gabi databases 2020 edition upgrades & improvements · small (1e-4 to 1e-5 kg ethene eq. per...

GaBi Databases 2020 Edition Upgrades & Improvements

February 2020

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

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About this document

This document covers relevant changes in around 12,700 upgraded LCI datasets of the GaBi Databases 2020 Edition. The document addresses both changes in technology and in methodology, when appliable, as well as reports about error corrections, and is structured by type of material/process 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In the Annex you will find the list of datasets that are no longer updated, as well as expired EPD datasets.

Sphera uses a professional issue tracking software (JIRA), so the issue numbers in the tables are issue numbers from this software. Please provide us with this number, if you have specific questions.

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.

The reference year of the GaBi Databases 2020 Edition is 2016 for all energy carrier supply mixes

(e.g., hard coal, crude oil and natural gas) and energies. For the remaining datasets the reference

year is documented in each dataset.

Please note that processes, that will no longer be updated (in the “Version 2019” folder), as well

as “flows with limited use” (for further information see Annex I: “Version 2019” discontinued da-

tasets – Explanations and Recommendations) are now marked with a separate icon in the data-

base: .

Important changes made in the 2020 GaBi Databases 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.

- HMDA from adipic acid route replaced by butadiene route: HMDA (or its precursor material

adiponitrile) can be produced in different ways. It is mainly used as a precursor for polyamide

production. Common is the production via the butadiene and acrylonitrile route. As result of

phasing out, the adipic acid route is no longer used. As the technology has changed, polyamide

data, which used the adipic acid route, was updated by replacing the HDMA. The HMDA da-

tasets based on this route will no longer be updated. The change to the butadiene route gener-

ally decreases the impact in CML GWP, AP and EP for polyamide between 20% to 40%.

- By-product SO2/H2SO4 in cobalt and nickel production - allocation replaced by credit: The com-

mon method for treatment of sulphuric acid produced in the metal production applied by the

industry is system expansion. For the smelter plans in the nickel, cobalt and PGM production

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routes, the economic allocation was hence removed and replaced by system expansion, giving

credit for sulphuric acid. This increases the impact of palladium when looking at CML Acidifica-

tion Potential by about 30%. GWP does not change in any relevant way.

- Update EU-28 methanol mix: The EU-28 methanol mix was updated with the latest data from

Eurostat and UN comtrade. The main producers and importers are now Russia and Trinidad

and Tobago. As methanol is used as a precursor for several production processes, changes will

also occur there (such as with formaldehyde). For the EU-28 methanol mix, CML GWP increases

by about 24%, AP and EP by about 200%.

- Harmonized utilization rate for trucks and ships in the database: throughout the databases, the

utilization rate of trucks and ships are now harmonized according to their specified range. This

typically leads to small increases across impacts. However, as transport is typically not very

relevant, these increases will in most cases not be visible.

- Nitric acid production update: N2O values were updated based on the National Inventory Report

2018 (reference year 2016): Due to the update of the laughing gas as well as nitrous oxide

emissions, a reduction of around 40% in GWP and around 37% to 80% in EP are seen in the

nitric acid datasets. All downstream products using nitric acid (such as ammonium nitrate) are

also affected by this change, of course.

- DDGS (dried distillers’ grain) update: The by-product DDGS is mainly used as animal feed. For

the German and European bioethanol from wheat production, the allocation of DDGS was

hence changed from energetic allocation (to reflect incineration to produce heat and electricity)

to economic allocation. The main affected potential impact category is POCP (CML), it de-

creases by about 50% for Europe and 13% for Germany, however the absolute values are very

small (1E-4 to 1E-5 kg Ethene eq. per kg).

- Updated infrastructure for geothermal energy: The geothermal plants now have updated infra-

structure.

- Further regionalization of Chinese datasets: Datasets of the region China were further region-

alized and – where available – precursor and consumables production updated to Chinese

conditions.

- Update of primary aluminium ingot for India: The electricity consumption of the electrolysis

(prebaked) was corrected (it was too low by a factor of 100). Due to this, an increase in GWP

by about 140% is seen. This also affects all products using aluminium, such as sheets or pro-

files

- Update nuclear fuel supply: Using several sources (such as publicly available company reports),

the nuclear energy production was updated (nuclear recycling process, Russian enrichment

process and creation of the MOX production process). The effects on the electricity datasets

are, however, small.

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

toward better definition and standardization of inventory methods and characterization. There-

fore, the water balance of more BF steel datasets could be improved, beyond what has been

done already in the 2019 edition. To close the water balance of the blast furnace steel route,

rainwater was added. To correct for LCI method inconsistency, as well as the blue water con-

sumption was double checked and adjusted to the most recent water elementary flows. In the

previous version, more water was being emitted to water bodies than was entering production.

This has now been corrected. This change leads to an improvement with a correct water bal-

ance and blue water consumption (for some steel sheet and steep pipe datasets, the blue water

consumption changed from negative to positive values, e.g., from about -0.7 kg to 1.4 kg per

kg of product).

- Railway tracks: The railway tracks datasets were corrected; an incorrect scaling factor was re-

duced (was too high by a factor of 73000). This change leads to a very large change in impact

results. On average, the potential impact decreases by a factor of 1E5 for each impact category.

As the dataset is not used for the background of other GaBi datasets, no other datasets were

affected.

- Electricity from biomass (solid): Eucalyptus is used as biomass for electricity production. The

water usage for eucalyptus plantations was identified to be considerably too high and was de-

creased. As irrigation water contains nitrate, the nitrogen balance was also affected. Therefore,

after correction, EP increased now by between 34% to 100% for Region Asia, China, Indonesia,

India and Malaysia. The blue water consumption decreased in contrast by about -95% for Brazil,

China, Indonesia, India, Malaysia, Russia, Region Asia and Region South America.

- Energies from biogas: Biogas from sewage sludge and biogas from landfill, which are used for

the energy conversion, were updated and corrected. On average, for electricity from biogas and

process steam from biogas, the total primary energy increased between 35% to 60%. For da-

tasets of Great Britain and Ireland, the increase for the primary energy total is about 6,500%

for electricity from biogas and process steam from biogas, as the previous value was incorrect

due to a factor mistake. Blue water consumption increased by 90% to 280%. Electricity from

biogas, process steam from biogas and thermal energy from biogas for The Netherlands, Great

Britain and Ireland now have a correct, positive water balance.

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

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Authors:

Dipl.-Ing. Steffen Schöll [email protected]

Dipl.-Ing. Jasmin Hengstler [email protected]

Dipl.-Ing. Alexander Stoffregen [email protected]

Dr.-Ing. Martin Baitz [email protected]

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

Dr.-Ing. Ulrike Bos [email protected]

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

www.sphera.com www.gabi-software.com For more information contact us at: https://sphera.com/contact-us ® 2020 Sphera. All Rights Reserved.

mailto:[email protected]






http://www.sphera.com/

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

https://sphera.com/contact-us

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List of Contents

List of Figures .......................................................................................................... 7

List of Tables ........................................................................................................... 7

Abbreviations ........................................................................................................... 8

1 Introduction to the upgrade of databases available with GaBi ....................... 9

2 GaBi Databases 2020 Edition .................................................................... 10 2.1 Principles ............................................................................................................ 10 2.2 Reasoning behind this document ......................................................................... 11 2.3 Regionalization ................................................................................................... 12 2.4 LCIA Methods – method updates, characterization factor updates, corrections ....... 12 2.4.1 Water scarcity AWARE 1.2c ................................................................................. 12

