PERE and PENRE have been deleted. They are not used in EPD-pilot solutions and carry a significant risk of double counting primary energy if not used properly.

• Water flows with regional characterization were added for Saudi Arabia • EPD EN 15804 flows were not linked to the respective EN 15804 impacts—this has been

corrected. If GaBi users were implementing EPD results directly using the EN 15804 flows, these were previously ignored, but are now characterized. This is only relevant if users were manually implementing finished EPD results (LCIA results). No standard datasets were af-fected.

• Waste heat [Other emissions to air] had a characterization factor for POCP in ReCiPe 2016 v1.1, which was removed.

• SOx was not characterized as emission in ReCiPe 2016 v1.1 and is now corrected. • Four water input flows used in the Ecoinvent database were a factor of 1,000 too low in the

WSI and AWARE water methodologies. Since output flows were correctly characterized, this led to negative water consumption for Ecoinvent processes. This has been corrected.

• The Ecoinvent flow for water and turbine use, unspecified natural origin was wrongly charac-terized in the GaBi implementation of UBP 2013 water resource quantity, leading to negative water consumption for Ecoinvent processes. This has been corrected.

• Other water flows were corrected in UBP2013 leading to a 30-50% decrease in the character-ized water footprint.

• Land use flows to/from/use as permanent crops in Costa Rica were not characterized in LANCA, which has now been corrected.

• Phosphorus as emissions to water (P total) had mistakingly received a toxic classification in ReCiPe 1.08. This has been corrected, leading to a nearly 100% decrease in toxicity for this specific flow. Other impact methods are not affected.

• CAS number of 1-butene was corrected to 000106-98-9 with 025167-67-3 as synonym. • Butane and iso-Butane both with CAS 000075-28-5 were merged and characterization factors

aligned. • CAS code of SO3 was corrected from 007746-11-9 to 007446-11-9. • Two flows of tin ore were not consistently characterized. The flows were merged. • Vulcanized natural rubber had wastewater emissions set as water vapour. This has been cor-

rected. • Flow duplicates were merged:

o Aldehydes to fresh water o Aluminium to air o Aluminium to sea water

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o Sodium to fresh water o Sodium to industrial soil o Sodium to agricultural soil

• Phosphorus minerals had been characterized for resource depletion with the inverse value (1.81E05 instead of 5.52E-06) in ‘CML’ and ‘ILCD 1.09 Resource use, minerals and metals’ and was corrected. The effect could go from being completely dominating on the resource depletion to being close to zero.

• The name of the flow Butylglycol was changed to 2-Butoxyethanol

2.5 New datasets

With this year’s upgrade, 379 new processes and 141 new plans are available:

Professional DB: 3 new plans, 162 new processes

Third party datasets, GLO: copper mix, production of ships, different electricity, regionalized tap water…

Extension DBs:

II “Energy”: 1 new plan, 84 new processes

Future electricity grid mixes (2025, 2030, 2040), gasoline mix E5 and E10, differ-ent “EU-28 electricity from …” mixes

IXa “End of Life”: 13 new processes

Bulk waste truck, US: hazardous waste incineration plants, open biomass burn-ing,…

New extension database IXb “End of Life parametrized models”:

137 new plans, over 40 unique new processes, plus many complementing pro-cesses of energy sources, consumables, for crediting

XIV “Construction”: 24 new processes

CN/BR/SA Portland cement, several EPDs

XVII “Full US”: 27 new processes

Different waste incineration plants, Iron ore mix…

XX “Food&Feed,” XXI ”India,” XI “Electronics,” Ib “Inorganics”: jointly 25 new processes

IC produced in India, corn and grain drying, Ammonia, canola oil…

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Details on the new datasets are available in this MS Excel file: http://www.gabi-software.com/filead-min/GaBi_Databases/Database_Update_2019_DB_content_overview.xlsx and access to the com-plete dataset documentation is available for searching and browsing by extension database online under http://www.gabi-software.com/international/databases/gabi-data-search/.

2.6 Inventories for electricity, thermal energy and steam

Relevant changes in energy carrier mix for electricity generation after the upgrade

In the GaBi databases 2019, the reference year is 2015 for all electricity grid mixes and energy

carrier mixes. As an exception, the electricity grid mixes in the Extension Module XVII: Full US

(electricity grid mixes for US sub-grids and subregions under eGRID) have been updated from

reference year 2014 to 2016, using the most recent version of eGRID [EPA 2018].

Relevant changes in the life cycle inventory (LCI) of the upgraded national grid mix datasets

occur for a couple of countries, because of 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 is to increase the share of renewable energy in their

electricity generation, which is the case for Austria, Belgium, Denmark, Estonia, Finland,

Germany, Great Britain, Greece, Ireland, Lithuania, Romania and Sweden, for example.

• Annual fluctuation in electricity generation from hydropower (availability of water for elec-

tricity generation) due to meteorological conditions. In 2015, lower water availability for

hydropower compared to 2014 resulted in higher shares of fossil fuels, for example in

Austria, Croatia, Italy, Latvia, Portugal, Serbia, Slovenia and Venezuela. In contrast,

higher water availability in Finland, Sweden and Turkey resulted in distinct higher elec-

tricity output from hydro power plants.

• As in previous years, several transition countries have an ongoing demand for increased

electricity production: In countries like China, India, Egypt, Saudi-Arabia, Vietnam or Tur-

key, electricity production has increased by 4 to 12%. In China, in contrast to previous

years, the extra electricity demand was not covered by electricity from coal. Around one

third of the 180 TWh of the increased electricity generation (5,860 TWh total gross pro-

duction) was generated by hydropower. The remaining increase in China was predomi-

nantly generated from nuclear, wind, natural gas and photovoltaic. In India, Egypt, Saudi

Arabia and Vietnam, the increased electricity demand was met by production from fossil

fuels, mostly coal or natural gas.

The following three figures present the development of the energy carrier mix for electricity gen-

eration in Germany, the European Union and the United States between 2000 and 2015.

http://www.gabi-software.com/fileadmin/GaBi_Databases/Database_Update_2019_DB_content_overview.xlsx

http://www.gabi-software.com/fileadmin/GaBi_Databases/Database_Update_2019_DB_content_overview.xlsx

http://www.gabi-software.com/international/databases/gabi-data-search/

19

Figure 2: Development grid mix in Germany (left) and EU-27 (right) [Eurostat 2018]

Figure 3: Development grid mix United States [EIA 2017]

Compared to 2014, the use of renewable energy sources for electricity generation in Germany

has increased from 27.0% to 30.0%6 in 2015. The main driver of this increase in renewable

energies was electricity from wind. Generation from combustible, fossil fuels decreased from

56.4% to 54.7%.

For the EU28, the share of natural gas in the power mix increased from 14.5% in 2014 to 15.5%

in 2015, after a significant reduction from 22.8% in 2010. The generation from renewable energy

6 50 % of electricity from waste is accounted as renewable energy

20

carriers increased slightly, from 29.4% in 2014 to 30.0% in 2015. The increase was mainly driven

by an increase in wind power generation.

In the U.S., the trend of coal substitution by natural gas was also happening in 2015, decreasing

the share of coal use in the grid mix significantly, from 38.5% to 33%. The share of natural gas

for electricity generation increased from 27.4% to 32.5%.

In the following tables, the energy carrier mixes for 2014 and 2015 are displayed for selected,

economically most-relevant countries, or those with important changes.

Table 1: Energy carrier mix for electricity generation—selected EU countries (calculated based on [IEA 2018])

[ %] France Germany Great Britain Italy Poland Spain

2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 2014 2015 Nuclear 77.6 77.0 15.5 14.2 18.8 20.7 0.0 0.0 0.0 0.0 20.6 20.4 Lignite 0.0 0.0 24.9 23.9 0.0 0.0 0.3 0.3 33.6 32.0 1.1 1.2 Hard coal 1.7 1.7 19.0 18.3 29.8 22.3 15.2 15.0 47.9 47.1 14.7 17.1 Coal gases 0.4 0.4 1.7 1.8 0.3 0.3 1.1 0.8 1.3 1.5 0.5 0.5 Natural gas 2.3 3.5 10.0 9.8 29.7 29.5 33.5 39.3 3.4 3.9 17.0 18.7 Heavy fuel oil 0.3 0.4 0.9 1.0 0.5 0.6 5.1 4.7 1.0 1.3 5.1 6.1 Biomass (solid) 0.3 0.4 1.9 1.7 4.1 5.7 1.4 1.4 5.8 5.5 1.4 1.4 Biomass (Biogas) 0.3 0.3 5.0 5.2 2.4 2.1 4.5 4.6 0.5 0.6 0.3 0.3 Waste 0.7 0.7 2.2 2.0 1.2 1.9 1.7 1.7 0.0 0.0 0.5 0.5 Hydro 12.3 10.5 4.1 3.9 2.6 2.7 21.6 16.6 1.7 1.5 15.4 11.2 Wind 3.1 3.7 9.2 12.3 9.4 11.9 5.4 5.3 4.8 6.6 18.7 17.6 Photovoltaic 1.1 1.3 5.8 6.0 1.2 2.2 8.0 8.1 0.0 0.0 2.9 2.9 Solar thermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 Geothermal 0.0 0.0 0.0 0.0 0.0 0.0 2.1 2.2 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

Table 2: Energy carrier mix for electricity generation—selected non-EU countries (calculated based on [IEA 2018])

21

[ %] Brazil China India Japan Russia USA


Table 3: Energy carrier mix for electricity generation—countries with significant changes (calculated based on [IEA 2018])

[ %] Belgium Denmark Greece Netherlands Portugal Turkey


22

The following list summarizes countries with significant changes in their energy carrier mix for

electricity generation:

• Belgium (BE) Due to a shutdown of several nuclear reactors (Doel 1, Doel3 & Ti-

hange2) in 2014 the generation from nuclear power decreased significantly from 46.6%

to 37.2%, the lower output was compensated for with natural gas (increase from 26.7%

to 32.5%), generation from renewables (increase from 19% to 22.5%) and higher elec-

tricity imports.

• Croatia (HR) Relevant lower output from hydro power stations (decrease from 67.3%

to 57.5%) resulted in higher generation from combustible fossil fuels (increase from

25.8% to 32.7%).

• Denmark (DK) An ongoing trend to increase the installed wind capacity and a higher

rate of annual full load hours resulted in a distinctly higher share of wind power at the

grid mix (increase from 40.6% to 48.8%). The share of renewable energies increased

from 55.6% in 2014 to 65.2% in 2015, substituting electricity from hard coal.

• Estonia (EE) The share of generation from oil shale has been reduced from 82.8% to

76.7%. In absolute terms, generation from oil shale decreased from 10.3 TWh to 8 TWh.

A significant part of the decreased generation from oil shale power plants was compen-

sated for with imports.

• Great Britain (GB) Output from all renewable energy technologies rose, resulting in

an increase from 20.3% in 2014 (7.6% in 2010) to 25.6 % in 2015. Wind power increased

from 9.5% in 2014 to 11.9% in 2015. Electricity from hard coal dropped from 29.8% to

22.3%.

• Greece (GR) The share of generation from lignite power plants dropped from 51% in

2014 to 42.6% in 2015, compensated for with natural gas, hydro power and wind power.

• Netherlands (NL) A part of the electricity from natural gas (decrease from 49.9% in

2014 to 42.3% in 2015) was substituted by electricity from coal (increase from 28.6% in

2014 to 36.1% in 2015).

• Portugal (PT) The electricity output from hydro power stations dropped significantly

from 16.4 to 9.8 TWh, the relative share decreasing from 31.1% to 18.7%. Generation

from renewable energies without hydro power stayed stable at around 30%. To compen-

sate for the lower generation of hydro power, generation from fossil fuels (mainly hard

coal and natural gas) increased from 38.2% to 50.8%.

23

• Sweden (SE) Higher electricity output from hydro power stations and wind power

increased the share of renewable energy sources in the grid mix from 55.7% in 2014 to

63.1% in 2015. The electricity from hydro and wind power substituted mainly electricity

from nuclear power (decrease from 42.2% to 34.8%).

• Slovenia (SI) Due to the lower output from hydro power stations, the share of elec-

tricity from lignite has increased from 19.3% to 26.5%.

• Turkey (TR) The share of electricity from natural gas dropped from 47.9% to 38.0%

due to higher output from hydro power plants.

Development GWP and other impact categories for electricity grid mix datasets

The following figures illustrate the absolute primary energy demand (PED), as well as global

warming potential (GWP7), acidification potential (AP7), eutrophication potential (EP7) and pho-

tochemical ozone creation potential (POCP7) per kWh of supplied electricity in Germany, the

European Union and the United States. In the 2019 edition databases, the emission factors for

the combustion of fuels in power plants have been kept unchanged compared to the 2018 edi-

tion, with the exception of the eGRID subregions (Extension Module XVII: Full US - electricity

grid mixes for US sub grids and subregions under eGRID) for which new data from eGRID 2016

was available. Therefore, the results are mainly influenced by the changes in the energy grid

mix, by changes in the power plant efficiencies and by changes upstream in the supply chains.

In Germany, the GWP for the electricity mix decreased from 592 g CO2-eq./kWh in 2014 to

569 g CO2-eq./kWh in 2015, mainly due to increased gross production from wind power and

photovoltaics. Whereas the production from fossil combustible fuels remained stable, at 353

TWh, and a slight reduction of nuclear power, from 97 TWh to 92 TWh, generation from renew-

ables increased from 169 TWh to 194 TWh. Increasing overall gross production from 628 TWh

in 2014 to 647 TWh in 2015. The increase in renewable PED was driven by the increase of

electricity from renewable energy sources. Changes in AP, EP and POCP are low and are linked

to changes of the energy carrier mix.

For the EU28, the GWP (417 g CO2-eq./kWh in 2014 vs. 418 g CO2-eq./kWh in 2015), but also

AP, EP and POCP, remained almost unchanged. For electricity generation, the use of renewa-

ble resources increased only slightly, from 29.4% to 30.0%, natural gas increased from 14.5%

to 15.5% and nuclear decreased from 27.6% to 26.6%.

7 CML 2001, Updated Januar 2016

24

In the U.S., the GWP decreased from 614 g CO2-eq./kWh in 2014 to 585 g CO2-eq./kWh in

2015. The main reason for the decrease in GWP is an ongoing trend in the U.S. to substitute

hard coal (decrease from 37.3% in 2014 to 32.1% in 2015) with natural gas (increase from

26.8% in 2014 to 31.8% in 2015). EP has been decreased by 8%, AP by 12% and POCP by 9%

due to reduced combustion emissions from fossil power plants (mainly coal power plants).

Figure 4: PED, GWP, EP, POCP and AP of electricity grid mixes DE, EU-27 and US

25

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 com-

pared to the 2018 edition (reference year 2014) data, as well as the absolute greenhouse gas

emissions per kWh in the 2018 and 2019 edition databases (reference year 2015).

Figure 5: Changes in GWP of electricity grid mix datasets in GaBi Professional 2019 Edition

Figure 6: Absolute GWP of electricity grid mix datasets in GaBi Professional 2018 & 2019 Edition

For most cases, the changes in the national electricity grid mix datasets are related to the up-

graded energy carrier mix or imports:

26

• Austria (AT) Due to lower electricity from hydro power station (decrease from 68.5%

to 62.2%), compensated for mainly with natural gas, the GWP increased from

309 g CO2-eq./kWh in 2014 to 356 g CO2-eq./kWh in 2015.

• Belgium (BE) Due to a temporarily shut down of several reactors (Doel 1& 3, Tihange

2) during most of 2015, the share of nuclear power dropped in the grid mix from 46.6%

in 2014 to 37.2% in 2015. Around two thirds of the electricity was substituted by natural

gas, the rest by renewable sources (mainly wind and biomass).

• Denmark (DK) The carbon intensity of the electricity supply in Denmark has been

further decreased from 352 g CO2-eq./kWh in 2014 to 248 g CO2-eq./kWh in 2015. The

30% decrease in greenhouse gases per supplied kWh electricity is related to higher a

generation from renewable sources (55.6% in 2014 to 65.2% in 2015), mainly realized

through higher capacities and output from wind power plants (share in domestic electric-

ity generation increased from 40.6% to 48.8%). Like the year before, relevant parts of

the electricity supply have been imported (36%), mostly from Sweden and Norway (84%)

with low carbon intensities. Importion from Germany, with a distinct higher carbon inten-

sity than the domestic production, has been reduced from 32% to 16%.

• Finland (FI) Compared to 2014, the GWP per supplied unit of electricity in Finland

has decreased by 17.5% from 212 g CO2-eq./kWh in 2014 to 175 g CO2-eq./kWh in

2015. The decrease is related to higher electricity outputs from hydro power plants, a

higher production from wind power plants and a decrease from coal power plants, from

11.7% in 2014 to 7.5% in 2015.

• Great Britain (GB) Due to an increase of electricity generation from renewable

sources (20.3% in 2014 to 25.6% in 2015) and an accompanied decrease of power gen-

eration from coal (29.8% in 2014 compared to 22.3% in 2015), the GWP decreased from

477 g CO2-eq./kWh to 417 g CO2-eq./kWh.

• Greece (GR) Electricity production from lignite was significantly reduced from 51% in

2014 to 42.6% in 2015, partly compensated for with higher production from natural gas

power plants and renewable resources. The GWP per kWh of electricity decreased from

987 g CO2-eq. in 2014 to 861 g CO2-eq. in 2015.

• Portugal (PT) As with years past, e.g., 2011/2012, lower water availability for power

generation (decrease from 31.1% in 2014 to 18.7% in 2015) resulted in higher output

from fossil power stations (hard coal and natural gas) and an increase of the carbon

intensity for power generation (380 g CO2-eq./kWh in 2014 compared to 472 g CO2-

eq./kWh in 2015).

27

• Spain (ES) Similar to Portugal, lower water availability for hydro power generation

resulted in higher GWP values (increase from 345 g CO2-eq./kWh in 2014 to 415 g CO2-

eq./kWh in 2015). The lower output from hydro power stations was compensated for with

fossil power stations (hard coal and natural gas).

• Sweden (SE), France (FR) The big relative GWP change for France and Sweden is

a result of the high sensitivity of changes in the energy carrier mix on electricity grid

mixes with low carbon intensities. In Sweden, the GWP decreased from 45g CO2-

eq./kWh in 2014 to 37g in 2015 due changes related the amount and origin of imported

electricity. In France, the GWP increased from 56 g CO2-eq./kWh in 2014 to 64 g in 2015,

mainly due to lower generation from hydro power, which was partly compensated for

with natural gas generation.

• Switzerland (CH) The increase from 131 g CO2-eq./kWh in 2014 to 163 g CO2-

eq./kWh in 2015 is related to higher imports (28 TWh in 2014 compared to 34 TWh in

2015) and a higher share of imports from Germany. Exports to Italy remained stable at

around 35 TWh.

• Slovenia (SI) The carbon intensity of the Slovenian grid mix has been increased from

335 g CO2-eq./kWh in 2014 to 386 g CO2-eq./kWh in 2015 because of lower output from

hydro power stations, compensated for with lignite power stations.

The following Figure 7 illustrates the GWP of the electricity supply in selected countries over the

last six years. Compared to 2008, the GWP in Germany has been reduced by 9% and in the EU

by 14%. The share of renewables for power generation has increased significantly, from 15% in

2008 to 30% in 2015, substituting mostly nuclear power and electricity from natural gas power

stations. In the U.S., the substitution of electricity from hard coal by electricity from natural gas

as well as a higher share of electricity from renewables, has decreased the GWP per kWh of

supplied electricity by 12%. In some of the EU Member States, relevant GWP reductions have

been achieved over the last seven years, mainly because of a substitution of fossil fuels by

renewable sources, e.g., Denmark -52%, Estonia -33%, Finland -44%, Great Britain -29%, Italy

-24%, Malta -34% and Romania -29 %. Small contributions to changes over the past 6 years

stem from error corrections and method adoptions.

28

Figure 7: Development GWP for electricity supply in selected countries

The following three figures illustrate the relative and absolute changes of the GWP for the elec-

tricity grid mix datasets in the extension module Energy, as well as the changes over time.

Figure 8: Changes in GWP electricity grid mix datasets in GaBi Extension Module Energy 2019

29

Figure 9: Absolute GWP of electricity grid mix datasets in GaBi Extension module Energy 2018 & 2019

• Argentina (AR) The increasing GWP (483 g/kWh in 2014, 540 g/kWh in 2015) is a

consequence of lower output from hydropower plants and increased demand, compen-

sated by higher generation from natural gas and fuel oil.

• Turkey (TR) Electricity output from hydro power increased from 40 TWh to 67 TWh,

resulting in a relative share of 25.7% in 2015 compared to 16.1% in 2014, reducing the

share of natural gas in the grid mix from 48% to 38%. Consequently, the GWP dropped

from 694 g CO2-eq./kWh in 2014 to 613 g in 2015.

• Venezuela (VE) Electricity output from hydro power stations dropped from 87 TWh

(68.3%) in 2014 to 75 TWh (63.7%) in 2015, increasing the carbon intensity of the elec-

tricity supply.

Figure 10: Development GWP for electricity supply in selected countries

30

Extension module XVII: Full US – electricity grid mixes US subregions

Figure 11 and Figure 12 illustrate the absolute and relative changes in GWP of the eGRID sub-

regions, as well as the five sub grids and the US average using data from eGRID2016 instead

of IEA data to calculate the energy grid mix. For the subregions (see Figure 13 to get an over-

view) and sub grids in GaBi, the reference year has been updated from 2014 in GaBi data sub

grids to 2016 in GaBi database 2019.

Figure 11: Absolute GWP of electricity grid mix datasets in GaBi Extension module Full US 2018 & 2019

The changes in GWP are mostly related to the updated energy grid mixes and partly to changes

in combustion plant efficiencies, updates in the supply of energy carriers and infrastructure.

• AKGD Increase in GWP from 543 g CO2-eq./kWh in 2014 to 688 g in 2016 is mainly

related to a decrease in conversion efficiency for natural gas power plants and to a minor

extent related to changes in the energy grid mix.

• AZNM The increasing GWP (483 g CO2-eq./kWh in 2014 to 562 g in 2015) is pre-

dominantly influenced by lower output from low-carbon-intensive electricity generation

(nuclear, hydro, photovoltaics) compensated for with power generation from coal (share

increase from 21.3% in 2014 to 29.7% in 2016).

• HIMS & HIOA The increase in GWP is for both subregions related to lower conversion

efficiencies of fuel oil power plants.

31

• NYCW Change in GWP due to lower output from nuclear power stations, compen-

sated for with natural gas power stations. Increase of GWP from 367 g CO2-eq./kWh in

2014 to 417 g in 2016.

• NYUP Decrease in GWP is a result of lower generation from hard coal power plants

and a higher share of nuclear and renewables.

• RMPA The share of electricity from hard coal power plants was significantly reduced

from 68.1% in 2014 to 51% in 2016 due to distinct higher output from hydro power sta-

tions (3.1% in 2014, 12.5 in 2016), higher generation from wind power and substitution

of coal by natural gas. Consequently, the GWP per kWh dropped from 831 g CO2-eq. in

2014 to 677 g in 2016.

• SPSO Similar to RMPA, the share of electricity from hard coal was significantly re-

duced, reducing the GWP from 744 g CO2-eq./kWh in 2014 to 666 g in 2016.

• Alaska Mostly influenced by changes within subregion AKGD.

• Hawaii See explanation for HIMS & HIOA.

Figure 12: Changes in GWP electricity grid mix datasets in GaBi Extension Module Full US 2019

Other impact categories, such as acidification or eutrophication, are in addition to the updated

energy carrier mixes also affected by updated emission factors for combustion power plants.

32

Figure 13: 26 eGRID subregions

Further developments in electricity datasets

Changes in electricity datasets from specific fuels:

Power plant efficiencies, calculated based on energy 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),

• correction of statistical errors,

• a combination of several factors listed above

2.7 Inventories for primary energy carriers The reference year of the GaBi databases 2019 edition is 2015 for all energy carrier supply mixes, like hard coal, crude oil and natural gas. The changes in the environmental impacts of the energy carrier processes are described here.

33

Changes in the results of the lignite and hard coal mixes are related to the update of the country-specific consumption mixes (mix of domestic production and imports) and changes in the background data. All country-specific lignite and hard coal mixes show GWP changes of less than 5% except the following mixes:

• Lignite mix of Turkey (TR) GWP decreases by 6 % due to changes in background data. • Hard coal mix of Austria (AT) decreasing shares of hard coal from Czech Republic and

increasing shares of hard coal from Russia lead to a slightly lower GWP result (- 5%). • Hard coal mix of Belgium (BE) less hard coal imports from the United States and Australia

and significant higher imports from Russia increase the GWP by 12 %. • Hard coal mix of Brazil (BR) more hard coal from Colombia, Australia and Russia is imported

and less from the United States and Canada, which lead to an increase of 6% for GHG emis-sions.

• Hard coal mix of Italy (IT) the GWP increases by 10% due to less imports from the United States, Canada and Spain and more imports from South Africa and Colombia.

• Hard coal mix of Slovenia (SI) significant changes in the consumption mix (imports from Colombia with a contribution of 41% to the consumption mix were substituted by imports from Russia, Italy, Czech Republic and Germany) worse the GWP result by 11%.

The environmental impacts of the natural gas mixes changed due to the update of the country-specific consumption mixes and changes in the background data. Additionally, heavy metal emissions to water associated with the natural gas production were updated, effecting the toxicity potentials of the natural gas mixes significantly (JIRA tracking number: GC-4944). Natural gas mixes with changes in the GWP results higher than 5% are listed in the following:

• Natural gas mix of Austria (AT) Austria's domestic natural gas production is drastically re-duced and compensated by natural gas imports from Russia. As a result, the GWP increases by 14%.

• Natural gas mix of Belgium (BE) less natural gas imports from the Netherlands and Norway, and higher imports from the United Kingdom and Qatar (via liquefied natural gas (LNG) transport) increase the GWP by 11%.

• Natural gas mix of the European Union (EU-28) slightly higher impacts (GWP + 6%) due to increasing natural gas imports from Russia and decreasing imports from the Netherlands.

• Natural gas mix of India (IN) the GHG emissions increase by 10% because India’s domestic natural gas production and LNG imports from Qatar are reduced and substituted by LNG im-ports from Nigeria, Equatorial Guinea and Australia.

• Natural gas mix of Lithuania (LT) less natural gas imports from Russia and more imports from Norwegian LNG lead to a reduction in the GWP result by 7%.

• Natural gas mix of the Netherlands (NL) the GWP result of the Netherlands increases by 13% due to less domestic natural gas production and more imports from Norway, the United Kingdom and Russia.

• Natural gas mix of Spain (ES) decreasing shares of natural gas and LNG from Norway, Trinidad and Tobago and Qatar, and increasing shares from France, Algeria and Nigeria worsen the GWP result by 9%.

34

• Natural gas mix of Thailand (TH) less domestic natural gas production and increasing LNG imports from Qatar and natural gas imports from Myanmar lead to an increase of the GWP result by 8%.

• Natural gas mix of Ukraine (UA) The Ukraine expands its domestic production and replaces natural gas imports from Germany, Hungary and Norway by Russian natural gas which re-duces the GWP result by 9%.

• Natural gas mix of United Kingdom (GB) the GWP result increases by 8% due to less im-ports from the Netherlands and increasing LNG imports from Qatar.

The update of the country-specific consumption mixes and changes in the background data effect the environmental impacts by more than 5% of the following crude oil mixes:

• Crude oil mix of Australia (AU) the GWP result is reduced by 11% due to lower crude oil imports from Nigeria, Congo and Russia, a higher domestic crude oil production and more crude oil imports from the United Arab Emirates and Brunei.

• Crude oil mix of Austria (AT) the GHG emissions increase by 5% because of major changes in the crude oil consumption mix: less domestic production, less crude oil imports from Kuwait, Nigeria, Kazakhstan, Saudi Arabia and Libya, but more imports from Mexico, Iraq, Tunisia, Algeria and Azerbaijan.

• Crude oil mix of Finland (FI) less crude oil from Norway and the United Kingdom and higher imports from Russia and Angola lead to an increase in the GHG emissions by 9%.

• Crude oil mix of France (FR) the GWP result increases by 6% due to major changes in the consumption mix: less crude oil imports from Saudi Arabia, Libya, Norway, Russia and Egypt, but more imports from Iraq, Angola, Mexico and Azerbaijan.

• Crude oil mix of Hungary (HU) higher impacts (+ 21% for GWP) are caused mainly by the reduction of crude oil imports from Russia and higher crude oil imports from Iraq.

• Crude oil mix of India (IN) a more detailed data source is used for the crude oil consumption mix of India leading to an increase of the GHG emissions by 7%.

• Crude oil mix of Ireland (IE) the GHG emissions are reduced drastically (- 55 %) due to decreasing shares of crude oil imports from Libya, Nigeria and Algeria and significant more crude oil imports from Norway.

• Crude oil mix of Italy (IT) major changes in the mix (less crude oil from Canada, Russia, Saudi Arabia, Italy, Libya, Colombia and Norway and more crude oil from Iraq, Gabon, Egypt, Congo, Azerbaijan and Angola) lead to higher GHG emissions (+ 9%).

• Crude oil mix of Lithuania (LT) less crude oil imports from Russia and more imports from Algeria, Nigeria, Azerbaijan and Iraq increase the GHG emissions by 11%.

• Crude oil mix: of New Zealand (NZ) major changes in the mix (less crude oil from Indonesia, the United Arab Emirates, Kuwait, Saudi Arabia, Nigeria and Australia and more crude oil from Qatar, Brunei, Russia and Malaysia) reduce the GWP result by 7%.

• Crude oil mix of Poland (PL) less crude oil imports from Russia and higher imports from Iraq and Saudi Arabia lead to an increase in the GHG emissions by 11%.

35

• Crude oil mix of Spain (ES) the GWP result increases by 6% due to higher crude oil imports from Kazakhstan, Gabon, Iraq and Brazil and lower imports from Russia, Colombia, Saudi Arabia and Cameroon.

• Crude oil mix of Sweden (SE) less crude oil imports from Nigeria and Russia and more crude oil imports from Denmark reduce the GHG emissions by 6%.

• Crude oil mix of Thailand (TH) a more detailed data source is used for the crude oil con-sumption mix of Thailand leading to a GWP reduction by 20%.

• Crude oil mix of Trinidad and Tobago (TT) the use of a more detailed data source for the crude oil consumption mix of Trinidad and Tobago results in higher GHG emissions (increase by 20%).

• Crude oil mix of Turkey (TR) the GWP result increases by 5% because of higher crude oil imports from Iraq and Russia and less crude oil imports from Nigeria, Iran, Kazakhstan, Turkey and Saudi Arabia.

• Crude oil mix of the United States (US) the domestic production increases and substitutes crude oil imports from Saudi Arabia, Canada, Mexico and Kuwait which reduces the GWP result by 6%.

• Crude oil mix of South Africa (ZA) a more detailed data source is used for the crude oil consumption mix of South Africa leading to an increase of the GHG emissions by 18%.

Changes in the environmental impacts of the refinery products can be mainly related to changes in the crude oil supply:

• Refinery products of Australia (AU) GWP reduction by 6% to 9%. • Refinery products of India (IN) 5% to 6% higher GWP results. • Refinery products of South Africa (ZA) the GWP results increase by 5% to 16%. • Refinery products of the African region (RAF) 5% to 7% higher GWP results. • Refinery products of the European Union (EU-28) increased GWP results by 5% • Refinery products of France (FR) increased GWP results by 5%.

The environmental impacts of the fuel mixes (diesel and gasoline, at refinery and filling station) change because of updated country-specific biofuel and fossil fuel consumption mixes, the update of the coun-try-specific blending quota of biofuels and changes in the biofuel and crude oil supply chains. The following country-specific fuel mixes show GWP changes of more than 5%:

• Gasoline mix of Australia (AU) the GWP result is reduced by 7% because of the reduction of GHG emissions in the crude oil supply chain of Australia.

• Gasoline mix of India (IN) changes in the crude oil supply lead to an increase of the GWP result by 5%.

• Gasoline mix of South Africa (ZA) the GWP result increases by 9% to 10% due to the in-creased GWP result of the crude oil supply.

• Gasoline mix of Africa (RAF) higher GHG emissions for the crude oil supply result in a higher GWP result for the gasoline mix (+ 5%).

• Diesel mix of Australia (AU) the GWP result increases by 6% to 7% which is caused by the correction of the blending quota (miscalculation of the blending quota in the data source).

36

• Diesel mix of India (IN) changes in the crude oil supply lead to an increase of the GWP result by 5%.

• Diesel mix of Malaysia (MY) changes in the GWP result (+ 11%) are mainly related to changes in the supply chain of bioethanol and crude oil.

Table 2- 1: JIRA issues for new energy datasets

JIRA Tracking Number

Issue Cate-gory

Item Description Change in re-sults

Affects Ex-tension module

GC-3928 New Thermal en-ergy/steam mixes for re-gions

"EU-28: Process steam from biogas" is now available.

New datasets Professional database

GC-4258 New New EU-28 electricity pro-duction mix

A new plan "EU-28: Electric-ity grid mix (production mix)" is now available in the Pro-fessional database.


GC-6492 New New DE: Elec-tricity grid mix (2017)

A new process is now availa-ble reflecting the German grid mix using 2017 data.

New dataset Extension database II: energy

2.8 Organic and inorganic intermediates Possible updates and upgrades of technologies may happen on 3 different levels. In the upgraded 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 technologies providing the same product, and lastly, due to changes and updates in the background system of resources and energy supply. In addition, errors in the data can affect a single dataset or several when the product is used downstream.

The required information to check and update the technologies and supply chains is based on the knowhow of our engineers as well as on information shared by our clients who are active in the chem-ical sector. The provided documentation of GaBi datasets serves as a 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 the like) as a basis to maintain and update chemical LCA data. All chemical technologies were checked in this sense. In relation to pos-sible 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

37

• 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 per-formance of the energy supply and the important resource, crude oil and natural gas.