2.4.2 Land use LANCA v2.5 2018 ................................................................................. 12

2.4.3 EN 15804 ........................................................................................................... 12

2.4.4 Environmental Footprint (EF) ............................................................................... 12

2.4.5 Single elementary flows ..................................................................................... 13

2.5 New datasets ...................................................................................................... 14 2.6 Inventories for electricity, thermal energy and steam ............................................. 16 2.7 Inventories for primary energy carriers .................................................................. 27 2.8 Inventories for organic and inorganic intermediates .............................................. 30 2.9 Inventories for metal processes ........................................................................... 32 2.10 Inventories plastics processes ............................................................................. 33 2.11 Inventories for End-of-life processes ..................................................................... 35 2.12 Inventories for electronic processes ..................................................................... 36 2.13 Inventories for renewable processes .................................................................... 38 2.14 Inventories for transport processes ...................................................................... 41 2.15 Inventories for construction processes ................................................................. 42 2.16 Inventories for US regional processes ................................................................... 44 2.17 Inventories for India regional processes ................................................................ 47

3 Industry data in GaBi ................................................................................. 48

4 General continuous improvements ............................................................. 49 4.1 Editorial .............................................................................................................. 49 4.2 LCIA Methods, Normalization and Weighting factors .............................................. 49 4.3 Fixing and improvements of cross cutting aspects ................................................. 51

References ............................................................................................................ 53

Annex I: Version 2019 discontinued datasets–Explanations and Recommendations. 54

Annex II: EPDs with expired validity ......................................................................... 58

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List of Figures

Figure 1: GaBi Master Database maintenance and upgrade process 9

Figure 2: Development grid mix in Germany (left) and EU-28 (right) [Eurostat 2019] 16

Figure 3: Development grid mix United States [EIA 2017] 17

Figure 4: PED, GWP, EP, POCP and AP of electricity grid mixes DE, EU-28 and US 22

Figure 5: Changes in GWP of electricity grid mix datasets in GaBi Professional 2020 Edition 23

Figure 6: Absolute GWP of electricity grid mix datasets in GaBi Professional 2019 & 2020 Edition 23

Figure 7: Development GWP for electricity supply in selected countries 25

Figure 8: Changes in GWP electricity grid mix datasets in GaBi Extension Module Energy 202025

Figure 9: Absolute GWP of electricity grid mix datasets in GaBi Extension module Energy 2019 & 2020 26

Figure 10: Development GWP for electricity supply in selected countries 26

List of Tables

Table 1: Energy carrier mix for electricity generation – selected EU countries [IEA 2019] 17

Table 2: Energy carrier mix for electricity generation – selected non-EU countries [IEA 2019] 18

Table 3: Energy carrier mix for electricity generation – countries with significant changes [IEA 2019] 18

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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 Sphera employees were involved in the upgrade of the GaBi databases. The invested time, knowledge and dedication of our employees resulted in the new GaBi Databases 2020 Edition, with about 12,700 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 of the GaBi Databases by the Content team is enabled and supported with domain expertise along the team structure within Sphera, which is illustrated in the figure below.

Figure 1: GaBi Master Database maintenance and upgrade process

In the GaBi databases, process documentation is directly integrated in the datasets. Additional information about the modelling principles that are applied to all datasets can be found in the document GaBi Data-bases and Modelling Principles.1 Furthermore, specific modelling information on specific topics and rec-ommendations for users to get the best value out of the GaBi databases can equally be accessed in com-plementary documents that can equally be accessed on the GaBi Software website.

This present 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 principle, all Sphera-related datasets have been upgraded, with some changes occurring exclusively in the background system of datasets, others also in the foreground.

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

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Note: LCI methodology changes do not automatically imply endorsement by Sphera and have been intro-duced only when necessary: Methodological changes are only useful if these changes or improvements are supported by relevant best practice cases, evolving or edited standards or by relevant stakeholder initia-tives with a respective practice approval.

2 GaBi Databases 2020 Edition

“Facts do not cease to exist because they are ignored.” – Aldous Huxley

2.1 Principles

Sphera introduced the annual upgrade of the GaBi databases for three main reasons:

• To keep your results as up-to-date and close to evolving supply chains as possible, including auto-mated 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.

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

Changes in the environmental profile of the datasets, from the preceding year’s 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 year-to-year changes and affect the environmental 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 devel-opments in production processes might achieve, for example, higher energy efficiency or a reduc-tion of material losses and of process emissions. Sometimes, the technology is subjected 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 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.

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• Further standardization and the establishment of regulative modelling approaches. Modelling of realistic technology chains has always been the core focus of the GaBi databases. Further harmo-nization and improvement in the LCI methodology and feedback from clients and consultants at Sphera have enhanced the modelling approach for the GaBi Databases. Detailed information is given in the document GaBi Databases and Modelling Principles.2 Methodological adoptions are carried out extremely carefully, passing through multiple levels of reviews by Sphera experts re-sponsible for standardization, technology developments and quality assurance. This internal review process was audited within the continuous improvement process by our external verification part-ner DEKRA. GaBi database updates and upgrades focus on reliability 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 Sphera data, our focus is to update and apply all relevant and important improvements and changes in technology and the supply chain and the necessary and established improvements and changes in the methodology.

2.2 Reasoning behind this document

GaBi models — leading to a single aggregated dataset in the GaBi databases — consist of many datasets all along the supply chain network towards the product that is represented by the resulting dataset. This means, many smaller or bigger changes within the supply chain contribute to the overall change in impact results. The change analysis from the preceding to the latest databases edition is a time consuming, but important process within Sphera, and the results are documented in this report.

It is important to be aware, that the relevance of changes in the GaBi databases related to the user’s own systems is highly dependent on the goal and scope in the specific user model and intended application of the results. This means the same dataset may lead to significant changes for one user and one kind of application (e.g. reporting), whereas in another user’s system or another application (e.g. a comparison, with both systems being affected in the same way), 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 func-tion 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, Sphera provides the present document “GaBi Databases 2020 Edition - Upgrades and Improvements” in addition to the document “GaBi Databases and Modelling Principles,” complemented by close to 14,000 interlinked electronical documentation files of the processes supplied with the GaBi databases and also accessible online.

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


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2.3 Regionalization

The regionalization of water flows was further expanded to wastewater treatment plants. The input and output water flows (input: ground water, river water, lake water; output: processed water to groundwater, processed water to lake, processed water to river) are regionalized to the country of the wastewater treat-ment plant. Where possible, the regionalization of country specific production processes was increased, to better capture water scarcity implications.

For further information regarding water assessment and how to ensure correct and coherent regionaliza-tion at the input and output side, please see the documentation in “Introduction to Water Assessment in GaBi.”3

2.4 LCIA Methods – method updates, characterization factor updates, corrections

2.4.1 Water scarcity AWARE 1.2c

Four new AWARE 1.2c, quantities are now available.

2.4.2 Land use LANCA v2.5 2018

A new version of LANCA is now available with improved reference land use type calculations. Note that LANCA v2.3 was renamed (now includes the version number) and can now be found in the folder "earlier versions of methods."

2.4.3 EN 15804

The new EN 15804+A2 method of 2019, which is based on EF 3.0 with the only difference being the way how biogenic carbon uptake and release is considered, is now available. This allows calculations of EPDs with this new method.

EN 15804+A1 and EN 15804+A2 quantities can be found each in a respective folder. Now those quantities are sorted into folders, each individual quantity is numbered, in the same way they should appear in an EPD. For EN15804+A2, the resource use, output and waste categories are now also available as quantities.

2.4.4 Environmental Footprint (EF)

The Environmental Footprint (EF) set of characterization factors is now available for EF2.0 and EF3.0.

IMPORTANT NOTE:

With the release of the GaBi Databases 2020 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/import function. EF 2.0 continuous to be 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 recalculate results using the EF 2.0 in GaBi (datasets created by users should also be doublechecked with recent official EF documents, before claiming compliance).


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In case you need any support with this topic, please contact [email protected]

Additional information: EF 3.0 is available in GaBi in parallel to EF2.0. This version may be used exclusively in context of newly to-be-developed PEFCRs/OEFSRs during the so-called EF transition phase.

2.4.5 Single elementary flows

The following corrected characterization factors of single elementary flows have been implemented:

Environmental cost of air emissions (UBA, version 3.0, 2019):

Carbon dioxide (biotic) [Inorganic emissions to air]: 0.18€/(kg CO2) Carbon dioxide [Renewable resources]: -0.18€/(kg CO2) Methane (biotic) [Organic emissions to air (group VOC)]: 4.5 €/(kg CO2)

Environmental Footprint 2.0:

Saudi Arabia water flows (for all water factors): value is now 0.0187m3 (with the appropriate +/- 1 signs)

Water Scarcity factors have been reimplemented with exact values as rounded by the method pro-vider

Characterization factor deleted for “Abiotic depletion potential for non-fossil resources (ADPE) - EN15804 EPD results”

Arsenic V characterized according to Non-cancer human health effects

Carbon dioxide, from soil or biomass stock: factor 1 for both Climate Change (land use) and Climate Change (total)

Nitrogen (N-compounds): characterization factors for Eutrophication marine removed

Weighting for Climate Change (total) was removed in order to not have a double counting (as the individual Climate Change (biogenic, fossil, land use change) is already calculated

Environmental Footprint 3.0:

Phosphorus-pent-oxide: Characterization factor for Ecotoxicity freshwater (Inorganic) & Ecotoxicity freshwater changed

Mixed ore flows: When exporting a process dataset to ILCD format (and hence mapped at export to the EF 2.0 elementary flow list), flows with mixed ore content are split and mapped to the individual ore flows. In order to get consistent result calculations on an exported process dataset and on the same process within GaBi, flows with mixed ore content are now characterized in GaBi according to their ore content

Methane to air: Characterization factor deleted for non-cancer human health effects

Methane biotic: Characterized for Ecotoxicity freshwater (Organic): 0.31974, Ecotoxicity freshwater: 0.31974, Non-cancer human health effects (Organic): 4.8548E-8 and Non-cancer human health effects: 4.8548E-8

EN 15804+A1:

Peat flows (gas, hard coal, lignite, crude). characterization factors were adjusted according to EN 15804


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ISO 14067:

Methane biotic characterization factor changed from 28 to 30

Others:

Pit gas (in MJ): harmonized impacts with flor Pit gas (in kg)

PM 2.5-10: All quantities (i.e., characterization factors) of the flow Dust (PM 2.5-10) that are not explicitly characterized in the respective methods were set to "0"

Flows merged:

Dust (combustion) was merged into Dust (PM10) Particles to air {6b2e74cf-b7c5-4529-af62-fc001b308cc4}

The two flows “Polycyclic aromatic hydrocarbons (PAH, carcinogenic)” [Group PAH to air] {181A491F-8ED4-4EE4-9535-578DB87AEE47} and “Polycyclic hydrocarbons” [Group NMVOC to air] {55B03F71-0315-40C2-8E6E-48549AA50840} were merged into the flow Polycyclic aromatic hy-drocarbons (PAH, unspec.) [Group PAH to air] {7E4CA62F-0266-44A2-B6C7-65285E6645E9}

Unit for ionizing radiation in ReCiPe was corrected from Bq C-60 eq. to air to kBq Co-60 eq. to air. The calculations and conversions remain correct, only the unit name has been corrected.

2.5 New datasets

With this year’s upgrade, 334 new processes are available:

Professional DB:

52 new processes

Several third-party industry datasets, CN: nitrogen, CN: Oxygen, EU-28: Nitro-gen, EU-28: Oxygen, DE: Acrylonitrile-Butadiene-Styrene Granulate (ABS), CN: diverse tap water, EU-28: Hexamethylenediamine (HMDA),…

Extension DBs:

Ia “organic intermediates”: 21 new processes

FR: Acetone, acrylic acid, benzene, propene, hexamethylenediamine (HMDA),…

Ib “inorganic intermediates”: 35 new processes

Nitrogen for various countries, oxygen for various countries, sodium hydroxide for var-ious countries, sulphuric acid for various countries,…

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II “Energy”: 122 new processes

Crude oil mix for various countries, future electricity grid mixes, thermal energy from heavy fuel oil for various countries,…

IV: “aluminium”. 12 new processes

Several aluminium alloys for DE

VII “plastics”: 1 new process

DE: Polyamide 6.6 granulate (PA 6.6) (HMDA from butadiene)

IXa “end of life”: 1 new process

DE: Slag (best case, inert landfill only)

XI “electronics”: 4 new processes

GLO: IC DFN 10 (22.3 mg) 3x3 mm CMOS logic (14 nm node) GLO: IC QFN 24 (61.6 mg) 4x6 mm CMOS logic (14 nm node), GLO: IC QFN 76 (578.8 mg) 10x11 mm CMOS logic (14 nm node), GLO: Transistor signal SOT-883 (SC-101/XQFN3) (0.855 mg) 1.0 x 0.6 x 0.48

XIV “construction materials”: 15 new processes

End of Life for EPDM seals, UA and BR: Gypsum, Lime, EPD datasets,…

XVII “full US”: 10 new processes

Copper sheet and copper tube from CDA, Benzene mix, Bioethanol from corn, Bioeth-anol from wheat, Polyethylene low density granulate (LDPE/PE-LD) secondary, Poly-ethylene high density granulate (HDPE/PE-HD) secondary, …

XX “food and feed”. 12 new processes

CN, each economically and mass allocated: corn bran, corn oil, corn steep liquor, gluten feed, gluten meal

DE: corn steep liquor, mass and economically allocated

XXI “India”: 52 new processes

Region-specific electricity grid mixes, landfills, polycarbonate granulate (PC), LLD-PE, PLA import mix, PMMA granulate, POM granulate, PP fibres and fabric, PTFE, PUR high density foam, chlorine mix, ….

Details on the new datasets are available in this MS Excel file: http://www.gabi-software.com/filead-min/GaBi_Databases/Database_Update_2020_DB_content_overview.xlsx and access to the complete dataset documentation is available for searching and browsing by extension database online under http://www.gabi-software.com/international/databases/gabi-data-search/.

http://www.gabi-software.com/fileadmin/GaBi_Databases/Database_Update_2020_DB_content_overview.xlsx

http://www.gabi-software.com/fileadmin/GaBi_Databases/Database_Update_2020_DB_content_overview.xlsx

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

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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 2020, the reference year is 2016 for all electricity grid mixes and energy

carrier mixes (hard coal, crude oil and natural gas). The electricity grid mixes in the Extension Module

XVII: Full US (electricity grid mixes for US sub grids and subregions under eGRID) referred already to

2016 as reference year in the GaBi databases 2019 and have not been updated as eGRID 2016

[EPA 2018] is still the most recent version of eGRID.

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

a couple of countries due to changes in the energy carriers that were used for electricity generation,

as well as changes in the amount of imported and exported electricity and the country of origin of

the imports. The changes in the LCI datasets reveal the following trends:

• An ongoing trend in some countries to increase the share of renewable energies in their elec-

tricity generation, which is, for example, the case for Brazil, Croatia, Denmark, Finland, Ger-

many, Great Britain, Greece and Sweden.