The following table documents the issues in this sector, the principle effect on the results (if any) and the affected extension databases. Issues with a larger affect on single or multiple datasets are high-lighted with a bolded JIRA number:

Table 2- 2: JIRA issues for organic and inorganic intermediates


Issue Category

Item Description Change in results Affects Ex-tension mod-ule

GC-2519 New Special glass da-tasets

3 new datasets for spe-cial glass types are now available: EU-28: Glass lamp bulbs (soda-lime - BREF) EU-28: Glass ceramic production EU-28: Borosilicate glass production


GC-4361 New New Hy-drogen peroxide datasets

Four new datasets for hydrogen peroxide for Belgium, Netherlands, France and Germany are now available.

New datasets Professional DB

GC-5792 Editorial Renaming of hydro-gen da-tasets

The Plastics Europe and the thinkstep hy-drogen datasets were renamed adding the production route in the name. The naming of all aggregated and partly aggregated da-tasets was harmonized by using brackets. This consistent naming helps to find all hydro-gen data at the same place (e.g., by using the GaBi search func-tion or by using the da-tabase content Excel list available at the homepage).

Does not change the re-sults

All

38


Issue Category


GC-5913 Bug Mass bal-ance of Tereph-thalic acid

The mass balance was closed by adding air as input which is used in the oxidation process. The water balance was also closed by slightly increasing the water vapor output. Natural gas input was changed to thermal en-ergy from natural gas since it is not used as material but as energy.

Acidification Potential rises by about 2% Eutrophication Potential rises about 3% Global Warming Potential rises about 5% Photochem. Ozone Crea-tion Potential rises about 2%"

Professional database Extension da-tabase XVII: full US

GC-6040 Docu-mentation

Erase Glauber's salt syno-nym from sodium sulphate

Synonym removed from documentation


All

GC-6357 Bug Correction of Purified tereph-thalic acid (PTA) pro-duction

Xylene on the PTA pro-duction was replaced by p-xylene, as PTA production requires p-xylene.

Since a new step has been added, all the impacts for PTA production increased: - +8 to +20% for Tox indi-cators and ozone layer de-pletion - +35% to +70% for Re-sources indicators, Acidifi-cation, Eutrophication, Smog and POCP - +80 to +120% for Abiotic elements and GWP PET production US is im-pacted (Polyester resin, and PET via PTA) - +20 to +45% for Acidifica-tion Resources (ADP fossil elements) Eutrophication Smog and POCP - +67% for GWP Partly bio-based PET is also impacted (from sugar cane, from corn and from Wheat) - +30 to +55% for Abiotic and resources indicators - +60 to 110% for GWP


Ali-phatic/aro-matic co-polyester documen-tation: fos-sil based

It was added in the documentation (general comment), that those processes fossil based but biodegradable


Several

39


Issue Category


but biode-gradable

GC-7046 Improve-ment

Ammonia production - use of by-product carbon di-oxide

In general, the ammo-nia plant is producing considerable amount of carbon dioxide. For smaller plants or plants which are not inte-grated in urea produc-tion, it is more likely that the CO2 is vented to air. For bigger, inte-grated ammonia plants, the CO2 is recovered and used as by-product or is directly used reac-tant in the urea synthe-sis. For the datasets which are considering CO2 re-covery, the amount of the by-product CO2 was increased. Further-more, the process CO2 emissions (approx. 500 kg per ton Ammonia) from the primary re-former were removed in the GaBi model, as these emissions are al-ready accounted via the provided thermal energy. Finally the allocation between carbon dioxide and Ammonia has been updated to the latest prices. Thus, 85% of the environmental im-pacts are allocated to ammonia and 15% to the by-product Carbon dioxide.

Correction distribution of CO2: Due to this change, the GWP is reduced for ammo-nia datasets with CO2 re-covery by approx. 10% to 30%. For the datasets CO2 by-product ammonia, the GWP is reduced by 50%, AP, EP POCP and primary energy demand by approx. 40%. The changes affect Melamine and Urea (Stami carbon process) where CO2 and ammonia are used as precursor materi-als. Updated price allocation for all ammonia/CO2 plan models: in the old price al-location share, 60% was distributed to CO2 and 40% to Ammonia which is now 15% to CO2 and 85% to Ammonia. For the common impact methods like AP, EP, POCP, GWP and pri-mary energy demand, this allocation change leads to an increase of approx. 40% to 70%. This change also leads to an increase in the LCIA impacts of the "Urea agrarian" datasets and other fertilizers like CAN, MAP, UAN and NPK.

Extension da-tabase Ib: in-organic inter-mediates Extension da-tabase XVII: full US


Use of re-gionalized deionised water

Country specific deion-ised water processes are now used through-out the database.

Water consumption does not change, however when looking at regionalized wa-ter quantities impacts can vary.

Several

GC-7177 New New am-monia da-tasets

New datasets for am-monia production In Europe, Germany and Great Britain are now available. - Ammonia (NH3) with CO2 recovery, by-prod-uct carbon dioxide (economic allocation)

New datasets Professional database Extension da-tabase Ib: in-organic inter-mediates

40


Issue Category


- Ammonia (NH3) syn-thesis with CO2 recov-ery, by-product carbon dioxide (without alloca-tion) In the last scenario, no allocation is applied for the two products am-monia and carbon diox-ide. This means, the output inventory shows both ammonia and car-bon dioxide as product flows. It enables the user to connect these flows to further use (e.g., a part of the amount of ammonia and carbon dioxide could be used to model an own urea production plant where both mate-rials are needed as feedstock).


Correction of density Hydrochlo-ric acid

Density is now correct. Does not change the re-sults

Several


Documen-tation in deionised water

Documentation for da-tasets Deionised water have been updated.


Professional database

GC-7363 LCIA Water split between global and regional

The water regionaliza-tion is now set correct for the following pro-cesses: DE: Tap water from surface water US: Process water from ground water US: Process water from surface water

No major change on re-sults



Documen-tation im-provement for hydro-gen perox-ide da-tasets

Included datasets and flow diagram were added to the documen-tation


Several

41

2.9 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.

The main changes this year are the exchange of German copper with a global copper mix, the cor-rection of the water balance in BF steel production and the introduction of the new IAI datasets.

Table 2- 3: JIRA issues for metal processes


Issue Cate-gory

Item Description Change in results Affects Exten-sion module

GC-2498 New EUROFER stainless steel da-tasets

Two new datasets from EUROFER for stainless steel are now available: EU-28: Stainless steel cold rolled coil (430 Stab.) EU-28: Stainless steel white hot rolled coil (430 Stab.)



Update UA/ CN / BR Al-uminium in-got/sheet/profile

Brazilian aluminium in-got replaced by South American IAI 2015 data Chinese aluminium in-got 2009 data replaced by Chinese IAI 2015 data Ukrainian aluminium profile and sheet pre-cursor data changed from EU to RU (IAI 2015) as IAI 2015 data includes "Russia and other Europe" (non-EU-28) US Calcium sulfoalumi-nate cement alumina (GLO) input updated to IAI 2015 data

BR: Aluminium ingot mix AP: -7.9% GWP: 8.4% EP: -22.9% ODP: -31.4% POCP: 12.6% CN: Aluminium ingot mix AP: 17.3% GWP: 9.6% EP: 13.9% ODP: -17.0% POCP: 19.3% UA: Aluminium profile AP: -13.9% GWP: -24.0% EP: -36.0% ODP: -69.3% POCP: -7.7% UA: Aluminium sheet AP: -14.4% GWP: -24.5% EP: -40.2% ODP: -76.5% POCP: -7.4% US: Calcium sulfoaluminate cement AP: 4.3% GWP: 28.8% EP: -5.7% ODP: -49.1% POCP: 31.1%

42


Issue Cate-gory



Harmoni-zation of Silicon and Ferro sili-con data

The world share mix of Ferro-Silicon production was updated based on information from United States Geological Sur-vey (USGS). For the Chinese and Russian production, the electric-ity consumption in the smelter furnace was re-duced from 43 MJ/kg Fe-Si (90%) to 36 MJ/kg fe-Si (90%), based on information from ULLMANN'S Encyclo-paedia of Industrial Chemistry and Best Available Techniques (BAT) Reference Docu-ment. The carbon bal-ance in the smelter fur-nace was updated which leads to higher CO2 emissions.

Due to the change, the common impacts methods AP, EP and POCP are re-duced by approx. 30% to 40%. The primary energy demand from ren. and non ren. resources are reduced by approx. 14%. The Global Warming Potential (GWP) is only reduced by approx. 1%, since less electricity is used but higher process CO2 emissions leads to a nearly unvaried overall re-sult.


GC-5811 Bug Elementary Cadmium input to Cadmium

Elementary Cadmium input is now 1.1 kg in-stead of 1kg.

Abiotic Depletion (ADP ele-ments) increases by 10%

Extension da-tabase V: non-ferrous metals

GC-6303 New New IAI da-tasets

38 datasets from IAI (In-ternational Aluminium Institute) are now avail-able in the database. They can be found un-der Processes -> Indus-try data -> IAI



Use of global cop-per mix

Copper is globally pro-duced and traded. For this reason, a German copper mix was re-placed with the GLO: copper mix.

High changes in single cases take place especially for AP. This is due to the dif-ference in both DE and GLO datasets, which have a difference of 155% in AP (higher value for GLO)

Several


Check us-age of ear-lier version Aluminium Data

Process IN: Alumina, precursor Bauxite min-ing changed from IN: Bauxite (2005 data) to GLO: Bauxite (2015 data)

Changes are below 2%. Extension da-tabase XXI: In-dia

GC-7379 Bug Water con-sumption in DE BF steel pro-duction

The water balance of the blast furnace steel route was corrected. Due to a double count-ing, more water was be-ing emitted than enter-ing the production. Rain

Blue water consumption in-creases for the steel da-tasets. The relative changes are quite high, for the absolute numbers the increase of blue water con-sumption is about 2 to 2.5

All

43


Issue Cate-gory


water has been added as additional input.

kg. For some steel sheet and steep pipe datasets, the blue water consumption changed from negative to positive values.

GC-7572 GC-6703 GC-6195

New Updated Copper DKI/ECI datasets

Three datasets from DKI/ECI are now availa-ble in an updated ver-sion: EU-28: Copper pipe mix (Europe 2015 {8e82d244-1022-4d20-a862-46c3f54f6379} EU-28: Copper Sheet Mix (Europe 2015 {d4587458-3dd0-4c6e-a1f8-73d440813310} EU-28: Copper Wire Mix (Europe 2015 {35a4b3f7-6e52-4e31-9894-e09d72bc0367}


2.10 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 com-pletely new process designs in polymerization are in industrial use compared to last year. The polymerization technologies in the GaBi Databases are considered representative. This is supported by our experience within the chemistry and polymer industries.

More specific aspects are mentioned in the following table:

Table 2- 4: JIRA issues for plastic processes


Issue Cate-gory



GC-6182 New Update of injec-tion moulding

A new plastic injection mould-ing unit process updated with new information is now avail-able. Also, the function was changed to be a parameter-ized process with free param-eters electricity input and waste output. The previous unit process has been moved to the Version 2018 folder.

The default elec-tricity consump-tion was reduced by about 30%. Since this is the only impact con-tributor, impact results reduced by 30% equiva-lently for the in-jection process only.

Profes-sional data-base

44


Issue Cate-gory



GC-7428 Improvement Steam amount in Styrene-buta-diene rubber (S-SBR)

Using newer information, the steam input for the S-SBR production was lowered

Most relevant change occurs in GWP, where the impact decreases by about 8%.

Several

GC-7724 Documenta-tion

Documentation of thinkstep car-bon fiber da-tasets

The documentation of the fol-lowing datasets has been up-dated: - DE Carbon Fiber (CF; from PAN; standard strength) {d2e4cb14-c5fa-49a3-b6c2-840a2b860d63} - EU-28 Carbon Fiber (CF; from PAN; standard strength) {bda5f1b5-719d-4d9b-8802-a4a9e16c0dd2} Has been updated by deleting the statement that the fibres are graphitized. More details about the surface treatment of the fibres after carbonization was added.

Does not change the results

Extension database VII: plastics

2.11 Inventories for End-of-life processes All data and models have been checked by thinkstep metals experts regarding technological upgrades and were identified as representative for their technology descriptions in 2018.

Major changes were done in the waste water treatment plants. Here diffuse emissions are now con-sidered, infrastructure and land use integrated, as well as an inconsistency in the calculation of the sludge amount coming out of the pre-thickening and dewatering corrected. Waste incineration plants now have infrastructure and land use added as well.

Noteworthy is also the newly available “Extension Database IXb: end of life parameterised models,” which contains 137 plan models with pre-configured settings, but which can be changed by the user.

Other more specific aspects are mentioned in the following table.

Table 2- 5: JIRA issues for end-of-life processes


Issue Cat-egory


GC-5886 GC-5888

Improve-ment

Waste wa-ter treat-ment plants: Dif-fuse Emis-sions to air,

Diffuse emissions to air are now incorporated in the Waste Water Treat-ment Plant model, as well as investment goods and land use.

The diffuse emissions will change the results from the WWTP a lot (e.g. +50% for GWP), so this shall be communicated to the cus-tomers as an improvement coming from the latest


45


Issue Cat-egory


Infrastruc-ture and land use added

achievements in measuring rather than being a bug that has been wrong so far. Both investment goods and land use do not have a big effect on the overall results of a product, because the mass of throughput through a WWTP is so high. Nevertheless, it is good that we added this, the EoL processes are now more consistent regarding investment goods and land use than before.


Include capital goods for waste in-cineration plants (fol-low up GC-4550)

Waste incineration da-tasets now have infra-structure and land use included.

Land use will increase when a waste incineration is used.


GC-7063 New Update DE/EU do-mestic waste in-cinerations and create remaining for EU countries

21 new datasets for do-mestic waste incinera-tion for European coun-tries are now available.

New datasets Extension Da-tabase IXb: end of life pa-rameterised models

GC-7181 Documen-tation

Typo in documen-tation of Sludge (hazardous low level)

Typos in the technology description were cor-rected.



Clarifica-tion in doc-umentation of waste incinera-tion pro-cesses

Depending on the ma-terial incinerated, elec-tricity and steam can be exported or is needed. To clarify this, the fol-lowing sentences have been added to the tech-nology description: Materials reacting exo-thermic in the incinera-tion plant lead to en-ergy generation (elec-tricity and steam OUT-put). Materials reacting en-dothermic in the incin-eration plant lead to


Several

46


Issue Cat-egory


energy consumption (electricity and steam Input.

GC-7421 New New da-taset "Glass cul-lets, sorted"

A new dataset for sorted glass cullets is now available: "EU-28: Glass cullet, sorted" {ae26c0a4-c43c-4e55-9426-28402256e592}

New datasets Professional database Extension Da-tabase IXb: end of life pa-rameterised models

GC-7604 New German regions waste wa-ter treat-ment plants

New wastewater treat-ment plants processes and plans for 7 German regions (Baden-Würt-temberg, Bayern, Hes-sen, Nord, Nord-Ost, NRW and Sachsen) are now available.


GC-7675 New New open burning of biomass datasets

Two datasets for open burning of biomass (dung and garbage) are now available and can be used to estimate the impact this method would have.

New datasets Extension da-tabase IXa: end of life

GC-7780 Bug Sludge drying in waste wa-ter treat-ment plant

An inconsistency in the calculation of the sludge amount coming out of the pre-thicken-ing and dewatering was corrected.

Big changes occur due to this correction. GWP, POCP, EP and PM de-crease by about 80%

Several

GC-7735 New New End of Life mixer for different routes and fractions (domestic waste, glass, pa-per…)

137 new plans are now available with the new Extension database IXb: end of life parame-terised models. The plan models use data for Eurostat for waste treatment for domestic waste, glass, plastics and paper. Additionally, three different methods (Cut-off, Avoided bur-den or EF methodol-ogy) can be evaluated.


2.12 Inventories for electronic processes All data and models have been checked by thinkstep electronic experts regarding technological up-grades and were identified as still representative for their technology descriptions in 2018. According to thinkstep electronic experts, any possible differences when comparing the results of impact

47

categories with the same results for 2018 were due to changes in background data in the metals and energy sector (see corresponding chapters in this document).

One noteworthy correction this year concerns some selected ICs, for which the electricity use in the production was corrected.

Table 2- 6: JIRA issues for electronic processes


Issue Cat-egory


GC-6556 Bug Node size for IC BGA 48 (72mg) 8x6 mm MPU ge-neric

The process "GLO: IC BGA 48 (72mg) 8x6 mm MPU generic (14 nm node)" {4818d9f5-6b98-4f52-95b3-428fa7b40223} now has the correct node in the name (14nm) in-stead of 130nm.


Extension da-tabase XI: electronics

GC-6813 Editorial MLCC ca-pacitor flow naming

The "D" in the flow name was removed in the following flows. Capacitor ceramic MLCC 1210 (50mg) 3.2x1.6x1.6 (Base met-als) {a97db42d-f6b8-45b7-adaa-c008f031997f} Capacitor ceramic MLCC 0201 (0.17mg) 0.6x0.3x0.3 (Base Met-als) {7b990070-5670-4a32-918f-95191cfef00a} Capacitor ceramic MLCC 2220 (450mg) 5.7x5x2.5 (Base Met-als) {10b88694-0da7-4b08-a428-74a9367ad188} Capacitor ceramic MLCC 0603 (6mg) 1.6x0.8x0.8 (Base Met-als) {f8db2875-4097-4d05-bdaf-1f315f43af6a}


All

GC-6887 Bug Fab emis-sions for CMOS 65nm sem-iconductor and IC

Parameters for PFC abatement in the model were corrected.