• As in the years before, several transition countries have an ongoing increased electricity con-

sumption. In countries like China, India, Indonesia or Turkey, the domestic electricity produc-

tion has increased by 4% to 7%. In China, one third of the increased electricity demand (360

TWh) was supplied from coal and approx. 40% from renewables. In India and Indonesia, the

additional electricity was produced from fossil fuels, mostly coal or natural gas.

The following three figures present the development of the energy carrier mix for electricity generation in Germany, the European Union and the United States between 2000 and 2016.

Figure 2: Development grid mix in Germany (left) and EU-28 (right) [Eurostat 2019]

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

Compared to 2015, the share of renewable energy sources for electricity generation in Germany

stayed unchanged at 30.0%4 in 2016. Absolute electricity generation from single renewable sources

stayed very similar compared to 2015 as well. Decreasing generation from nuclear power stations

(from 14.2% to 13.1%) and coal power stations (from 42.2% to 40.4%) was compensated for by

higher generation from natural gas (from 9.8% to 12.7%).

For the EU-28, the share of natural gas in the power mix further increased again from 15.4% in 2015

to 18.8% in 2016 after a significant decrease from 22.8% in 2010 down to 14.4% in 2014. The

additional 100 TWh of electricity from natural gas has substituted mainly electricity from coal, reduc-

ing the share of coal in the grid mix from 24.1% to 21.1%. Generation from renewable energy carriers

remained nearly stable at 30%.

In the U.S., the trend of coal substitution by natural gas was going on also in 2016, decreasing the

share of coal in the grid mix significantly, from 33% to 30.2% (coming down from approximately 50%

in 2008). The share of natural gas for electricity generation increased from 27.4% to 32.5%.

In the following tables, the energy carrier mixes for 2015 and 2016 are displayed for selected coun-

tries and those with important changes.

Table 1: Energy carrier mix for electricity generation – selected EU countries [IEA 2019]

4 50% of electricity from waste is accounted as renewable energy

18

[%] France Germany Great Britain Italy Poland Spain

2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 Nuclear 77.0 72.6 14.2 13.1 20.7 21.1 0.0 0.0 0.0 0.0 20.4 21.3 Lignite 0.0 0.0 23.9 23.1 0.0 0.0 0.3 0.1 32.0 30.6 1.2 0.7 Hard coal 1.7 1.5 18.3 17.3 22.3 9.0 15.0 12.2 47.1 47.7 17.1 12.6 Coal gases 0.4 0.4 1.8 1.8 0.3 0.2 0.8 1.0 1.5 1.6 0.5 0.4 Natural gas 3.5 6.3 9.8 12.7 29.5 42.2 39.3 43.6 3.9 4.7 18.7 19.2 Heavy fuel oil 0.4 0.5 1.0 0.9 0.6 0.5 4.7 4.2 1.3 1.4 6.1 6.2 Biomass (solid) 0.4 0.6 1.7 1.7 5.7 5.8 1.4 1.4 5.5 4.2 1.4 1.5 Biogas 0.3 0.3 5.2 5.3 2.1 2.3 4.6 4.5 0.6 0.6 0.3 0.3 Waste 0.7 0.8 2.0 2.0 1.9 2.2 1.7 1.7 0.0 0.1 0.5 0.5 Hydro 10.5 11.8 3.9 4.0 2.7 2.5 16.6 15.3 1.5 1.6 11.2 14.5 Wind 3.7 3.9 12.3 12.1 11.9 11.0 5.3 6.1 6.6 7.6 17.6 17.8 Photovoltaic 1.3 1.5 6.0 5.9 2.2 3.1 8.1 7.6 0.0 0.1 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 2.2 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 [IEA 2019]

[%] Brazil China India Japan Russia USA

2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 Nuclear 2.5 2.7 2.9 3.4 2.70 2.6 0.9 1.7 18.3 18.0 19.3 19.5 Lignite 1.3 1.2 0.0 0 11.30 10.6 0.0 0.0 5.7 7.1 2.0 1.9 Hard coal 2.0 1.7 68.8 66.8 63.90 64.0 29.3 30.6 8.6 8.0 32.1 29.4 Coal gases 1.4 1.5 1.3 1.5 0.10 0.1 3.7 3.1 0.5 0.6 0.1 0.1 Natural gas 13.7 9.8 2.5 2.7 4.90 4.8 39.4 39.2 49.6 47.8 31.8 32.9 Heavy fuel oil 5.0 2.6 0.2 0.2 1.70 1.6 9.8 8.2 0.9 1.0 0.9 0.8 Biomass (solid) 8.3 8.6 0.9 1 1.70 2.8 3.3 1.4 0.0 0.0 1.1 1.1 Biogas 0.1 0.1 0.0 0 0.10 0.1 0.0 0.0 0.0 0.0 0.3 0.3 Waste 0.0 0.0 0.2 0.2 0.10 0.1 0.7 1.8 0.3 0.2 0.4 0.4 Hydro 61.9 65.8 19.3 19.2 10.00 9.3 8.8 8.2 15.9 17.1 6.3 6.8 Wind 3.7 5.8 3.2 3.8 3.10 3.0 0.5 0.6 0.0 0.0 4.5 5.3 Photovoltaic 0.0 0.0 0.8 1.2 0.40 1.0 3.4 4.9 0.0 0.0 0.7 1.1 Solar thermal 0.0 0.0 0.0 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0.1 0.1 Geothermal 0.0 0.0 0.0 0.0 0.00 0.0 0.2 0.2 0.0 0.0 0.4 0.4 Peat 0.0 0.0 0.0 0.0 0.00 0.0 0.0 0.0 0.1 0.1 0.0 0.0

Table 3: Energy carrier mix for electricity generation – countries with significant changes [IEA 2019]

19

[%] Belgium Denmark Greece Lithuania Luxembourg Portugal

2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 2015 2016 Nuclear 37.2 51.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Lignite 0.0 0.0 0.0 0.0 42.6 34.7 0.0 0.0 0.0 0.0 0.0 0.0 Hard coal 3.2 0.5 24.5 29.0 0.0 0.0 0.0 0.0 0.0 0.0 28.1 21.0 Coal gases 2.9 2.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Natural gas 32.5 26.0 6.3 7.1 17.5 27.3 42.4 24.7 30.2 11.7 20.2 20.9 Heavy fuel oil 0.3 0.2 1.1 1.1 10.9 10.2 5.9 5.5 0.0 0.0 2.5 2.2 Biomass (solid) 5.1 4.0 9.7 11.4 0.0 0.0 6.8 6.6 0.9 1.1 4.8 4.1 Biogas 1.5 1.2 1.7 1.9 0.4 0.5 1.8 3.1 2.2 3.3 0.6 0.5 Waste 3.0 2.5 5.8 5.1 0.2 0.4 2.2 4.0 3.8 5.1 1.1 1.0 Hydro 2.0 1.8 0.1 0.1 11.9 10.2 21.9 26.1 55.4 69.6 18.7 28.1 Wind 7.9 6.4 48.8 41.9 8.9 9.5 17.4 28.4 3.7 4.6 22.1 20.7 Photovoltaic 4.4 3.6 2.1 2.4 7.5 7.2 1.6 1.7 3.8 4.6 1.5 1.4 Solar thermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Geothermal 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.3 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

The following list summarizes countries with significant changes in the energy carrier mix for electric-

ity generation:

• Belgium (BE) The restart of several nuclear reactors (Doel 1, Doel3 & Tihange2) in 2016

has increased the share of nuclear power generation from 37.2% in 2015 to 51.2% in 2016.

Consequently, the share of electricity from natural gas dropped from 32.5% to 26% and im-

ports decreased from 26.2% to 15.4%.

• Brazil (BR) Higher generation from hydro power stations increased the share of hydro

power from 61.9% in 2015 to 65.8% in 2016, substituting mainly electricity from natural gas.

• Denmark (DK) Lower generation from wind power installations (dropped from 48.8% in

2015 to 41.9% 2016) resulted in an increase of electricity from coal (increase from 24.5%

to 29.0%) and biomass (increase from 9.7% to 11.4%).

• France (FR) Output from nuclear power plants dropped by 34 TWh (total generation in

2016 568 TWh), reducing the share of nuclear power in the grid mix from 77.0% in 2015 to

72.6%). A part of the lower output was compensated by natural gas power stations, increas-

ing the share from 3.5% to 6.3%.

• Great Britain (GB) A relevant substitution of coal by natural gas for electricity generation

significantly reduced electricity from hard coal (decreased from 22.3% in 2015 to 9.1% in

2016). Consequently, the share of natural gas increased from 29.5% in 2015 to 42.2% in

2016.

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• Greece (GR) The share of generation from lignite power plants dropped from 42.6% in

2015 to 34.7%, which was compensated for mostly by natural gas.

• Lithuania (LT) Output from natural gas power plants dropped from 1.6 TWh in 2015 to

0.6 TWH in 2016 (total generation 4.9 TWh in 2015 vs. 4.3 TWh in 2016). Lower generation

was partly compensated by wind power (increased from 17.4% in 2015 to 28.4% in 2016)

and imports (increase from 67.9% in 2015 to 77.3% in 2016 related to electricity supply).

• Malta (MT) Output from oil power stations dropped from 961 GWh to 731 GWh, which was

mainly compensated for with higher imports (increase from 46% to 65.5%) and to a lesser

extent with higher generation from photovoltaics (increase from 93 GWh to 125 GWh). Con-

sequently, the share of PV in the grid mix increased from 7.1% in 2015 to 14.6% in 2016).

• Netherlands (NL) A part of electricity from hard coal (decreased from 36.1% in 2015 to

31.9% in 2016) was substituted by electricity from natural gas (increased from 42.3% in

2015 to 46.9% in 2016).

• New Zealand (NZ) Higher generation from hydro power stations (increased from 55.6% in

2015 to 59.9% in 2016) reduced output from fossil power stations (decreased from 19.8%

in 2015 to 16.0% in 2016).

• Portugal (PT) Like observed in previous years for Portugal, water availability for electricity

generation can lead to relevant annual changes in the grid mix: In 2016, the share of hydro

power in the grid increased from 18.7% in 2015 to 28.1%. Consequently, output from hard

coal power stations was reduced, reducing the share from 28.1% to 21.0%.

• Spain (ES) Higher generation from renewables (increased from 36.0% in 2015 to 39.6%

in 2016) has compensated the reduced output from coal power stations (decrease from

18.3% in 2015 to 13.3% in 2016).

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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 (GWP5), acidification potential (AP5), eutrophication potential (EP5) and photochemical

ozone creation potential (POCP5) per kWh of supplied electricity in Germany, the European Union and

the United States. In the 2020 edition databases, the emission factors for the combustion of fuels

in power plants have been updated compared to the 2019 edition, with exception of the eGRID sub-

regions (Extension Module XVII: Full US - electricity grid mixes for US sub grids and subregions under

eGRID) for which eGRID 2016 [EPA 2018] is still the most recent version.

In Germany, the grid mix remained relatively stable, resulting in moderate changes for the GWP,

which decreased slightly from 568 g CO2-eq./kWh in 2015 to 562 g CO2-eq./kWh in 2016. Lower

output from nuclear power stations (reduction of generation from 91 TWh to 84 TWh) and coal power

stations (down from 272 TWh to 261 TWh) was compensated for by higher utilization of natural gas

power stations (63 TWh in 2015, 82 TWh in 2016). Output from renewable power installations re-

mained nearly unchanged (194 TWh in 2015 vs. 195 TWh in 2016). Changes in PED, AP, EP and

POCP are low and are linked to changes in the energy carrier mix, infrastructure or updated emissions

factors for combustion plants.

For the EU-28, the GWP decreased from 417g CO2-eq./kWh in 2015 to 397g CO2-eq./kWh in 2016,

related mostly to a substitution of coal by natural gas for power generation (share of coal decreased

from 24.1% in 2015 to 21.2% in 2016) and higher efficiencies for natural gas power stations. Re-

ductions of EP, AP, POCP are mostly related to improved flue gas cleaning for coal and fuel oil power

plants (especially SO2 and NOx) and the reduced share of electricity from coal.

In the U.S., the GWP decreased from 585 g CO2-eq./kWh in 2015 to 551 g CO2-eq./kWh in 2016.

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

(decreased from 32.1% in 2015 to 29.4% in 2016) by natural gas (increased from 31.8% in 2015

to 32.8% in 2016) and the generation of renewables (increased from 13.7% in 2015 to 15.3% in

2016). Changes in PED, EP, AP and POCP are related to changes in the energy carrier mix, fuel supply

and infrastructure.

5 CML 2001, Updated Januar 2016

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Figure 4: PED, GWP, EP, POCP and AP of electricity grid mixes DE, EU-28 and US

The following figures present the percentile changes of the greenhouse gases for the upgraded elec-

tricity grid mixes in the GaBi Professional database and the Extension Module Energy compared to

the 2019 edition data (reference year 2015), as well as the absolute greenhouse gas emissions per

kWh in the 2019 and 2020 edition databases (reference year 2016).

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Figure 5: Changes in GWP of electricity grid mix datasets in GaBi Professional 2020 Edition

Figure 6: Absolute GWP of electricity grid mix datasets in GaBi Professional 2019 & 2020 Edition

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

energy carrier mix or imports:

• Austria (AT) A reduction of electricity generation from coal (decrease from 4.6% to 3.0%)

and lower imports, resulted in a decrease of the carbon intensity of the electricity from

356g CO2-eq./kWh in 2015 to 302g CO2-eq./kWh in 2016.

• Belgium (BE) Due to the restart of several nuclear reactors (Doel 1& 3, Tihange 2), the

share of nuclear power, the carbon intensity of supplied electricity dropped from 272g CO2-

eq./kWh in 2015 to 205g CO2-eq./kWh in 2016.

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

creased from 247g CO2-eq./kWh in 2015 to 310g CO2-eq./kWh in 2016. The 26% increase

in greenhouse gases per supplied kWh electricity is related to lower output from the wind

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power installation (48.8% in 2015 to 41.9% in 2016), mainly compensated for by electricity

from coal.

• Finland (FI) Compared to 2015, the GWP per supplied unit of electricity in Finland has

increased by 19% from 175g CO2-eq./kWh in 2015 to 209g CO2-eq./kWh in 2016. The in-

crease is related to lower electricity output from hydro power plants, compensated for by

electricity from coal.

• Great Britain (GB) The carbon intensity of grid electricity has been reduced from 416g CO2-

eq./kWh in 2015 to 339g CO2-eq./kWh in 2016 through a reduction in coal use, instead

using natural gas for electricity generation.

• Lithuania (LT) The carbon intensity of grid electricity has been reduced from 656g CO2-

eq./kWh in 2015 to 424g CO2-eq./kWh in 2016 by higher generation from wind power instal-

lation and higher imports with lower carbon intensity.