Minor changes in all impact categories (< 3%).


GC-7601 Bug Correction of ICs

Several corrections were done in the IC model. All bond wire + bulk materials pro-cessing electricity was increased. The back-

For most ICs changes are around or below 5%. Larg-est change is for IC DIP 24 (1.7g) 35.5x8.2 mm CMOS logic (250 nm node), where


48


Issue Cat-egory


end electricity factor was increased as well.

changes about 32% for GWP.

2.13 Inventories for renewable processes The datasets, including renewable materials (e.g., crop cultivation), are modelled with a comprehen-sive agricultural model. The model considers local and regional aspects of climate, soil and farming practices on the technical side. In addition, it considers international guidelines, current scientific liter-ature and available databases on the methodological side. The thinkstep agriculture and farming ex-perts maintain and enlarge the model frequently, making it one of the most advanced LCA models related to this topic.

As part of the 2019 annual upgrade the agrarian and renewable processing datasets have been re-viewed and updated based on the most recent information identified by the thinkstep experts consid-ering the aspects previously mentioned. In addition, the documentation of certain datasets has been improved.

The biogenic carbon balance was harmonized in all the foreground and background systems when renewable materials are involved, especially in case the economic allocation approach has been used. The primary energy data has been harmonized and corrected in all the datasets used as fuel where an allocation based on a different reference than mass has been applied.

Table 2- 7: JIRA issues for renewable processes


Issue Cate-gory


GC-5507 Bug Water con-tent for DE: Sawmill sawdust abs. dry (resinous wood)

Wood water content calculator was added to estimate the wood (in wet basis) before the drying process. It is as-sumed that the water content of the wood be-fore drying is 44% (based on the energy requirement of the dry-ing process).

By the implementation of this change the most common LCIA results like GWP, AP, EP, POCP reduce by around 5%

Several


Parameter description for Carbon correction process

The comments of the parameters were im-proved in a way to make it clearer for the user to enter the re-quired information

Does not change the results Professional database


Exchange of fertilizer datasets

A few models were har-monized regarding the use of fertilizer da-tasets. The main ef-fects are on following

Due to the replaced fertilizers datasets, the GWP excl. bio-genic carbon in the winter rape cultivation is reduced by approx. 10 to 15%. For the

Several

49


Issue Cate-gory


data: - Winter rape cultivation - EU Waste water treat-ment plant where ferti-lizer data is used to give credits for the sludge

EU WWTP processes, the environmental impact changes are high. For EU-28: Municipal waste water treatment (mix): GWP excl. biogenic carbon is in-creased by approx. 40%, AP, EP and POPC increased by 20 to 30%. For EU-28: Mu-nicipal waste water treatment (agricultural sludge applica-tion), GWP excl. biogenic carbon is increased by ap-prox. 190%, EP increased by 50% and AP POCP reduced by approx. 200%


Merge two corn flows

Two 12% Water corn flows existed. To avoid confusion only one will be available: Corn, Seeds (12% H2O content) {d95b77d9-8365-453e-8e61-efc5fea58447}

Does not change the results All


Biogenic carbon content in the flow

Quantities C_bio-gen_wt and (if product is 100% biogenic) C_total_wt were added to several product flows.

Does not change the results All. For de-tails please see Annex IV


Leather seat co-vers - pro-cessing step

For the following three processes the final step of cutting and sewing for application as seat covers was added: DE Leather seat cover (10 sqm/9.5 kg) {a6a6524d-9ebe-4899-b369-856ff1cbb200} DE PUR synthetic leather seat cover (10 sqm) {fbcdf61c-480d-4114-a27b-5f72d7a3d7e5} DE PVC synthetic leather seat cover (10 sqm/7.2 kg) {b45c62b9-e81d-4bd8-91b9-15e7a76e057e}

All impacts increase around 15%

Extension database XVI: seat co-vers


Documen-tation for Semi chemical Fluting

Added the following sentence in the field Deviation from LCI: "Al-location by mass be-tween semi chemical and the by-products


50


Issue Cate-gory


Tall oil and Turpentine. Influence of the alloca-tion on the results very low, since only small number of by-products are produced"


Harmoni-zation of used corn datasets

The newer corn dataset is now used in the mod-els.

Almost no change in results.


Replace-ment of wheat plan

For the process "DE: Wheat grains, at farm (14% H2O content)", the production of the grains was updated newer information.

Extension database XX: food & feed

GC-6756 Bug Change of corn pro-cess in up-stream da-taset

The plan of corn grains at farm was replaced by the corn grains dried. Reason of ex-change is that that fungi may grow during the storage and trans-portation of corn when the water content of grains is high. For this application the water content of grains should be lower. EU-28: Thermoplastic starch polymer (TPS), unblended EU-28: Glucose (via starch hydrolysis from corn)

By the implementation of this issue the most common LCIA results such as GWP, EP, AP, ODP and ADP increased by between 3-4%

Extension database XIX: bioplas-tics Extension database XX: food & feed

GC-6757 New New Dry-ing grain unit pro-cess

Two new processes were modelled: GLO Warm-air dryer corn {19e65956-7ba2-43af-9c3f-4cc93c7e1507} GLO Warm-air dryer grain {b79e8945-35cc-469d-9a91-7483d12928db} The processes are meant to replace the following: GLO Drying grain {895cc239-d11f-409c-9cce-4d68d770943b}

New datasets Several

GC-6890 Bug Functional unit of

The reference flow of the leather datasets is changed to 1 sqm as

The results slightly change for all impact categories due

Extension database

51


Issue Cate-gory


leather da-taset

functional unit, respec-tively 0.95 kg painted leather or 0.88 kg un-painted leather.

to the adaption of the func-tional unit.

XVI: seat co-vers

GC-7047 Bug Implemen-tation of al-location in leather splitting

In the leather splitting, no allocation between grain leather and split leather was applied. Due to the implementa-tion of the allocation ac-cording to the PEFCR leather pilot based on the Protein-Nitrogen content, 63% of the en-vironmental burdens are allocated to grain leather and 37% to split leather.

Due to the implementation of the allocation, the common impact methods like GWP, AP, EP, POPC etc. are re-duced by 30% to 50%for the cradle-to-gate leather da-tasets. Looking at the partly aggregated datasets with open input cattle hide, the re-duction is approx. between 20% to 30%.

Extension database XVI: seat co-vers


Documen-tation for FEFCO/ts datasets

The technology de-scription has been up-dated to clarify the origin of the picture



Carbon content in flow quan-tities for US AHEC Wood da-tasets

Flows now have the carbon content in the flow quantity.


2.14 Inventories for transport processes

In this year’s upgrade, global ship transportation datasets as well as US specific trucks received an update.

Table 2- 8: JIRA issues for transport processes


Issue Cate-gory


GC-3189 New New ships production

Datasets for the pro-duction of bigger sized ships are now availa-ble: Container ships: GLO Ocean container ship production (100,000t DWT) {eab89a1a-f9a3-499b-b7ba-9cb7c2861d57} GLO Ocean container ship production (125,000t DWT)


52


Issue Cate-gory


{7a79d755-fc87-446c-be73-e59eeb90607f} GLO Ocean container ship production (150,000t DWT) {d5648855-384a-4ba5-acaa-e1baaeaa10df} GLO Ocean container ship production (200,000t DWT) {d5b9688f-4434-48df-aa5b-e93186954856} GLO Ocean container ship production (50,000t DWT) {cc2520a5-3cd1-4223-b877-f76c5b9bd420} GLO Ocean container ship production (85,000t DWT) {7b35a94e-d6d2-4f7f-ac71-b646c7b4e4ca} Bulk carrier / Tanker GLO Ocean bulk car-rier/tanker ship produc-tion (200,000t DWT) {5c461eb6-7fdc-479c-99bb-460db7c476bc} GLO Ocean bulk car-rier/tanker ship produc-tion (50,000t DWT) {e92b2123-c440-4b44-bad7-3f2285f61d57}


Update trucks tire production and truck tires incin-eration

The production of truck tyres data was im-proved, affecting the truck and truck trailer production. Better data for the chemicals used for vul-canization was imple-mented.

The truck and truck-trailer production datasets show very minor decreases (0-2%) in all impact categories. Only ADP decreases up to 28%.


GC-4718 Editorial Harmoni-zation of "," and "." in truck and car dataset names

Dataset names for truck and cars are now harmonized with cor-rect use of "." and ",".



Adjustment of standard sulphur amount for HFO in ships

The average sulphur amount of Heavy Fuel Oil burned in ocean go-ing ships was adjusted from 2.7 to 2.5 wt.%. The following unit

CML2001 - Jan. 2016, Acidi-fication Potential (AP) [kg SO2 eq.]: -5% CML2001 - Jan. 2016, Photo-chem. Ozone Creation


53


Issue Cate-gory


processes were changed: GLO: Bulk commodity carrier, 20,000 to 200,000 dwt payload capacity, ocean going GLO: Bulk commodity carrier, average, ocean going GLO: Container ship, 27,500 dwt payload ca-pacity, ocean going GLO: Oil tanker, 10,000 – 300,000 dwt payload capacity, ocean going GLO: Oil tanker, aver-age, ocean going

Potential (POCP) [kg Ethene eq.]: -4%

GC-7211 GC-7190

Improve-ment

Replace previous versions of transport with cur-rent ones

Previous versions of transport datasets were replaced by current ones.

Generally, the impacts de-crease.

Several


Update of ships - transporta-tion

Emission factors were updated according to IMO GHG report 2014. The fuel consumption calculation related to DWT was updated and new discrete fuel con-sumption values from IMO GHG report 2014

Most relevant result catego-ries slightly decrease except categories connected to par-ticle emissions, where results increase up to 90% due to new emission factor.



Update of ships - pic-ture in doc-umentation

The picture in the ship transportation u-so pro-cesses show the formu-las that were used to calculate the ship's fuel consumption. These have been updated.


GC-7786 Bug Correction of use da-tasets for woodchip and pellet boiler

Woodchip and pellet boiler use datasets (Module B6) were har-monized regarding use of facilities. All facilities (i.e. the boilers them-selves were removed). Additionally, now the correct wet wood flows are used.

Primary energy demand and climate change are reduced by 60% to 97%. Acidification and eutrophication are around 90% to 103% com-pared to the results before the change. As the biogenic carbon content in the wood flows had to be adapted, for some boiler processes a very little rise in eutrophication re-sulted due to a bigger amount of ash that must be deposed due to a higher bio-genic C content in the wood flow.

Extension database XIV: con-struction ma-terials

54

2.15 Inventories for construction processes Foreground data and models have been checked by thinkstep construction experts regarding techno-logical upgrades and passed. Identified technology improvements were updated in the database. In total, 15 new EPDs datasets have been included in the extension database XIV: construction materi-als. For EPD datasets with expired validity, please see Annex II.

Further changes leading back to the background system (energy, intermediates) are responsible for the remaining differences between GaBi Databases 2018 and 2019 for construction.

Specific aspects for this year’s upgrade are mentioned in the following table.

Table 2- 9: JIRA issues for construction processes


Issue Cat-egory


GC-2708 New Chinese FGD gypsum

New dataset for Chinese FGD gypsum is now availa-ble.

New datasets Extension da-tabase XIV: construction materials

GC-4472 GC-4473

New New EPD datasets for concrete blocks

Two new EPD datasets are now available: DE: Concrete blocks with facing, grey mottled - Kron-imus (A1-A3) {c54abf16-c7a5-4a23-b1f8-b1e8308c94d5} DE: Concrete blocks with facing, sanded surface- Kronimus (A1-A3) {5265acf0-1b70-4a9d-bca0-c8134f5a524a}


GC-4626 New District heat-ing datasets

Four new datasets for dis-trict heatings are now avail-able: DE: District heating from bi-omass (solid) CHP DE: District heating from hard coal CHP DE: District heating from natural gas CHP DE: District heating from waste CHP


GC-5861 New HPL EPD da-tasets from ICDLI EPD

Two new EPD datasets are now available: EU-28: Dec-orative High Pressure Com-pact Laminate (HPL) - ICDLI (A1-A3) EU-28: Decorative High


55


Issue Cat-egory


Pressure Thin Laminate (HPL) - ICDLI (A1-A3)

GC-5880 Bug Credits for iron in mod-ule C4 for waste incin-eration plant

Iron is recovered from the incinerated waste and can be reused. So far, credits for iron were given in mod-ule C4, now they are cor-rectly given in module D.

Module C4 increases in im-pact (due to iron credit now in Module D), module D de-creases.

Extension da-tabase XIV: construction materials


High impacts for siliceous sand

To produce quartz sand, a wastewater treatment was in the model. It has been re-moved for the process "DE: Dried quartz sand (grain size 0/2)

GWP decreases by about 50%, AP also by about 50%, EP decreases by about 70%.


GC-6041 Bug Land Use in clay produc-tion

Land use in the clay pro-duction now has the correct values. This affects clay da-tasets and all datasets us-ing clay.

The impact of land use has decreased drastically.

Several


Improve doc-umentation of ready-mix concrete da-tasets

Foreground model descrip-tion and technological de-scription have been up-dated for ready-mix con-crete datasets



Update ce-ment da-tasets with newest re-lease from VDZ

The German cement da-tasets have been updated using information from VDZ (Year 2015). Main changes were done in the clinker production, such as fuel mixture and emissions up-date.

GWP increases by about 2%. For Portland cement AP de-creases by about 40%, POCP by about 30%, EP by about 14%.


GC-6286 Bug Check IN: Portland slag cement da-taset

The BF slag used now has the correct scaling factor leading to the correct emis-sion profile.

Acidification, Eutrophication and POCP decrease signifi-cantly (by about a factor of 1000).

Extension da-tabase XXI: In-dia


German Ce-ment on CN and IN aer-ated con-crete plans

Changed German cement to country specific cement.

For China: Impacts increase by about 10%


GC-6468 New New EPD datasets of Xella light-weight con-crete

Three EPD datasets for aerated concrete from Xella are now available: DE: Aerated concrete gran-ulate (Ytong®) - Xella (A1-A3 {63d5ed62-294c-413b-9e08-52c8007e88c2}


56


Issue Cat-egory


DE: Aerated reinforced concrete - Hebel/Xella (A1-A3) {5f78fc38-aebf-4827-ba9e-3d7c207ffc19} DE: Aerated concrete (Ytong®) - Xella (A1-A3) {ee4ae595-44af-4bfa-a81a-e0d4488ccf12}


Packaging material on stone wool heating pipe shell

Packaging material is now not included anymore.

Almost no change in results. Extension da-tabase XIV: construction materials

GC-6883 Bug Carbon bal-ance for "Kraft paper (EN15804 A1-A3)"

The carbon balance for the kraft paper dataset was cor-rected.

Global Warming Potential in-cluding biogenic carbon changed from 0.19kg CO2 eq. to -1.1kg CO2 eq.


GC-6916 LCIA kg instead of MJ in EPD ADPF flow

The reference quantity is changed to energy. No di-rect impact on result. If us-ers have manually con-verted energy content of a material into mass, then the calculations might no longer be correct.



Documenta-tion for In-dian slag

Documentation was changed to make the allo-cation used clearer.

Does not change the results Extension da-tabase XXI: In-dia

GC-7129 Bug Conversion factor from kg to piece for electric outlet flow

Conversion factor was cor-rected from 0.0625 kg/piece to 0.077 kg/piece.

Almost no change in results. Extension da-tabase XIV: construction materials


Update doc-umentation picture of air conditioner

The graphic has been up-dated.

Does not change the results Extension da-tabase XIV: construction materials

GC-7210 Bug Precursor for "Joint gasket tape, polyiso-butylene (EN15804 A1-A3)"

The precursor used in the model was checked and re-placed: polybutadiene ter-ephthalate is replaced by Polybutadiene

When looking at CML method, changes are -15% to -30% for ADP el.; ADP fossil; AP; EP; GWP; HTP, Primary energy demand, blue water use


GC-7392 New New FWPA Australian

45 new EPD datasets from FWPA in Australia are now available.


57


Issue Cat-egory


EPD da-tasets

GC-7553 Bug Wood input for strip par-quet UA and BR

The transport of the sawn lumber is now modelled correctly. This leads to an increase of the wood input.

GWP decreased due to higher uptake of CO2 in the wood.


GC-7597 Bug Mass bal-ance of wood windows

Mass was corrected from 3.1 to 2.1 kg/m. Thermal energy production from the sawdust produced has been updated to the latest country specific standards.

Update of the mass in the product and update of the thermal energy consumed. For the following processes: - CN: Wooden frame - DE: Wooden casement (EN15804 A1-A3) - UA: Wooden frame all indicators except GWP re-duced between 18% to 40%, GWP reduced between -145% to -77% For DE: WINDOW (IV 68 PINES) (1.0m x 1.5m) +80% GWP For DE: Wooden window (1.00x1.50) -30% GWP

Professional database Extension da-tabase XIV: construction materials

GC-7423 New EUMEPS EPS boards EPD da-tasets

7 new/updated datasets added to Extension data-base XIV: construction ma-terials: EU-27: Expanded Polysty-rene (EPS) Foam Insulation (25 kg/m³) EU-27: Expanded Polysty-rene (EPS) Foam Insulation (15 kg/m³) EU-27: Expanded Polysty-rene (EPS) Foam Insulation (20 kg/m³) EU-27: Expanded Polysty-rene (EPS) Foam Insulation (with infra red absorbers) (20 kg/m³) EU-27: Expanded Polysty-rene (EPS) Foam Insulation (with infra red absorbers) (15 kg/m³) EU-27: Expanded Polysty-rene (EPS) Foam Insulation (30 kg/m³) EU-27: Expanded


58


Issue Cat-egory


Polystyrene (EPS) Foam Insulation (shape moulded) (25 kg/m³)

2.16 Inventories for US regional processes The datasets in the US extension database have been checked by thinkstep experts for their techno-logical validity and have passed.

27 datasets have been added to the Extension database XVII: full US. Noteworthy here are especially 17 specific waste incineration processes.

Table 2- 10: JIRA issues for US regional processes


Issue Cat-egory



US trucks updates

Emission factors for US trucks were updated.

EP and PM decrease. Extension da-tabase XVII: full US

GC-1119 New New da-tasets for white paints (US)

Two new datasets for white paints (solvent and water based are now available: US: Coating solvent-based (industry; white) (estima-tion) US: Coating water-based (industry; white) (estima-tion)

New datasets Extension database VIII: coating

GC-4361 New New hydro-gen peroxide datasets

A new dataset "US: Hydro-gen peroxide (100%; H2O2) (integrated prod., 70% H2 chemical synthesis, 40% raw solut.)" is now availa-ble.

New datasets Extension da-tabase XVII: full US

GC-5723 New New US iron ore mix

A new dataset for "US: Iron ore mix" is now available.


59


Issue Cat-egory


GC-6431 Editorial PIMA data-sets rena-ming

The following EPD datasets were renamed: RNA: Installation of Polyiso wall insulation 4.9 (A5) [PIMA] {6cea2d3b-ce23-4149-8f1c-7ec42982aa99} RNA: Installation of Polyiso wall insulation 19.5 (A5) [PIMA] {b3d301a7-bc74-4424-97a2-dfcfa00c1a3b} RNA: Installation of Polyiso wall insulation 14.6 (A5) [PIMA]{beb7db81-5ac6-452f-9ce2-6817b41d7c40} RNA: Installation of Polyiso wall insulation 9.8 (A5) [PIMA] {3aae81af-5fd2-457e-a6c1-0181e18d8d09}

Does not change the results Extension da-tabase XVII: full US 2019

GC-6528 Bug USLCI data-sets correc-tions

The inputs and outputs for the datasets "Fertilizer, stover, 2022" and "Pesticide, corn, 2022" were corrected.

Does not change the results Extension da-tabase XVII: full US Extension da-tabase XVIII: NREL USLCI integrated

GC-6781 New EPD dataset for fabricated steel rein-forcement from CRSI

The dataset "US: Fabri-cated steel reinforcement - CRSI (A1-A3)" is now avail-able.



R114 emis-sions in R143a pro-duction

R-114 emissions were de-leted where R-114 is used as an intermediate product, since the emission is forbid-den and will only occur in case of an accident (which is generally not accounted in LCA) US: Trifluoroethane (R143a) (estimation)

Does not change the results Extension da-tabase XVII: full US

60


Issue Cat-egory


GC-6991 New US material specific mu-nicipal waste incineration

17 new US material specific waste incineration are now available: US Polyvinyl chloride (PVC) US Polyethylene tereph-thalate (PET) US Polyurethane (PU) US Polyethylene (PE) US Polystyrene (PS) US Polypropylene (PP) US Polycarbonate (PC) US Polybutadiene (PB) US Polymethylmethacry-late (PMMA) US Polyamide (PA) 6.6 US Polyamide (PA) 6.6 GF US Polyamide (PA) 6 US Polyamide (PA) 6 GF US Acrylonitrile-butadiene-styrene (ABS) US Populated printed wir-ing board (after RoHS) US Populated printed wir-ing board (before RoHS)


GC-7177 New New ammo-nia datasets

A new dataset for ammonia production is now available. - Ammonia (NH3) synthesis with CO2 recovery, by-prod-uct carbon dioxide (without allocation) No allocation is applied for the two products ammonia and carbon dioxide. This means, the output inventory shows both ammonia and carbon dioxide as product flows. It enables the user to connect these flows to fur-ther use (e.g., a part of the amount of ammonia and carbon dioxide could be used to model an own urea


61


Issue Cat-egory


production plant where both materials are needed as feedstock).

62

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 their own cycle for upgrading their data, these processes cannot be up-dated by thinkstep in the annual upgrade without permission. thinkstep must keep these pro-cesses identical to those in the GaBi Databases 2018 Edition until the associations decide to update and make them available for our system. However, several new association datasets use the GaBi database to reach global customers.

New industry data added in GaBi Databases 2019 Edition:

From European Calcium Carbonate Association (CGA-Europe)

(https://www.ima-europe.eu/about-ima-europe/associations/cca-europe)

Coun-try Process name Process GUID Can be entered in the

search tool

RER Ground calcium carbonate slurry {c220ef06-50b6-4037-b6b2-7562d01ea9e3}

From European Kaolin and Plastic Clays Association (KPC)

(https://www.ima-europe.eu/about-ima-europe/associations/kpc-europe)


search tool

EU-27 Processed kaolinitic clay, granular or powder, moisture content 0 to 14%, expressed in dry mass

{17d07838-4b8f-42ea-83b5-77283a8807a6}

From European Bentonite Association (EuBA)

(https://www.ima-europe.eu/about-ima-europe/associations/euba)


search tool

RER Bentonite granular, sodium activated {2b78d6f1-2d78-4781-9701-3d34e00f2762}

RER Bentonite powder, sodium activated {a2eaa86a-57cd-4f78-9e5a-9bc44458f6b0}

From European Lime Association (EuLA)

(https://www.ima-europe.eu/about-ima-europe/associations/eula)


search tool

EU-27 Hydrated Lime {5191d1aa-78e8-42d5-ae18-211a3f5485f3}

EU-27 Quicklime {7c3d4590-c4dc-420b-89d2-7a5f717b1e29}

https://www.ima-europe.eu/about-ima-europe/associations/cca-europe

https://www.ima-europe.eu/about-ima-europe/associations/kpc-europe

https://www.ima-europe.eu/about-ima-europe/associations/euba

https://www.ima-europe.eu/about-ima-europe/associations/eula

63

From Eurofer (http://www.eurofer.org)


search tool EU-28 Stainless steel cold rolled coil (430 Stab.) {ac231075-6766-4034-bdeb-

e52048b370b4} EU-28 Stainless steel white hot rolled coil (430 Stab.) {1e605caf-68dc-45de-beea-

1b5d84601697}

From Nickel Institute (https://www.nickelinstitute.org)


search tool GLO Ferro Nickel (29% Ni) ILCD 2017 {1671ea28-ebe1-4ec0-9277-ab-

fbf2cbd5a8} GLO Nickel (Class 1, 99.95%) ILCD 2017 {04dc7156-8fda-4c67-923e-

e779abd20e49}

From International Aluminium Institute (IAI) (http://www.world-aluminium.org)

Country Process name Process GUID Can be entered in the search tool

RLA Alumina production 2015 {1a3874c2-af16-41f1-92b7-f52fc58e572c}

GLO Alumina production 2015 {16667cd1-1d8d-428f-b87a-70e823298079}

EU-28 Alumina production 2015 {c40a16bb-ed5f-42f2-9ed0-4d51b81bf95e}

OCE Alumina production 2015 {6bc432ce-b2c0-468a-84b8-c178708e2df9}

CN Alumina production 2015 {38a4d785-8b4d-46f6-8aca-3195dd194279}

RNA Alumina production 2015 {ec37caac-ac46-4ac1-891b-2b3251555883}

OCE Anode production 2015 {cb15e931-e626-49dd-9d4a-21c443250d79}

GLO Anode production 2015 {687876f0-9d73-4a28-ac66-716c446070ce}

RME Anode production 2015 {39799948-28f3-4a9a-94f3-665036c8a7a9}

CN Anode production 2015 {8d905fd3-f31f-4c85-ac97-066485690493}

EU-28 Anode/Paste production 2015 {f17750e8-f721-4201-94dc-679a9c1ae3af}

RNA Anode/Paste production 2015 {a590cf29-465a-4804-a041-7b8a679040d8}

CA Anode/Paste production 2015 {67038581-d285-48a6-9075-16b25950b189}

RLA Anode/Paste production 2015 {b7c31cf3-7400-4d61-9154-98533785d9a5}

GLO Bauxite mining 2015 {05ac9ad7-6f85-4972-92da-1c7c42e1fa79}

OCE Ingot casting 2015 {98cc9fdc-5a6d-4185-ace6-93150bc0b77a}

http://www.eurofer.org/

https://www.nickelinstitute.org/

http://www.world-aluminium.org/

64

Country Process name Process GUID Can be entered in the search tool

EU-28 Ingot casting 2015 {5053be34-7992-4394-b570-f1e60bea78bb}

RLA Ingot casting 2015 {a57df78e-1951-4399-b933-85f0b1334657}

CA Ingot casting 2015 {70113245-b5d4-4486-8e70-5c0d5868360f}

RME Ingot casting 2015 {b5e35536-4d5e-4510-8ec7-5eae241f4b6c}

GLO Ingot casting 2015 {2891db6d-a540-4c05-bf0d-5ca031015ca3}

CN Ingot casting 2015 {7e6fa231-84e2-4cc7-af1a-f7dce5dcf8c0}

RNA Ingot casting 2015 {cafc6dda-d0ab-4ed7-84b1-97cbce5bf981}

GLO Paste production 2015 {29f08e8b-04eb-4181-8696-fd0c192f9e7f}

RME Aluminium ingot mix IAI 2015 {1861bc3a-c181-4589-8968-88136b2e5e44}

CN Aluminium ingot mix IAI 2015 {5b008e1e-890e-4876-81a8-094d759d3044}

RU Aluminium ingot mix IAI 2015 {51723e62-6503-4d10-af5e-03d36da5008e}

CA Aluminium ingot mix IAI 2015 {dc21fd62-0b94-4da0-9dff-d81d353ceb56}

GLO Aluminium ingot mix IAI 2015 {241d1242-4d0f-4ded-9a96-5181615b0bfb}

RME Electricity from natural gas (IAI 2015) {609599a6-919d-48f8-bdbc-4c2c7a487bc5}

CN Electricity grid mix (IAI 2015) {54ada522-3e54-48da-8f5b-bff5f69ee275}

GLO Electrolysis (Prebake) IAI 2015 {9e363c0c-5c4f-4017-a79a-1cb1b2cc6035}

OCE Electrolysis (Prebake) IAI 2015 {3183ca2d-168c-443e-b917-5b8b4ba717d6}

RME Electrolysis (Prebake) IAI 2015 {950d41e9-544a-47c9-a9d4-b7352dd823c6}

CN Electrolysis (Prebake) IAI 2015 {c3ed6e88-f0da-4b20-9a5d-0398c7555b53}

RNA Electrolysis (Prebake/Søderberg) IAI 2015 {aa91f79e-8bcc-4137-b7a5-92391854e09d}

EU-28 Electrolysis (Prebake/Søderberg) IAI 2015 {54afbda9-91a4-4415-9dec-f9923dd55d05}

CA Electrolysis (Prebake/Søderberg) IAI 2015 {1bf00fca-35cf-4ccc-9e82-67f2956e45c7}

RLA Electrolysis (Prebake/Søderberg) IAI 2015 {f34ec162-d18e-4ec0-9380-d35a03154910}

GLO Electrolysis (Søderberg) IAI 2015 {03b658cd-30df-4e6c-8852-388ec6d014fd}

65

New data from Forest and Wood Products Australia (FWPA) (https://www.fwpa.com.au)


search tool AU Energy recovery from hardwood glulam, untreated (EN 15804 C3) {aa05e5be-60df-4f52-bb8b-

43f71bfc6c23} AU Energy recovery from hardwood glulam, untreated (EN 15804 D) {509bd8a4-8b61-4b86-9359-

5b40164a4475} AU Energy recovery from MDF, MR, E1, melamine coated, 16 mm (EN

15804 C3) {091c90cf-e28e-40f0-810e-f0e73984845e}

AU Energy recovery from MDF, MR, E1, melamine coated, 16 mm (EN 15804 D)

{284c408f-faef-4e8f-a434-d56e69c397f0}

AU Energy recovery from MDF, standard, E1, melamine coated, 16 mm (EN 15804 C3)

{e4083a13-e92b-4d5d-a8b9-5d25d0851013}

AU Energy recovery from MDF, standard, E1, melamine coated, 16 mm (EN 15804 D)

{c5238491-a552-46f9-a24c-43ef010eef18}

AU Energy recovery from plywood, formply, B-bond, 17 mm (formwork) (EN 15804 C3)

{946ed01f-c4ba-4007-8c73-4667f3bed919}

AU Energy recovery from plywood, formply, B-bond, 17 mm (formwork) (EN 15804 D)

{942497b6-740e-464d-a813-f0169e80a24a}

AU Energy recovery from softwood glulam, untreated (EN 15804 C3) {84f99b7f-e4c5-4474-b24e-85e99e598d3b}

AU Energy recovery from softwood glulam, untreated (EN 15804 D) {4c515ae6-85f2-4229-9ab8-63d69c5ecb62}

AU Hardwood Glulam, untreated (EN 15804 A1-A3) {02b968af-47ad-4233-8e69-fe58f2ce-abd6}

AU Landfill of hardwood glulam, untreated (NGA) (EN 15804 C4) {c88f7fb1-da78-488f-8836-b67b447c18c4}

AU Landfill of hardwood glulam, untreated (NGA) (EN 15804 D) {2ae25b37-6f7c-43f1-8ae1-adc5751a623f}

AU Landfill of hardwood glulam, untreated (typical) (EN 15804 C4) {5644bc32-0a81-41a3-952b-cbbb04c0221c}

AU Landfill of hardwood glulam, untreated (typical) (EN 15804 D) {a58017d3-0566-4570-b2db-ef76437812a2}

AU Landfill of MDF, moisture resistant (MR), E1, melamine coated, 16 mm (NGA) (EN 15804 C4)

{2d5da2d4-8bce-4598-809f-d12fdc205a83}

AU Landfill of MDF, moisture resistant (MR), E1, melamine coated, 16 mm (NGA) (EN 15804 D)

{58d0ffdf-03c5-4bf4-9e51-9645558ba29f}

AU Landfill of MDF, moisture resistant, E1, melamine coated, 16 mm (typ-ical) (EN 15804 C4)

{a8618c8f-0b27-4955-a83b-2ca53f71d181}

AU Landfill of MDF, moisture resistant, E1, melamine coated, 16 mm (typ-ical) (EN 15804 D)

{db165190-a621-440e-9d57-27765a213a92}

AU Landfill of MDF, standard, E1, melamine coated, 16 mm (NGA) (EN 15804 C4)

{7ba738c5-4487-4e54-85ff-008328037c19}

AU Landfill of MDF, standard, E1, melamine coated, 16 mm (NGA) (EN 15804 D)

{5be4c37a-240d-4f67-82aa-a90f02b398bf}

AU Landfill of MDF, standard, E1, melamine coated, 16 mm (typical) (EN 15804 C4)

{6498a301-5c43-4355-b00c-1e209eae9388}

AU Landfill of MDF, standard, E1, melamine coated, 16 mm (typical) (EN 15804 D)

{f285cc5a-fb9a-4cfc-a3db-193d1b9b06eb}

AU Landfill of plywood, formply, B-bond, 17 mm (formwork) (NGA) (EN 15804 C4)

{1dc0afe8-248e-4128-aabb-dc666ffbbf76}

https://www.fwpa.com.au/

66


search tool AU Landfill of plywood, formply, B-bond, 17 mm (formwork) (NGA) (EN

15804 D) {a3cb8b96-a0ce-47e4-842b-f39346a82a53}

AU Landfill of plywood, formply, B-bond, 17 mm (formwork) (typical) (EN 15804 C4)

{8e285743-7d67-4dca-93e3-2bb52d77ed69}

AU Landfill of plywood, formply, B-bond, 17 mm (formwork) (typical) (EN 15804 D)

{ebafa1b2-1411-4261-acd5-61cae3ecfd64}

AU Landfill of softwood glulam, untreated (NGA) (EN 15804 C4) {936d2e0a-1225-4bb6-b2b1-80c6be5030bf}

AU Landfill of softwood glulam, untreated (NGA) (EN 15804 D) {28f28257-93f0-48a3-8187-973cf34e1618}

AU Landfill of softwood glulam, untreated (typical) (EN 15804 C4) {92955e79-b0a0-43c9-883a-090252eeaadc}

AU Landfill of softwood glulam, untreated (typical) (EN 15804 D) {72502e9f-23cb-43d4-9a65-18e947e092ca}

AU MDF, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 A1-A3)

{ceeb1954-77e3-4781-adb7-b8d3ae65af90}

AU MDF, standard, E1, melamine coated, 16 mm (EN 15804 A1-A3) {db6ba74d-2f62-49c7-a132-9bc20c20177d}

AU Plywood, formply, B-bond, 17 mm (formwork) (EN 15804 A1-A3) {a876715b-e325-4874-acc9-a7cb491980fd}

AU Recycling of hardwood glulam, untreated (EN 15804 C3) {9d5a5f99-5ca6-484c-a36f-02c18d0fdbac}

AU Recycling of hardwood glulam, untreated (EN 15804 D) {0aaf89fa-8d2b-4ebd-896d-be01d63b7e7b}

AU Recycling of MDF, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 C3)

{93130b4c-c409-4f68-ae47-4f3df813bb16}

AU Recycling of MDF, moisture resistant (MR), E1, melamine coated, 16 mm (EN 15804 D)

{b7731150-fbe4-4eb3-a9b0-bf336329eae4}

AU Recycling of MDF, standard, E1, melamine coated, 16 mm (EN 15804 C)

{bf0ea8dc-53e2-480d-abaf-e74d51395020}

AU Recycling of MDF, standard, E1, melamine coated, 16 mm (EN 15804 D)

{8ee18457-b9ef-4740-a57a-d83e1639f4db}

AU Recycling of plywood, formply, B-bond, 17 mm (formwork) (EN 15804 C3)

{8279b06b-7dd6-4196-89bc-a0ad1f86f199}

AU Recycling of plywood, formply, B-bond, 17 mm (formwork) (EN 15804 D)

{32cfc41b-ec80-4074-a173-90f1495388d6}

AU Recycling of softwood glulam, untreated (EN 15804 C3) {327ff958-2aa0-4d03-8ec0-ceb64134da50}

AU Recycling of softwood glulam, untreated (EN 15804 D) {85dbf989-14c4-4bad-ba7e-cb6724ed26c2}

AU Softwood Glulam, untreated (EN 15804 A1-A3) {3651fb2c-a3cf-4144-8a6e-13618171417b}

67

4 General continuous improvements

4.1 Editorial JIRA Tracking Number

Issue Cate-gory



GC-6189 Editorial Position of pro-cess folder "Forestry and logging"

Process folder "Forestry and logging" was moved.