• Malta (MT) The carbon intensity of grid electricity has been reduced from 841g CO2-

eq./kWh in 2015 to 607g CO2-eq./kWh in 2016, mainly due to higher imports substituting

electricity from fuel oil power stations.

• Portugal (PT) Like in previous years, changing water availability for power generation re-

sulted in higher output from hydro power stations (increased from 18.7% in 2015 to 28.1%

in 2016) and lower generation from hard coal power stations. Consequently, the carbon in-

tensity for power generation decreased from (471g CO2-eq./kWh in 2015 compared to

389g CO2-eq./kWh in 2016).

• Spain (ES) Similar to Portugal, higher water availability for hydro power generation re-

sulted in lower GWP values for grid electricity (decrease from 415g CO2-eq./kWh in 2015 to

359g CO2-eq./kWh in 2016).

• Sweden (SE), France (FR) The high 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 increased from 37g CO2-eq /kWh in 2015 to 46 g in

2016. In France, the GWP increased from 64 g CO2-eq./kWh in 2015 to 79 g in 2016, mainly

due to lower generation from nuclear, which was partly compensated for by generation from

natural gas.

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 10%, in the EU by 19%. The

share of renewables for power generation in Germany has increased significantly from 15% in 2008

to 30% in 2016, substituting mostly nuclear power. In some of the other EU Member States, relevant

GWP reductions have been achieved over the last seven years, mainly due to a substitution of fossil

25

fuels by renewable sources, e.g., Denmark -40%, Great Britain -42%, Greece -27%, Portugal -28%

and Romania -33%. 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 17%. In Japan, a different development can be seen, related largely to the shift

toward more fossil fuels after the 2011 Fukushima catastrophe.

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 electricity

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 2020

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Figure 9: Absolute GWP of electricity grid mix datasets in GaBi Extension module Energy 2019 & 2020

Figure 10: Development GWP for electricity supply in selected countries

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

The energy mix and the emission factors of the eGRID subregion and US sub grids datasets have not been updated as eGRID2016 is still the most recent version of eGRID. Changes within the data sets compared to the 2019 GaBi database are related to fuel supply and infrastructure (e.g. photovoltaic panels or wind power plants).

27

Further developments in electricity datasets

Changes in electricity datasets from specific fuels:

Power plant efficiencies, calculated based on energy statistics, can significantly vary between refer-

ence years. The following reasons are considerations for variations over time:

• final or periodic shutdown and use as reserve capacity 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 (or to reduce emissions and thereby reduce effi-

ciency)

• rounding effects (if a small amount of fuel is used)

• correction of statistical errors

• a combination of several of the factors listed above

The update of the nuclear fuel supply chains leads to smaller changes for electricity generation from nu-clear below 10% for GWP, except for France (reduction of the GWP result by 13%).

Changes in the environmental impacts of electricity from photovoltaic are related to the update of market technology mixes and technology updates.

Updates for electricity from geothermal power lead to better results for photochemical ozone formation, but worse results for eutrophication.

2.7 Inventories for primary energy carriers

The reference year of the GaBi databases 2020 Edition is 2016 for all energy carrier supply mixes (e.g., hard coal, crude oil and natural gas). The changes in the environmental impacts of the energy carrier pro-cesses after the upgrade are described in the following paragraphs.

The environmental impacts of the lignite mixes changed due to the update of the country-specific consump-tion mixes (mix of domestic production and imports) and changes in the background data. The country-specific lignite mixes show smaller changes for GWP of below 5%.

Changes in the results of the hard coal mixes are related to the update of the country-specific consumption mixes and changes in the background data, too. All country-specific hard coal mixes show GWP changes of less than 10% except the following two mixes. The changes in the consumption mixes of these two coun-tries can be related to the consideration of a more detailed data source for the calculation of the mixes:

28

• Hard coal mix of Argentina (AR) higher impacts (e.g., GWP +10%) because of increasing hard coal imports with higher environmental impacts (e.g., from Australia, Canada and Colombia) and decreasing coal imports from the United States and South Africa.

• Hard coal mix of Morocco (MA) GWP increases by 11% due to higher hard coal imports from Russia, Poland and the United States, and less imports from China, Colombia, Indonesia, Venezuela and South Africa.

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, the production of unconventional natural gases, like shale gas, tight gas and coalbed methane, was updated. Natural gas mixes with changes in the GWP results of more than 10% are listed in the following:

• Natural gas mix of Belgium (BE) less natural gas imports from the United Kingdom and Qatar (via liquefied natural gas (LNG) transport), and higher imports from Germany, the Netherlands and Norway led to a reduction of the GWP by 12%.

• Natural gas mix of Brazil (BR) some of the LNG imports from Nigeria and Norway were replaced by domestic natural gas production. This resulted in a reduction of the GWP by 11%.

• Natural gas mix of Egypt (EG) the GWP increased significantly (+ 56%). The reasons were the declining share of domestic natural gas production and the compensation by LNG imports from Nigeria, Norway, Qatar and Trinidad and Tobago.

• Natural gas mix of France (FR) less natural gas imports from the Netherlands and Norway, and higher imports from Qatar and Russia increased the GWP by 12%.

• Natural gas mix of Ireland (IE) the GWP increased significantly (+ 90%) due to a higher share of domestic production and less natural gas imports from the United Kingdom.

• Natural gas mix of Lithuania (LT) less natural gas from Russia and increasing imports from Nor-way led to a reduction of the GWP by 14%.

• Natural gas mix of Mexico (MX) the GHG emissions decreased by 12% due to higher shares of natural gas from Nigeria and the United States and decreasing shares from Qatar and domestic production.

• Natural gas mix of the Netherlands (NL) increasing shares from Russia and decreasing shares from domestic production and imports from Norway worsened the GWP result by 17%.

• Natural gas mix of Romania (RO) the domestic natural gas production was reduced and com-pensated by natural gas imports from Russia. As a result, the GWP increased by 16%.

• Natural gas mix of Ukraine (UA) the GWP increased by 39% due to significant changes in the natural gas consumption mix (decreasing share of natural gas from Russia and increasing share of domestic production and natural gas imports from European countries).

• Natural gas mix of the United Kingdom (GB) less natural gas from Qatar and more natural gas imports from the Netherlands, Norway and Trinidad and Tobago lead to a reduction of the GWP by 11%.

• Natural gas mix of the United States (US) the GHG emissions were reduced by 16%. The main reasons were the update of the unconventional natural gas production and small changes in the consumption mix.

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Changes in the results of the crude oil mixes were related to the update of the country-specific consumption mixes and changes in the background data.

• Crude oil mix of Belgium (BE) the GWP result was reduced by 14% due to lower crude oil imports from Nigeria, Norway and Saudi Arabia, and higher crude oil imports from Gabon, Iran, Kuwait, Russia and the United States.

• Crude oil mix of Bulgaria (BG) the GHG emissions increased by 16% because of major changes in the consumption mix (more crude oil imports from Iraq, Kuwait, Kazakhstan and Saudi Arabia, and less imports from the United Arab Emirates, Egypt, Italy, Libya and Russia).

• Crude oil mix of Croatia (HR) reduced crude oil imports from Azerbaijan, Nigeria and Russia, and more crude oil imports from Brazil, Egypt, Iraq, Kazakhstan, Libya and Saudi Arabia led to an in-crease of GHG emissions by 11%.

• Crude oil mix of Ireland (IE) the GWP result was reduced by 15% due to changes in the crude oil mix (higher crude oil imports from Azerbaijan, Algeria and Norway, and less imports from Canada, Denmark, Egypt, the United Kingdom and Nigeria).