All

GC-6203 Editorial CAS number of butene flows

CAS number in butene flows was changed.


All

GC-6274 Editorial Merge double product flows

Double valuable substance flows for Aluminium foil and Dimethylamine valuable were merged, leaving only one flow in the database.


All

GC-6459 Editorial Naming of Gyp-sum plaster

Gypsum plaster datasets were renamed to Gypsum. Alpha semihydrate was cor-rected to alpha hemihydrate. For easier identification of da-tasets, additionally "from FGD gypsum" was added to the name.


All

GC-6562 Editorial Correct sum formula of Pen-tachlorophenol in flow

The flows Pentachlorophenol now has the correct sum for-mula in the documentation.


All

GC-6586 Editorial CAS code of Io-dine-131

Now the flow Iodine-131 {B03F3A10-43DB-4C2B-8612-34D5A66C63A1} has the correct CAS number 010043-66-0. Additionally, two synonym CAS codes were added to the flow.


All

GC-7066 Editorial Spelling of VDA material classi-fication„2 Lights alloys, cast and wrought alloys“.

VDA material classification now is correctly spelled „2 Light alloys, cast and wrought alloys“.


All

GC-7084 Editorial Naming of EPD LCIA flows

8 flows in the folder "Oth-ers\EPD (EN 15804 Indica-tors) were renamed to con-tain the name 'Proxy' to indi-cate that they should only be used when no standard emis-sion flow is available, i.e. when only finished EPD char-acterized results are availa-ble. Proxy for Abiotic depletion potential for fossil resources (ADPF) - EN15804 EPD re-sults


All

68


Issue Cate-gory



Proxy for Abiotic depletion potential for non fossil re-sources (ADPE) - EN15804 EPD results Proxy for Acidification poten-tial (AP) - EN15804 EPD re-sults Proxy for Eutrophication po-tential (EP) - EN15804 EPD results Proxy for Global warming po-tential (GWP) - EN15804 EPD results Proxy for Ozone depletion potential (ODP) - EN15804 EPD results Proxy for Photochemical Ozone Creation Potential (POCP) - EN15804 EPD re-sults

GC-7102 Editorial CAS numbers for red and white phospho-rus

CAS numbers for white and red phosphorus are now cor-rect. Please check if flow choice selection was made based on CAS numbers.


All

GC-7294 Editorial Harmonization of flow name "Lake water, to turbine"

Name changed to "Lake wa-ter to turbine".


All

GC-7309 Editorial Renaming of 'Petrol coke at refinery' da-tasets

All datasets 'Petrol coke at refinery' were renamed to 'Petroleum coke at refinery'.


All

4.2 LCIA Methods, Normalisation and Weighting factors

In this chapter, JIRA issues for LCIA and Normalization and Weighting are listed. JIRA Tracking Number

Issue Cate-gory




Documentation of water quanti-ties (WSI and AWARE)

The recommended geo-graphical use of each quan-tity was corrected


All

GC-6118 LCIA EPD quantity PERT double counts renewa-ble primary en-ergy in Ecoin-vent wood da-tasets

The energy content in the PERT quantity is deleted from the 2 flows: Wood, hard, standing 01/06/2017 {5c49aff9-3cc2-4502-b212-31fec0f807b1} Wood, soft, standing

All

69


Issue Cate-gory



01/06/2017 {0dc6375b-4a9e-4ad6-a12e-1b885c4b5a4e}

GC-6196 Bug Duplicate flows for butane

Duplicate flows for isobutane are confusing: Duplicate: Butane {f61e423f-89f0-431e-9a99-be39e98d21ff} Flow kept: iso-Butane {c2058222-e630-441e-ad42-be03dd6dd353} The duplicate flow is not de-livered anymore.

Only effects if 'wrong' flow was used in product system with high butane emission and running ReCiPe 2016. Otherwise no ef-fect.

All

GC-6201 LCIA Long-term GWP correc-tions

18 flows were corrected. Does not change the results

All

GC-6385 LCIA Flow Sulphur Oxides not characterized in ReCiPe 2016 v 1.1

The flow Sulphur oxides [In-organic emissions to air] now has a characterization factor for Acidification Potential in ReCiPe 2016 v1.1.

When looking at AP in Recipe 2016 V1.1 the impacts will in-crease depend-ing on the amount of Sul-phur oxides emitted.

All

GC-6599 LCIA Land Use flows with no charac-terization

Land use flows with regional reference GB were merged with UK flows. CR flows are now character-ized.

Land use for cof-fee will increase due to character-ization of CR land use.

All

GC-6776 LCIA Turbine water in water re-sources UBP 2013

The Ecoinvent flow for water, turbine use, unspecified natu-ral origin now has the correct characterisation factor in the UBP 2013 water resource quantity.

Overall low re-sult changes. Changes in all ecoinvent pro-cesses, since the water used in turbines to produce electric-ity uses a very big amount of water. The out-put flow had to be set to zero to close the bal-ance.

All

GC-6875 LCIA Characteriza-tion factors for some water Ecoinvent flows for WSI and AWARE

The input factors for the fol-lowing flows are now correct. Water, well, in ground {67c40aae-d403-464d-9649-c12695e43ad8} Water, cooling, unspecified natural origin {fc1c42ce-a759-49fa-b987-f1ec5e503db1} Water, lake {1acb026e-9de6-

No effect on thinkstep da-tasets as these are ecoinvent flows. Strong effect on ecoinvent. Cur-rently, there is a negative water

All

70


Issue Cate-gory



48fe-9e0d-be4d24125bbc} Water, river {8c75e7ab-8ab8-41e4-b394-c166ff5b050d}

consumption in a lot of processes as the input here is a factor 1000 too low but out-put values are correct. This is corrected. This will mean close to a 100% change for most processes.

GC-6892 Editorial Reference for odour potential impact category

Added complete reference to method.


All

GC-6909 LCIA Mass quantity in flow Radium (Ra226)

Mass deleted from radioac-tive emission.

Does not change the results.

All

GC-6977 LCIA POCP factor for waste heat in ReCiPe

The flow Waste heat {C15EA0F9-D5FB-4338-9469-7DA533 C28879} now has the characterization fac-tors for POCP removed.

Decrease in 98-100% of all ReC-iPe 2016 v1.1 POCP values - both human health and eco-systems quantity

All

GC-7100 LCIA Regionalized water flow for SA (Saudi Ara-bia)

New flows for Saudi Arabia were created. Additionally, the dummy processes for wa-ter regionalization are now updated and include this flow.


All

GC-7130 LCIA Characteriza-tion factor for toxicity in ReC-iPe 1.08 for Zinc-65

The flow Zinc-65 now does not have the toxicity charac-terization factors in Recipe 1.08 anymore. The ReCiPe 2016 and other methods (CML, PEF, Traci, USEtox) were not affected.

Electricity sce-narios in ecoin-vent are most af-fected. Reduc-tions on ReCiPe 1.08 toxicity re-sults of 2-98%.

All

GC-7209 LCIA ISO 14067 Global Warm-ing Potential quantities

Four new quantities based on ISO 14067 (based on IPCC AR5) are now available: avia-tion, biotic, fossil and land use

New quantities All

GC-7237 Bug 3 EPD flows for PERM

All three flows are deleted, as PERM should be entered in the Quantity and specific to a product flow.


All

GC-7364 Bug Correct land use characteri-zation factors

Important change on LANCA land use values. Factors for non-regionalized land use are now correct.

Results vary de-pending on the flow used.

All

GC-7537 LCIA Move quantity "ILCD/PEF rec-ommendation v1.09"

Moved environmental quan-tity "Impacts ILCD/PEF rec-ommendation v1.09" to folder "Earlier versions of methods"

Does not change the results.

All

71


Issue Cate-gory



GC-7545 LCIA Ecoinvent en-ergy flows ad-justment in EF 2.0

Characterization factors "Ura-nium, in ground", "Peat, in ground", "Oil, crude, in ground", "Coal, brown, in ground", "Gas, natural, in ground", "Coal, hard, unspec-ified, in ground" for the EF 2.0 quantity "Resource use, energy carriers" were up-dated.

This only applies to Ecoinvent da-tabase - GaBi datasets are not affected. The calorific value of some flows was in-creased by 30-50%: - hard coal - brown coal - peat This means the ADP fossil val-ues will go up for EF2.0 and other ADP fossil im-pacts to the same extent. Uranium energy flow remain con-stant Crude oil and natural gas change very little (2-3%).

All

GC-7590 LCIA Remove the CF in EF 2.0 Re-source use, en-ergy carriers for "Gas, mine, off-gas, process, coal mining"

Characterization factor in EF 2.0 for Resource use, energy carriers was removed for the Ecoinvent flow "Gas, mine, off-gas, process, coal mining" {9cd05a6f-ca4c-40c8-a88c-992f51cd1265}


All

4.3 Fixing and improvements of cross cutting aspects

In this chapter, JIRA issues for bugs and improvements of process datasets and a few other dataset types are listed.


Issue Cate-gory



GC-1081 Improvement Refractory ma-terials (infra-structure) in glass models

The consumption of furnace refractory materials is now considered in the glass pro-duction, based on the given information from BREF (Source:

Due to the change, minor changes occur. The common im-pact categories like GWP, AP,


72


Issue Cate-gory



http://eippcb.jrc.ec.eu-ropa.eu/refer-ence/BREF/GLS_Adopted_03_2012.pdf). The amount is about 5 to 10 kg refractory materials per tonne of glass.

EP and POCP increase by ap-prox. 1%.

GC-3609 GC-6922 GC-7036

Bug Price quantity update

Price information in the quan-tity "Price" were updated us-ing mainly information from Eurostat.

Changes vary between da-taset.

Several details see Annex III

GC-5719 Improvement Calculation of own consump-tion (system) and grid losses

The calculated own con-sumption (of the net) and the grid losses are now correct.

Significant changes for electricity mixes of countries with high own con-sumption (e.g., LU and LT.

All

GC-5779 Improvement Capacity fac-tor/effi-ciency/full load hours wind power (photo-voltaics)

The model for electricity from wind power is annually up-dated with the average full load hours of all existing wind power installations in a coun-try. From now an average over a longer period is used which reduces the fluctuation between two years.


All

GC-5929 Improvement Update of infor-mation in diesel flows

Heating value, density and carbon content are updated in the diesel flows using infor-mation from the JRC Well-to-wheel study.

Environmental impact will de-crease slightly wherever diesel is combusted due to better heating values.

All


CAS Code of SO3

CAS code is now correctly 7446-xx-x and not 7746-xx-x


All


Documentation for infrared thermoforming dataset

The documentation for the dataset GLO: Infrared Ther-moforming (LDPE, HDPE, PS, PVC, PMMA, PA-6) was improved.


Extension database X: machining processes

GC-6277 Improvement Update of 2 stroke motor emissions

The chain saw dataset used in several models has been updated with updated emis-sions factors for 2 stroke mo-tors The emissions are based on a study done for DUH (Deutsche Umwelt Hilfe) by TÜV. An average of the two stroke motors above and un-der 50cm3 has be done and applied to the existing two strokes motors in agreement with our Farm experts.

For models us-ing chainsaw (primarily renew-able materials): - higher AP and EP (up to 80%) due to higher NOx emissions - lower GWP due to lower CO2 emissions (up to -44%) - higher HTP (+14%) due to

Several

73


Issue Cate-gory



higher NMVOC (+230% in the basis process) and Higher NOx emissions - higher POCP (up to +50%) due to higher CO emissions and NMVOC

GC-6382 Improvement Integration of underground land use infor-mation

Underground mining land use information is now imple-mented in the database.

Land use will in-crease through-out the data-base.

All

GC-6464 Bug Credit correc-tion for Non-ionic surfactant (fatty acid deri-vate)

A credit for a catalyst in the background system which caused negative ADP values was corrected. GLO: Non-ionic surfactant (fatty acid derivate) {91d72fe2-709d-4bfe-a739-c520bbf4366b}

CML2016 ADP elements now no longer has a negative value.

Extension database XV: textile finishing

GC-6495 Improvement Dust emissions in gypsum min-ing

Gypsum mining (open pit) now also has dust emissions.

Impacts will in-crease when looking at the impact catego-ries for PM.


GC-6683 Improvement Update of re-sidual grid mixes

The residual grid mixes were updated using the latest available report.

Changes de-pend on the country. Switzer-land has the greatest changes in all categories. ODP for Malta and Croatia in-creased signifi-cantly also, since now nu-clear energy is included.

Extension database II: energy

GC-6877 Bug Beverage car-ton converting unit process

The unit in the parameters was changed from kg/m^2 to g/m^2. The grammage pa-rameter is now used for the calculation of the input mate-rials aluminium, LDPE and liquid packaging board.

Will change re-sults if parame-ter for gram-mage is changed.



Documentation for specific flows ('forbid-den flows')

For some flows, further ex-planation on the usage was added. Thinkstep does not recommend using certain flows to e.g. avoid double counting. For further infor-mation please refer to the


All

74


Issue Cate-gory



document "GaBi Databases and Modelling Principles".

75

References EIA, U.S. Energy Information Administration: Electricity Data – Generation and thermal out-put by energy source, total of all production types, release date January 2017, http://www.eia.gov/electric-ity/data.cfm#generation

EPA, U.S. Environmental Protection Agency, “eGRID2016 - Emissions and Generation Resource integrated database (eGrid)”, 2016 data, Washington, 2018, https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

Eurostat, Energy Database - Supply, transformation, consumption - electricity - annual data [nrg_105a], Luxembourg, 2018

IEA, International Energy Agency Data services: World Energy Balances, World Energy Statistics, Electricity Information (2018 edition), Paris, 2018

thinkstep, “GaBi Database & Modelling Principles”, 2019

thinkstep, “Introduction to Water Assessment in GaBi”, 2019

thinkstep, “The GaBi refinery model”, 2019

thinkstep, “The Agricultural LCA Model Documentation”, 2019

thinkstep, “Land Use Change Emissions in GaBi Documentation”, 2019

thinkstep, “Documentation of land use inventory in GaBi”, 2019

thinkstep, “Documentation for Passenger Vehicle Processes”, 2019

thinkstep, “Documentation for Duty Vehicle Processes”, 2019

http://www.eia.gov/electricity/data.cfm#generation

http://www.eia.gov/electricity/data.cfm#generation

https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

76

Annex I: “Version 2018” discontinued datasets – Explanations and Recommenda-tions

For various reasons, there are a few processes in the Databases 2019 Edition that are no longer appropriate. These have been moved into a

folder called “Version 2018.” 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 to not provide information that is not up-to-date, and, at the same time,

ii) thinkstep wants to enable users who have used the dataset to decide if it is still appropriate for their specific goal and scope.

The tables in Annex I and Annex II list all those processes along with the explanations and recommended alternatives where applicable.

77

Version 2018 processes Alternative process to be used instead

Coun-try Process name Type Source

Process GUID (can be entered in the search tool)

Country Process Name Source GUID

EU-28 Beverage carton convert-ing u-so ACE/ELCD {6118E7C9-98CE-46F9-

A3DF-95CC3F8F8B2E}

If relevant, please contact data on demand from thinkstep for alternative processes

EU-28 Liquid Packaging Board (LPB) production agg ACE/ELCD {7D580A76-D2A4-46FE-

A3A3-C6C8ED585382}

RER Continuous filament glass fibre (assembled rovings) agg APFE/ELCD {D1BEFC51-AF18-4725-

B356-3C79BE66B249}

RER Continuous filament glass fibre (direct rovings) agg APFE/ELCD {6756D1F6-8661-452F-

BADF-BD386A8B947A}

RER Continuous filament glass fibre (dry chopped strands) agg APFE/ELCD {B1661CC6-B2F5-46AE-

8CE0-F1D33AC7E1AF}

EU-28

Waste incineration of bio-degradable waste fraction in municipal solid waste (MSW)

p-agg ELCD/CEWEP {3C0A1214-F4B2-4254-8FCA-1D9E6EE9839F}

EU-28

Waste incineration of bio-degradable waste fraction in municipal solid waste (MSW)

agg ELCD/CEWEP {898618BC-3306-11DD-BD11-0800200C9A66}

EU-28 Waste incineration of ferro metals p-agg ELCD/CEWEP {870ED176-3E13-4BAA-

8488-BBC3C9579EC5}

EU-28 Waste incineration of ferro metals agg ELCD/CEWEP {89863FC7-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of glass/inert material agg ELCD/CEWEP {89863FC8-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of mu-nicipal solid waste (MSW) p-agg ELCD/CEWEP {4F035020-4599-424D-

8F48-627B35DD1A7F}

EU-28 Waste incineration of mu-nicipal solid waste (MSW) agg ELCD/CEWEP {898618BA-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of paper fraction in municipal solid waste (MSW)

agg ELCD/CEWEP {898618BB-3306-11DD-BD11-0800200C9A66}

EU-28 Waste incineration of plas-tics (Nylon 6 GF 30, Nylon 66 GF 30)

p-agg ELCD/CEWEP {400AF2CE-8EC3-45A7-B700-8FBC50B16346}

EU-28 Waste incineration of plas-tics (Nylon 6 GF 30, Nylon 66 GF 30)

agg ELCD/CEWEP {89863FC5-3306-11DD-BD11-0800200C9A66}

EU-28 Waste incineration of plas-tics (Nylon 6, Nylon 66, PAN)

p-agg ELCD/CEWEP {14D74618-44F7-40D7-8B35-4364E23C76BB}

EU-28 Waste incineration of plas-tics (Nylon 6, Nylon 66, PAN)


EU-28 Waste incineration of plas-tics (PE, PP, PS, PB) agg ELCD/CEWEP {89863FC2-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of plas-tics (PE, PP, PS, PB) p-agg ELCD/CEWEP {E01167AD-6CF8-47A7-

8DF9-E89BF35CB704}

78





EU-28 Waste incineration of plas-tics (PET, PMMA, PC) agg ELCD/CEWEP {89863FC3-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of plas-tics (PET, PMMA, PC) p-agg ELCD/CEWEP {9C0CF135-02AA-46A1-

AE22-DFA2683C9BD8}

EU-28 Waste incineration of plas-tics (rigid PVC) agg ELCD/CEWEP {89863FC6-3306-11DD-

BD11-0800200C9A66}

EU-28 Waste incineration of plas-tics (rigid PVC) p-agg ELCD/CEWEP {962FEE73-E692-4AE2-

812D-FFAC7EDCE7BA}

EU-28

Waste incineration of plas-tics (unspecified) fraction in municipal solid waste (MSW)

p-agg ELCD/CEWEP {46D6BD5B-16E1-459C-A018-340F33ED2F72}

EU-28

Waste incineration of plas-tics (unspecified) fraction in municipal solid waste (MSW)


EU-28 Waste incineration of tex-tile fraction in municipal solid waste (MSW)

p-agg ELCD/CEWEP {7E2BFB23-9F75-4976-AF0A-1DFEF7FB3EC3}

EU-28 Waste incineration of tex-tile fraction in municipal solid waste (MSW)


EU-28 Waste incineration of un-treated wood (10.7% H2O content)


EU-28 Waste incineration of un-treated wood (10.7% H2O content)

p-agg ELCD/CEWEP {C6F0B870-89BC-414F-A7B4-3398F56EAE25}

EU-28 Waste incineration of wood products (OSB, particle board)

p-agg ELCD/CEWEP {39F61D7A-9CEA-4E61-B292-50AD6EE05CCC}

EU-28 Waste incineration of wood products (OSB, particle board)

agg ELCD/CEWEP {89863FCA-3306-11DD-BD11-0800200C9A66}

RER Aluminium extrusion profile agg ELCD/EAA {09215EB0-5FC9-11DD-AD8B-0800200C9A66}

EU28+EFTA

Aluminium extrusion profile (2015) [p-agg] Please note: Aluminium ingot needs to be added

European Aluminium {DD3BC399-A4E9-4150-8879-8421A04F8579}

RER Aluminium sheet agg ELCD/EAA {09215EB1-5FC9-11DD-AD8B-0800200C9A66}

EU28+EFTA+Turkey

Aluminium sheet (2015) [p-agg] Please note: Aluminium ingot needs to be added

European Aluminium {1BFA0B24-DB14-4785-BF69-35966F2E807E}

RER Hydrogen chloride p-agg ELCD/Plas-ticsEurope

{C71F9B0F-BE89-4BE7-A00C-086230324492} If relevant, please con-

79





RER Polybutadiene granulate (PB) p-agg ELCD/Plas-

ticsEurope {4575C944-457D-4825-82FF-17528D28B3AC}

tact data on demand from thinkstep for alternative processes

RER Polyethylene low density granulate (PE-LD) p-agg ELCD/Plas-

ticsEurope {46C09193-AB51-43AE-957F-E6383B67E73D}

RER Polyethylene terephthalate granulate (PET, amorph) p-agg ELCD/Plas-

ticsEurope {028B2915-00A5-4967-B356-34BCE19960C3}

RER Polypropylene granulate (PP) p-agg ELCD/Plas-

ticsEurope {0DC3D65B-7FF8-4C92-A694-748FB28070A9}

RER EPS - expanded polysty-rene (grey, 15kg/m3, cra-dle-to-gate, A1-A5)

agg EUMEPS {E269749D-E259-4719-87E8-DA4882869D6E} EU-27

Expanded Polystyrene (EPS) Foam Insulation (with infra red absorbers) - EUMEPS (A1-A3)

EUMEPS {be07b79f-794b-4d02-867d-4def6e97e73f}

RER EPS - expanded polysty-rene (grey, 20kg/m3, cra-dle-to-gate, A1-A5)

agg EUMEPS {E96A6123-EAC7-4893-87BA-1A3FD959397A} EU-27

Expanded Polystyrene (EPS) Foam Insulation (with infra red absorbers) - EUMEPS (A1-A3)

EUMEPS {98c5cd32-d0cb-4ca1-997f-b42e216e968f}

RER EPS - expanded polysty-rene (white, 15kg/m3 cra-dle-to-gate, A1-A5)

agg EUMEPS {E4982F06-2938-4E4F-B206-4ADCC1953AEA} EU-27

Expanded Polystyrene (EPS) Foam Insulation - EUMEPS (A1-A3)

EUMEPS {15edb9dc-1ac4-4acc-a051-fbc458d11a4e}

RER EPS - expanded polysty-rene (white, 20kg/m3, cra-dle-to-gate, A1-A5)

agg EUMEPS {9F091455-46C3-4A6F-9E76-CD92CB7D865A} EU-27


EUMEPS {c51b9ced-6675-4b31-8d51-a72a2c6bbe24}


agg EUMEPS {35CF06A0-50CA-411F-9A41-14E2E433A3BB} EU-27


EUMEPS {0892d2b6-1459-40c6-a8f3-8533b73dca3f}


agg EUMEPS {F1AF9EB7-2F07-4AB1-834B-A414CD0CF863} EU-27


EUMEPS {0c46eb9d-2dd8-4d5b-b536-a39899f65b3c}

RER

EPS - expanded polysty-rene, shape moulded (white, 25kg/m3, cradle-to-gate, A1-A5)

agg EUMEPS {F5AEE319-DB4D-41BD-B3EC-466691767B67} EU-27

Expanded Polystyrene (EPS) Foam Insulation (shape moulded) - EUMEPS (A1-A3)

EUMEPS {7f535a3b-9d3d-4e94-909f-09a4dd8b1087}

EU-28 Calcium carbonate > 63 microns agg IMA-Europe/ELCD {6006D87E-CCEE-42B1-

B203-F67C7C0BAD97} If relevant, please contact data on demand from thinkstep for alternative processes

EU-28 Kaolin coarse filler agg IMA-Europe/ELCD {4A1EBE7C-6835-4A22-8B2E-3201F1CD32E8} EU-27 Kaolin calcined, granular or

powder {2857FC5B-CDE6-486C-8DC0-759A727AD7BE}

EU-28 Very fine milled silica sand d50 = 20 micrometer agg IMA-Europe/ELCD {B81D1E8F-5A19-483A-

8077-4799839ECF26} If relevant, please contact data on demand from thinkstep for alternative processes

US Polyiso Manufacturing (Roof - R15.3) agg PIMA {0125DD2F-3F16-4FD8-

9D64-D3EC06E34FA0}

No direct replacement available, however different datasets for Polyiso manufacturing from PIMA are available.

US Polyiso Manufacturing (Roof - R15.3) agg PIMA {2D9A4FC5-5942-45EC-

B060-9016875DCA8A}

US Polyiso Manufacturing (Wall - R13.0) agg PIMA {0F7C2789-13DC-4D51-

BBF9-6CDA164AA4EC}

US Polyiso Manufacturing (Wall - R14.6) agg PIMA {671D6D99-3527-4741-

A144-86A595D895B4}

US Polyiso Manufacturing (Wall - R19.5) agg PIMA {71F3DD37-C9AB-409E-

A66C-395E243F4E6F}

80





US Polyiso Manufacturing (Wall - R4.9) agg PIMA {5669F12B-9CB8-4DD6-

8734-17F09E30A671}

US Polyiso Manufacturing (Wall - R6.5) agg PIMA {7B139F40-B1F7-4813-

A68C-A1EA1C377302}

US Polyiso Manufacturing (Wall - R9.8) agg PIMA {BD7E29A8-E51C-464E-

9BA3-04F433F30D82}

EU-28 Bath- and shower tub acrylic (1kg) (EN15804 C3) agg ts {9757AC2E-D613-4DDC-

9186-2F91D3E0FCC2} EU-28 Bath- and shower tub acrylic (1kg) (EN15804 C4) ts {1bb51e8e-680c-413a-

a9b9-75ed0dfce81f}

DE Construction waste treat-ment plant p-agg ts {9CB7100E-F2D1-480E-

9A3F-F06B9B705C8E} EU-28 Construction waste treatment plant (C3) ts {78d97b2f-6abd-466b-

adff-745f907f8630}

EE Electricity from hard coal agg ts {9F9558C3-DB76-4D14-A64A-5529EF18F86A} no new dataset because no electricity from hard produced anymore in Estonia

HU Electricity from hard coal agg ts {59F3315D-ADFE-4416-8CFB-ABE877592477} no new dataset because no electricity from hard produced anymore in Hungary

ID Electricity from lignite agg ts {03984936-94C0-4DF1-B3F0-17D496950BE4} no new dataset because no electricity from hard produced anymore in Indonesia

LT Electricity from peat agg ts {2C5D8AF5-AB76-4552-8722-FEC7D7F46D5F} no new dataset because no electricity from hard produced anymore in Lithuania

MY Electricity from waste agg ts {8EC09B73-C31D-4278-9255-BA1F14C83D24} no new dataset because no electricity from hard produced anymore in Malaysia

BR Electricity grid mix (2020) agg ts {2776CB6D-E7C9-4904-A1BA-8FBEC3F61DF1}

Please use one of the following 2025 scenarios: “no improvements in sustainability policy”, “little improvements in sustainability policy”, “significant improvements in sustainability pol-icy”

CN Electricity grid mix (2020) agg ts {C69A4279-8BED-47F9-965D-A0312A044023}


DE Electricity grid mix (2020) agg ts {FC025BD0-9585-4128-A7F3-62070AD171FB}


ES Electricity grid mix (2020) agg ts {D432259C-8223-4D54-A784-127B69008460}


FR Electricity grid mix (2020) agg ts {1F2E905A-F559-4CDD-9F28-18B8CACAE7DA}


GB Electricity grid mix (2020) agg ts {F73CF9F0-55E5-4296-8FDA-68FFBA24DB02}


IN Electricity grid mix (2020) agg ts {D06DA946-C903-4CE6-9186-FFD4EA8F7F44}


IT Electricity grid mix (2020) agg ts {E646CDFF-002B-4823-B4D7-C5CCD9E6FAAF}


JP Electricity grid mix (2020) agg ts {7A09E977-F033-4EE7-A256-F099054706ED}


81





US Electricity grid mix (2020) agg ts {50B43832-A72B-4EDD-AC37-E80085FB8158}


EU-28 Electricity grid mix (aver-age power plants) (2020) agg ts {69158719-55CD-4941-

8A14-EB5947BB942B}


EU-28 Electricity grid mix (aver-age power plants) (2020) agg ts {9C13B341-3370-4B7F-

9F10-0F6AB8C19524}


DE Plastic injection moulding part (unspecific) u-so ts {B230EFD9-A154-4D9C-

B1A9-EF71BBE5D120} GLO Plastic injection moulding (pa-rameterized) ts {aaf7c3a1-6ecd-459e-

a493-3f376507e29b}

DE Polyamide 6.6 (PA 6.6) GF injection moulded part (0,02 - 0,2kg)

u-so ts {370EBEA7-D518-4ECB-931E-0CFA892F2E05} GLO Plastic injection moulding (pa-

rameterized) ts {aaf7c3a1-6ecd-459e-a493-3f376507e29b}

DE Polypropylene (PP) injec-tion moulded part u-so ts {947C903B-C652-4EEB-

B5A8-7F3A544B521A} GLO Plastic injection moulding (pa-rameterized) ts {aaf7c3a1-6ecd-459e-

a493-3f376507e29b}

DE

Polypropylene / Ethylene Propylene Diene Elastomer Granulate (PP/EPDM, TPE-O) injection moulded part

u-so ts {E94D4D06-2326-4100-9A8D-ED98FD3D2303} GLO Plastic injection moulding (pa-

rameterized) ts {aaf7c3a1-6ecd-459e-a493-3f376507e29b}

GLO Steel hot dip galvanized (ILCD) agg worldsteel/ELCD {339B2536-C881-409D-

AC71-49AB0D228FE3} GLO Steel hot dip galvanized worldsteel {797396DA-60D2-4C76-BF8B-E42803673CBA}

GLO Steel hot dip galvanized, including recycling agg worldsteel/ELCD {7DCB51EF-2D85-481C-

B943-3B148D9F6500} If relevant, please contact data on demand from thinkstep for alternative processes

GLO Steel hot rolled coil (ILCD) agg worldsteel/ELCD {E16174FE-6542-4572-90BC-8980616EBE53} GLO Steel hot rolled coil worldsteel {A62F24D0-5474-4C12-

A435-281DE7FE9A29}

GLO Steel hot rolled coil, includ-ing recycling agg worldsteel/ELCD {B0B413A1-2A7D-4CB5-

A108-BFD7B37502E4} If relevant, please contact data on demand from thinkstep for alternative processes

GLO Steel sections (ILCD) agg worldsteel/ELCD {09D61948-238A-40E7-8E1F-AFDC0C98F902} GLO Steel sections worldsteel {289BC59E-2080-4517-

B519-6A03ACA32DCF}

GLO Steel sections, including recycling agg worldsteel/ELCD {9C0C2F04-FD6A-4D3C-

950C-F9DEAD3639FE} If relevant, please contact data on demand from thinkstep for alternative processes

82

Annex II: EPDs with expired validity

Country Process name Type Source Process GUID (Can be entered in the search tool)

DE Chipboard (average) agg ts-EPD {0D98E99D-9FF4-46B1-ADF0-B638F97E114A}

DE Chipboard (average) agg ts-EPD {5BE2A1D7-B4E8-4309-91C3-F88AB5C7AA1C}

DE Chipboard Eurospan - Egger agg ts-EPD {0F9705F0-12D3-4341-AECB-18AD55FB6EA8}

DE Chipboard Eurospan - Egger agg ts-EPD {30DB63BF-23F8-4332-BD3D-31D11A70452B}

DE Concrete admixtures – Set accelerators - Deutsche Bauchemie e.V. (DBC) (A1-A3) p-agg ts-EPD {168C2BFE-388D-4A77-B2BB-A81B5E0BAA49}

DE Facing bricks/clinker bricks/quater bricks (BV Ziegel) (A1-A3) agg ts-EPD {3D14509A-D8C6-4996-9366-47BFB22E7D8E}

RNA Leak barrier, asphalt shingle roofing system component - ARMA (A1-A3) agg ts-EPD {8104830F-9B8C-4779-920D-9B50D9A58CB5}

DE Lime sand brick (Kalksandsteinverband e.V.) (EN15804 A1-A3) agg ts-EPD {928D5917-C780-4646-9E79-21C6702DC7CD}

DE Mineral panel, 1 m2, WETEC, (A1-A3) agg ts-EPD {27C95712-EB0D-464A-9577-61B14C45025C}

DE Normal masonry mortar - IWM (A1-A3) p-agg ts-EPD {2437950A-2946-464D-A4E5-65E3C7A92675}

RNA Painting of aluminum extrusion, AEC p-agg ts-EPD {0B0415F1-57EC-48EB-A245-852EDA74C403}

DE Production (A1-A3) Lucobit 1210A (ECB) agg ts-EPD {5683C209-E00E-4CBE-A92D-CDEB3CF1519B}