• Crude oil mix of Morocco (MA) less crude oil imports from Iraq, Russia and Saudi Arabia, and more imports from Algeria led to a reduction of the GWP by 29%.

• Crude oil mix of New Zealand (NZ) changes in the consumption mix (more crude oil from the United Arab Emirates, Australia and Saudi Arabia, and less crude oil from Brunei, Indonesia, Kuwait, Malaysia, Qatar and Russia) led to a reduction of the GWP by 16%.

• Crude oil mix of Norway (NO) the GWP result was reduced by 10% due to lower crude oil imports from Russia and a higher domestic crude oil production.

• Crude oil mix of Peru (PE) higher impacts (GWP + 18%) were caused mainly by the reduction of domestic production and crude oil imports from Brazil, and higher crude oil imports from Colombia, Ecuador, Saudi Arabia as well as from Trinidad and Tobago.

• Crude oil mix of Portugal (PT) the GWP result was reduced by 13% due to significant changes in the consumption mix (more crude oil imports from Algeria, Azerbaijan, Brazil, Congo, Cameroon, Gabon, Iraq, Mexico and Russia, and less crude oil from Angola, the United Kingdom, Nigeria, Nor-way and Saudi Arabia).

• Crude oil mix of Serbia (RS) major changes in the consumption mix (less imports from Kazakh-stan, Romania and Russia, but more imports from Iraq and Nigeria) led to a significant higher GWP result (+ 63%).

• Crude oil mix of Singapore (SG) the GHG emissions were reduced by 14% due to increasing crude oil imports from Kuwait, Libya, Saudi Arabia and Vietnam, and less crude oil from Iraq, Oman, Qatar and Russia.

• Crude oil mix of South Africa (ZA) more crude oil imports from Angola and Saudi Arabia, and reduced imports from the United Arab Emirates, Gabon, Iraq, Kuwait and Nigeria worsened the GWP result by 18%.

• Crude oil mix of Sri Lanka (LK) less crude oil imports from Oman and an increase of imports from the United Arab Emirates led to a reduction of the GWP by 14%.

• Crude oil mix of Ukraine (UA) the domestic crude oil production was reduced and compensated by crude oil imports from Kazakhstan. As a result, the GWP increased by 18%.

30

• Crude oil mix of the United Kingdom (GB) the GWP was reduced by 14% due to higher domestic production and crude oil imports from Norway, and less crude oil imports from Angola, Algeria, Nigeria and Venezuela.

Reductions in the GWP results of the refinery products of South Africa by 10% to 16% were mainly related to changes in the South African crude oil mix. The changes in the GWP results of refinery products from other countries were below 10%.

The environmental impacts of the fuel mixes (diesel and gasoline, at refinery and filling station) change of because of updated country-specific biofuel and fossil fuel consumption mixes, the update of the country-specific blending quota of biofuels and changes in the biofuel and crude oil supply chains. All country-specific fuel mixes show GWP changes of less than 10% except the

• Gasoline mix (E25) of Brazil (BR) the GWP was reduced by 11% due to the updates stated above. • Gasoline mix of South Africa (ZA) a better GWP result for the South African crude oil mix leds to

a better GWP result for the gasoline mix (reduction by 10%). • Diesel mix of the United Kingdom (GB) the GWP was reduced by 11% due to the update of the

biofuel feedstock.

2.8 Inventories for organic and inorganic intermediates

Possible updates and upgrades of technologies may happen on 3 different levels, while 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 chemical 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 con-servation, 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 possible break-through technologies, no major new technologies or significant process improvements on existing technol-ogies were identified by Sphera experts in this year’s upgrade.

Changes in the background system mainly relate to:

• Upgraded distribution on primary, secondary and tertiary fossil resource extraction, like oil and gas • Upgraded market share of imported fossil resources • Upgraded distribution of the type of resources used (oil, gas and coal, etc.) • Increased amounts of renewable feedstock and energy supplies

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Changes in the energy sector and supply chain are, in most cases, a key driver for overall improvement throughout several impact categories. The intermediates are directly influenced by the upgraded perfor-mance 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 effect on single or multiple datasets are highlighted with a bold JIRA6 number. Moreover, all issues with changes of a high or very high relevance in one of the more robust impact categories for at least one dataset are highlighted with a bold JIRA number; the same is done for all material groups/subchapters below:

Table 2- 1: JIRA issues for organic and inorganic intermediates

JIRA Tracking Number

Issue Cate-gory

Item Description Change in results Affects Extension module

GC-7627 Improve-ment

N2O emis-sions update in nitric acid production

According to the National in-ventory report 2018, laugh-ing gas as well as nitrous oxide emissions were up-dated for nitric acid produc-tion.

A reduction of around 40% in GWP and up to 80% in EP were seen in the nitric acid produc-tion.

Several


European Methanol mix update

The EU-28: Methanol mix was updated with the latest data from Eurostat and UN comtrade. The main producers and im-porters are now Russia and Trinidad and Tobago. As methanol is used as precur-sor for several production processes, changes will also occur there.

Due to the changed country mix, impacts increased: GWP: + 24% ODP: +1% Primary energy: +10% AP: + 200% EP: + 200%

Extension data-base Ia: organic intermediates

GC-8403 Bug Harmoniza-tion of hydro-gen cyanide (prussic acid) precursor

Hydrogen cyanide now con-sistently uses methane as precursor and not natural gas.

Difference in results: small dif-ferences in the main impact cat-egories (EP, POCP, GWP, AP) in a range of 0.4% ~ 8%

Professional da-tabase Extension data-base Ib: inor-ganic intermedi-ates

GC-8404 Bug Amount of natural gas quantity based on me-thane com-position

The methane production from natural gas was up-dated with country specific methane shares in the natu-ral gas.

This affects the amount of natu-ral gas input and therefore in-creases or decreases all impact categories equally: AU: Methane +26% BE: Methane -25% DE: Methane +15% ES: Methane +2% EU-28: Methane +9% FR: Methane +18% GB: Methane -17% IN: Methane +12% IT: Methane +1% NL: Methane +17% NO: Methane +12% SA: Methane +13% US: Methane -2%

Several

6 JIRA is our issue tracking system. Please provide this number if you have specific questions back to us.

32

2.9 Inventories for metal processes

All data and models have been checked by Sphera metals experts regarding technological upgrades.

Table 2- 2: JIRA issues for metal processes

JIRA Track-ing Num-ber

Issue Cate-gory

Item Description Change in results Affects Exten-sion module

GC-7379 Bug Water con-sumption in DE BF steel production

The water balance of the blast furnace steel route was corrected. Due to a double counting, more wa-ter was being emitted than entering the production.

Rainwater has been added as additional input.

Blue water consumption in-creased for the steel datasets. The relative changes are quite high, for the absolute numbers the increase of blue water con-sumption is about 2 to 2.5 kg. For some steel sheet and steep pipe datasets, the blue water consumption changed from neg-ative to positive values

Several

GC-7413 Improvement Updated Ferro Molyb-denum da-taset as pre-cursor for steel

An updated Ferro Molyb-denum (60%) process is now used as a precursor to steel datasets.

Only minor changes to results (small decrease)

Several

GC-7785 New New DE: Stainless steel cold roll dataset

A new DE: Stainless steel cold roll dataset is now available in the professional database.

New dataset Professional da-tabase

GC-7790 Improvement Harmonize water on Iron ore produc-tion plans

Tap water input and wastewater output were re-gionalized for iron ore da-tasets from AU, BR and CA.