DE Production (A1-A3) Lucobit 1235 (ECB) agg ts-EPD {779527DE-9E1F-48D4-B226-26A93923312B}

US Reinforced ethylene propylene diene monomer roofing membrane [45mils] (A1-A3) - Car-lisle agg ts-EPD {43E83D5F-E4AA-483F-99AB-F29298344A61}

EU-28 Special mortar (Bulwark - joint mortar) - IWM (A1-A3) agg ts-EPD {963BEF67-AEBB-4DAC-B0F8-BB28F3A26D99}

DE Stone wool - Rockwool agg ts-EPD {4C7F15D9-F7E0-4C11-84F5-29985B11D958}

DE Stone wool - Rockwool agg ts-EPD {4C7F15D9-F7E0-4C11-84F5-29985B11D958}

DE Technical textile VALMEX® FR 1000 - Mehler Texnologies (C2) agg ts-EPD {D5BB65F0-1F63-469D-9653-DE3B59FE0028}

DE Texlon System 100% Recycling - Vector Foiltec (C3) p-agg ts-EPD {9510FB94-60CA-4878-BF3A-2B3EFDCD039C}

EU-28 Waste incineration of glass/inert material p-agg ELCD/CEWEP {60815257-BDAA-495A-A8DA-163C5ED2439D}

EU-28 Waste incineration of paper fraction in municipal solid waste (MSW) p-agg ELCD/CEWEP {77B31DCD-5ACD-47BF-8B6E-D9EADFD8B136} AU Energy recovery from hardwood timber, green, dressed, untreated (EN 15804 C3) agg FWPA {7DACE985-E535-45F0-B58C-08A2A6C0E6DD}

AU Energy recovery from hardwood timber, green, dressed, untreated (EN 15804 D) agg FWPA {502CD239-AD29-4F6C-9E26-469011DB352B}

AU Energy recovery from plywood, interior, C-bond, 9 mm (joinery) (EN 15804 C3) agg FWPA {47002EB4-4B8C-43FD-AF9C-F9F58B274FD1}

AU Energy recovery from plywood, interior, C-bond, 9 mm (joinery) (EN 15804 D) agg FWPA {8BD7C3B4-D6BE-4262-B9C8-905EB34BDCBD}

83

Country Process name Type Source Process GUID (Can be entered in the search tool)

AU Hardwood timber, green, dressed, untreated (EN 15804 A1-A3) agg FWPA {33053243-EFA3-4B4E-9252-71C57EF73B54}

AU Landfill of hardwood timber, green, dressed, untreated (NGA) (EN 15804 C4) agg FWPA {991CAF53-0FFB-4588-9EF6-02078B1BA34E}

AU Landfill of hardwood timber, green, dressed, untreated (NGA) (EN 15804 D) agg FWPA {28F025E1-51C0-4B11-98D8-447CF5DF3753}

AU Landfill of hardwood timber, green, dressed, untreated (typical) (EN 15804 C4) agg FWPA {3A1E84A2-5DE0-491F-8E8B-E079D1DD8FF5}

AU Landfill of hardwood timber, green, dressed, untreated (typical) (EN 15804 D) agg FWPA {625DCEE8-5980-4165-AB93-1259C548AE77}

AU Landfill of plywood, interior, C-bond, 9 mm (joinery) (NGA) (EN 15804 C4) agg FWPA {2A724BD1-4D5E-432D-9747-CEB38B47992B}

AU Landfill of plywood, interior, C-bond, 9 mm (joinery) (NGA) (EN 15804 D) agg FWPA {89A6B4D6-02A4-490A-BBB8-D0463C046C0A}

AU Landfill of plywood, interior, C-bond, 9 mm (joinery) (typical) (EN 15804 C4) agg FWPA {82CBE69C-8C70-40FE-B2B2-957CB618A9F8}

AU Landfill of plywood, interior, C-bond, 9 mm (joinery) (typical) (EN 15804 D) agg FWPA {0142D332-BACC-4302-82A9-412ECCA083B4}

AU Plywood, interior, C-bond, 9 mm (joinery) (EN 15804 A1-A3) agg FWPA {DCB3C0A4-6E6E-4B60-B152-4990D4FC7CB8}

AU Recycling of hardwood timber, green, dressed, untreated (EN 15804 C3) agg FWPA {4DE0341B-FC93-4612-B970-402E2D0859B8}

AU Recycling of hardwood timber, green, dressed, untreated (EN 15804 D) agg FWPA {A23BF3FA-3CE3-43BB-B47E-B7528D38DA33}

AU Recycling of plywood, interior, C-bond, 9 mm (joinery) (EN 15804 C3) agg FWPA {9B65592F-2CA1-4A6F-9E46-6E778FFDE6E1}

AU Recycling of plywood, interior, C-bond, 9 mm (joinery) (EN 15804 D) agg FWPA {8D02A7B7-9602-47B6-AAF6-12929049B78B}

84

Annex III: Price quantity changes

Flow Price 2018 (SP36) in

€/kg Price 2019 (SP37)

in €/kg

(**) Aluminium profile (blank) [Metals] 67.00 2.34

(**) Bovine blood, unprocessed [Materials from renewable raw materials] 9.66 0.19

(*) Cerium [Metals] 23.04 5.16

(*) Cerium oxide [Inorganic intermediate products] 23.04 1.92

(*) Chlorine [Inorganic intermediate products] 0.12 0.28

(*) Copper (98%; blister copper) [Metals] 1.74 5.17

(*) Copper (99.999%; electrolyte copper) [Metals] 5.89 5.21

(**) Copper (speiss) [Metals] 0.52 5.20

(*) Copper [Metals] 5.89 5.20

(*) Copper cathode (>99.99 Cu) [Metals] 2.00 5.20

(**) Copper matte [Metals] 0.26 3.18

(*) Copper sheet [Metals] 4.35 5.17

(*) Cotton fiber (ginned) [Materials from renewable raw materials] 1.20 1.67

(**) Cotton oil [Renewable primary products] 942.33 2.28

(*) Cotton, raw fibers (includes grains and fibers) [Renewable primary products] 1.00 1.29

(*) Cream (38%) [Materials from renewable raw materials] 1.31 1.66

(*) Cream [Materials from renewable raw materials] 2.98 1.66

(*) Crude palm kernel oil [Materials from renewable raw materials] 1.02 0.95

(*) Crude Palm Oil [Materials from renewable raw materials] 0.78 0.70

(**) Dioctyltin oxide [Organic intermediate products] 72000.00 4.00

(*) Dysprosium [Metals] 144.00 234.80

(*) Dysprosium oxide [Inorganic intermediate products] 144.00 161.26

(**) Europium [Metals] 1188.00 252.00

(**) Europium oxide [Inorganic intermediate products] 1188.00 49.45

(*) Flax, Seeds (15% H2O) [Materials from renewable raw materials] 0.15 0.46

(*) Gadolinium [Metals] 156.00 38.70

(*) Gadolinium oxide [Inorganic intermediate products] 156.00 18.06

(*) GBG Expeller Cake, 12% Fat [Materials from renewable raw materials] 2.00 2.33

(*) Hydrochloric acid (100%) [Inorganic intermediate products] 0.05 0.07




(*) Hydrogen [Inorganic intermediate products] 2.30 3.41

(*) Hydrogen [Other fuels] 2.30 3.41

(*) Lanthanum [Metals] 23.52 5.14

(*) Lanthanum oxide [Inorganic intermediate products] 23.52 1.97

(**) Linseed oil [Materials from renewable raw materials] 942.33 1.40

(**) Linseed press cake [Materials from renewable raw materials] 164.28 0.24

(*) Manganese oxide (highly purified) [Inorganic intermediate products] 1.17 0.78

85

Flow Price 2018 (SP36) in

€/kg Price 2019 (SP37)

in €/kg

(*) Methanol [Organic intermediate products] 0.10 0.33

(*) Milk (at operation site) [Materials from renewable raw materials] 0.80 0.55

(*) Milk (at operation site, before pretreatment) [Materials from renewable raw materials] 0.80 0.55

(**) Milk (pasteurized) [Materials from renewable raw materials] 2.98 0.55

(**) Milk Protein Concentrat [Renewable primary products] 3.82 1.48

(*) Neodymium [Metals] 35.00 58.90

(*) Neodymium oxide [Inorganic intermediate products] 34.20 45.87

(*) Nitrogen liquid [Inorganic intermediate products] 0.04 0.09

(*) Oil palm, palm oil [Materials from renewable raw materials] 0.55 0.70

(*) Pasteurized cream (42%) [Materials from renewable raw materials] 2.98 1.66

(*) Phosphoric acid [Inorganic intermediate products] 1.10 0.66

(*) Praseodymium [Metals] 44.16 108.80

(*) Praseodymium oxide [Inorganic intermediate products] 44.16 59.35

(**) Refined palm oil [Materials from renewable raw materials] 880.00 0.75

(**) Samarium [Metals] 432.00 13.27

(**) Samarium oxide [Inorganic intermediate products] 432.00 1.92

(*) Skim milk [Renewable primary products] 0.80 0.55

(**) Sodium [Inorganic intermediate products] 662.00 2.80

(*) Sodium chloride (rock salt) [Inorganic intermediate products] 0.07 0.06

(*) Sodium sulphate [Inorganic intermediate products] 0.18 0.34

(*) Sulphur dioxide [Inorganic intermediate products] 0.05 0.23

(**) Sulphur hexafluoride [Inorganic intermediate products] 20.00 2.91

(*) Sunflower Meal (25-30% CP) [Materials from renewable raw materials] 0.18 0.17

(*) Sunflower Oil, Crude [Materials from renewable raw materials] 0.06 0.42

(*) Wheat Germ (25-30% CP) [Materials from renewable raw materials] 0.04 0.34

(*) Wheat grains dried (14% H2O content) [Materials from renewable raw materials] 0.11 0.18

(*) Wheat grains dried (14% H2O content) [Renewable primary products] 0.11 0.18

(*) Winter wheat straw (15% H2O) [Renewable primary products] 0.01 0.08

(*) Winter wheat, Straw (10% H2O) [Renewable primary products] 0.01 0.08

(*) Yttrium [Metals] 86.92 31.57

(**) Yttriumoxide [Inorganic intermediate products] 86.92 2.82

(*) value updated

(**) value updated and unit conversion mistake corrected

Table 2- 11: Datasets most affected by price changes

GUID Dataset name Change

{315A2324-2D6D-47A7-9BAF-BC70D6F195F7}

TH: De-oiled rice bran (economic allocation) ts 30% increase in GWP 150% increase in AP 250% increase in EP

86


{674CACF9-E755-4025-9493-0F9789FC7ECF}

US: Cheese curd (from general cheese making pro-cess) (economic allocation) ts <p-agg>

2% increase in GWP 10% increase in AP 55% increase in EP

{852BE11A-219B-44BB-970B-539B2A35DB98}

US: Hydrochloric acid by product chlorobenzene ts 40% increase in GWP 22% increase in AP 50% increase in EP

{860CAB7E-5422-4D1E-991A-98E814D08FC7}

US: Cheese curd (from general cheese making pro-cess; 50% whey as waste) (economic allocation) ts <p-agg>

1% decrease in GWP 10% decrease in AP 85% increase in EP

{8A0B9EF1-F01E-48FA-96DC-8472DBF77291}

DE: Leather (varnished; 1 sqm/0.95 kg) ts 30% decrease in GWP 40% decrease in AP 40% decrease in EP

{9B41D7FB-38D4-4306-9264-E0E20CCCA180}

DE: Wheat germ (wheat mill) (economic allocation) ts 34% increase in GWP 660% increase in AP 640% increase in EP

{9C42124F-6C96-4099-B74E-9EFA4EACF43F}

US: Whey (from general cheese making process) (eco-nomic allocation) ts <p-agg>

No change in GWP 10% decrease in AP 55% increase in EP

{A523977E-FE89-4208-8924-736CE136258C} DE: Monoammonium phosphate (MAP) ts


{A6A6524D-9EBE-4899-B369-856FF1CBB200}

DE: Leather seat cover (10 sqm/9.5 kg) ts 32% decrease in GWP 35% decrease in AP 35% decrease in EP

{AD6525FD-51E6-4EF4-9CF4-C19C0C7C1C56}

FR: Sunflower oil (economic allocation) ts 20% increase in GWP 230% increase in AP 230% increase in EP

{B2C21CFD-6FC2-426C-9123-54100F10BDB4}

US: Cottonseed, refined (economic allocation) ts 43% decrease in GWP 40% decrease in AP 40% decrease in EP

{B80355FF-D10C-42BB-A0AF-49DEA028C527}

DE: Hydrochloric acid mix (100%) ts 25% increase in GWP 25% increase in AP 20% increase in EP

{C0488EAD-FAB3-417D-A1D0-DA30B10043D8}

DE: Pasteurized skim milk (mechanical separation) (economic allocation) ts

55% decrease in GWP 45% decrease in AP 45% decrease in EP

{C39D410E-37A9-41FA-A844-11B961E91F3B}

FR: Sunflower meal (economic allocation) ts 48% decrease in GWP 55% decrease in AP 55% decrease in EP

{C684FF99-5662-4CD1-B28D-D6B3053C3CB6}

US: Triple superphosphate (TSP) ts 70% increase in GWP 55% increase in AP 120% increase in EP

{D42F6D3E-C401-4B30-BA71-14BF043D9DDB}

US: Pasteurized skim milk (mechanical separation) (economic allocation) ts

56% decrease in GWP 45% decrease in AP 45% decrease in EP

{D7E35F18-407F-45F2-843D-76D7852167C9} US: Monoammonium phosphate (MAP) ts


87


{E83AF145-0214-4919-A4A1-8B55DBACCFE5}

NL: Triple superphosphate (TSP) ts 70% increase in GWP 80% increase in AP 60% increase in EP

{EF7E62AF-97B0-49A3-9DA5-3BF63721D97E}

GLO: Antifermentative agent ts 51% increase in GWP 20% increase in AP 50% increase in EP

{F248CD05-A7F5-4954-97E0-B581355AF9CB}

US: Phosphoric acid (54% P2O5, agrarian) ts 80% increase in GWP 60% increase in AP 140% increase in EP

88

Annex IV: Biogenic carbon content quantity changes

Flow Remark Previous value biogenic carbon

content per unit of flow

New value biogenic carbon content per

unit of flow Unit of flow

American Tulipwood lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Apple Juice Concentrate (packaged) [Materi-als from renewable raw materials] Correction 0.075 0.279

kg

Apple Juice Concentrate [Materials from re-newable raw materials] Correction 0.075 0.279

kg

Ash lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Aspen lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Bamboo flooring (kg) [Materials from renewa-ble raw materials] New entry - 0.505

kg

Basswood lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Beech lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Birch lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Carrots (87% H2O content) [Renewable pri-mary products] Correction 0.387 0.057827586

kg

Cherry lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Corn, grains (25% H2O content) [Renewable primary products] Correction 0.406 0.3375

kg

Cotton fiber (ginned) [Materials from renewa-ble raw materials] New entry - 0.5

kg

Cottonwood lumber [Materials from renewa-ble raw materials] New entry - 0.4301

kg

Elm lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Glycerine (99.5%) [Organic intermediate products] New entry - 0.39

kg

Hackberry lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Hard Maple lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Hickory lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Installed multilayer parquet [Materials from re-newable raw materials] New entry - 0.409

kg

Installed wood block flooring [Materials from renewable raw materials] New entry - 0.434

kg

Limonene [Materials from renewable raw ma-terials] Correction 0.9 0.0485

kg

Oil palm, palm oil [Materials from renewable raw materials] New entry - 0.77

kg

Orange juice [Materials from renewable raw materials] Correction 0.9 0.0485

kg

Palm oil methyl ester (PME) [Organic interme-diate products] Correction 0.77 0.74

kg

89

Pecan lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Phosphogypsum panel [Minerals] New entry - 0.00066 kg

Rapeseed methyl ester (RME) [Biomass fuels] Correction 0.773 0.74

kg

Raw juice [Materials from renewable raw ma-terials] Correction 0.21 0.0588

kg

Red Oak lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Sap Gum lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Shea kernel (moisture content 1%, H2O con-tent 1%) [Materials from renewable raw mate-rials] New entry - 0.008

kg

Soft Maple lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Tall oil (raw product) [Organic intermediate products] Correction 0.43 0.79

kg

Textiles (Flax fabric) [Textile] New entry - 0.423 kg

Timber cedar (12% moisture; 10.7% H2O content) (m3) [Materials from renewable raw materials] Correction 277 257

m3

Walnut lumber [Materials from renewable raw materials] New entry - 0.4301

kg

White Oak lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Willow lumber [Materials from renewable raw materials] New entry - 0.4301

kg

Wing frame [Building industry] Correction 0.95 1.055 m

Wooden window frame (kg) [Building indus-try] New entry - 0.434

kg

Wooden window frame [Building industry] Correction 0.559 1.055 m

90

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thinkstep AG Hauptstr. 111 – 113, 70771 Leinfelden-Echterdingen, Germany Phone: +49 711 341 817-0 Fax: +49 711 341 817-25 E-mail: [email protected] Websites: www.thinkstep.com www.gabi-software.com _______________________________________________________________________________________________________________________________________________


http://www.thinkstep.com/

http://www.gabi-software.com/