EF 2.0 Water scarcity [m³ world equiv.] AU:-54% CA:-20% BR: -67%

Several

GC-8003 Editorial Industry data: change folder name to European Aluminium

Folder name is now named European Aluminium

Does not change the results Professional da-tabase

GC-8324 Improvement By-product SO2/H2SO4 in cobalt and nickel pro-duction - eco-nomic alloca-tion replaced by credit

For the smelter plans in the nickel, cobalt and PGM pro-duction routes, the eco-nomic allocation was re-placed by system expansion giving credit for sulphuric acid. In the course of this change, the Canadian and Russian sulphuric acid by-product amount was adapted. The produced amount was reduced.

Following changes arise as a re-sult: GLO Nickel mix: the primary en-ergy demand is reduced by about 14%, AP and POCP are in-creased by ca. 18%, EP by 2%. GWP is slightly decreased by only 2%. PGM's: almost no changes to the results.

Extension data-base VI: precious metals

GC-8371 Bug Water con-sumption in PGM data (Platinum, Rhodium, Palladium) and Nickel

The water balance of the PGM and nickel route were corrected. The water inputs and outputs now correctly consider only the used wa-ter, and not recycled water, which reduces the freshwa-ter amount and conse-quently reduces the water losses.

GLO: Palladium mix GLO: Platinum mix GLO: Rhodium Mix The blue water consumption is reduced by ca. 90% GLO: Nickel mix The blue water consumption is reduced by ca. 60% GLO: Cobalt mix, Ni/Co and

Extension data-base VI: precious metals

33


Issue Cate-gory


Cu/Co route The blue water consumption is reduced by ca. 20%

GC-8473 Bug Lithium hy-droxide and chloride pro-duction

Input amount of lithium hy-droxide for lithium chloride production was reduced, since the reaction needs an aqueous solution of lithium hydroxide and the water was mistakenly accounted as also being lithium hy-droxide. It is now separated into LiOH and H2O.

Due to the reduction of the LiOH input of about 60% all impacts reduce about 55% to 60%. Lith-ium Iron Phosphate battery changes only minimally

Extension data-base XIV: con-struction materi-als Data on demand

GC-8517 Improvement Thermal en-ergy in coke-oven

Where a coke oven is used, thermal energy from natural gas is now used instead of a blast furnace gas proxy. Affected datasets are DE: Coke mix and US: Coke mix.

GWP rises by about 10% for DE and US ADP elements increases by 20% for DE, for US by 60%

Several

GC-9224 Documenta-tion

Documenta-tion of Brass

Brass is produced mainly from secondary route (scrap). This is now also stated in the first documen-tation field "treatment, standard, routes".

Does not change the results Professional da-tabase Extension data-base XIV: con-struction materi-als

GC-9259 New New Zircon sand mix da-taset from ZIA

A new Zircon sand mix da-taset from ZIA is now availa-ble in the professional data-base.

New dataset Professional da-tabase

2.10 Inventories plastics processes

The environmental profile of polymers is largely influenced by the monomer impacts. Sphera experts checked whether the polymerisation technologies are still representative. To our knowledge, no completely new process designs in polymerization are in industrial use compared to the preceding year. The polymer-ization technologies in the GaBi Databases are considered representative. This is supported by our experi-ence working for the chemistry and polymer industries.

More specific aspects are mentioned in the following table:

Table 2- 3: JIRA issues for plastics processes

JIRA Track-ing Number

Issue Cate-gory

Item Description Change in results Affects Ex-tension module

GC-7789 Improvement Harmonization of water for Polyam-ide 6.6 granulate (PA 6.6)

Several "Polyamide 6.6 granulate (PA 6.6)" datasets now use the cor-rect regionalized water input.

Impacts will change only when looking at regionalized water methods.

Several

GC-8220 Improvement Import mix of plas-tics in Germany

The import mix of different plastics in DE were updated according to the latest Eurostat data.

Impact changes be-tween +- 5-15% for all categories.

Several

34


Issue Cate-gory


GC-8322 Improvement EU bio-based plas-tics feedstock sugar beet and wheat

EU bioplastics, such as PE, PP, PET etc., which are based on feedstock sugar beet and wheat, now have EU-28 Consumption and Produc-tion mixes as feedstocks.

Mainly the changes can be found in AP, EP and GWP espe-cially for the wheat-based datasets. Biggest relative change is around 300%, but since the absolute values are small, changes have a bigger relative change

Several

GC-8247 GC-8541

Improvement Harmonization of production route of Hexamethylene di-amine (HMDA)

HMDA is mainly used as precursor for Polyamide production. HMDA, respectively the precursor material adiponitrile, can be produced via different routes. In usage today is the butadiene and acrylonitrile route, while the adipic acid route is no longer used. This is now re-flected in the datasets. Addition-ally, the production process of HDMA via adiponitrile now has the correct amount of waste being treated. Due to a mismatch, wastewater was treated as hazard-ous waste. This is now corrected.

Due to the changes the environmental burdens of the Ny-lon/Polyamide 6.6 and 6.10 production are reduced by about 15% for GWP. PA granulate from BE, DE, GB, IN, IT, NL and US are re-duced by about 40%, as here the production route was also updated.

Professional database Extension da-tabase VII: plastics

GC-8692 Improvement Bioplastic/ bioeth-anol route - treat-ment of by-product DDGS

Based on literature sources, the by-product DDGS is mainly used as animal feed. Therefore, the US and DE bioethanol (feedstock wheat) production is modelled applying an economic allocation. For the EU bi-oethanol production, DDGS was used energetically (burned to pro-duce heat and electricity), which was changed to economic alloca-tion for the US and DE. Due to the change, following LCIA results of bioplastic data has been changed: EU-28 Polyvinyl chloride granulate (S-PVC) (bio based from wheat) {32d7f24d-9455-4604-b627-62749aab97ca} EU-28 Polyethylene terephthalate granulate (PET) via terepht. acid + EG (partially bio based from wheat) {6272d065-e59e-477e-9611-8f678f641d6e} EU-28 Polypropylene granulate (PP) (bio based from wheat) {0917de63-d32d-4a4d-b232-fc82726799c1} EU-28 Polyethylene Low Density Granulate (LDPE/PE-LD) (bio based from wheat) {963d13f3-4845-4c5a-90fa-288f7c05df34}

For AP, the impacts are reduced by 6 to 40%, for EP about -2% (for EU EP in-creases by 70%) GWP is reduced by 20% to 70% for PE PP and PVC. POCP changes are be-tween -25% to -85%. Primary energy de-mand about 10% to 20%.

Extension da-tabase XIX: bi-oplastics

GC-8704 Documentation Flow diagram for Hexamethylenedia-mine (HMDA)

Hexamethylenediamine (HMDA) datasets now show the correct flow diagram.

Does not change the results

Professional database Extension da-tabase VII: plastics

35


Issue Cate-gory


GC-8809 Improvement Harmonize waste incineration on all bio based plastic production plans

Production of all bio-based plastics (e.g., BR Polyethylene high density granulate (HDPE/PE-HD) bio based from sugar cane) datasets was checked, and where necessary a plastic waste incineration was ex-changed with an incineration of bio-based plastic materials in or-der to account for the correct amount of biogenic CO2.

GWP Values changed by -+50%. One dataset was af-fected with a change of -92% in GWP.

Several

2.11 Inventories for End-of-life processes

All data and models have been checked by Sphera metals experts regarding technological upgrades and were identified as representative for their technology descriptions in 2019.

Other more specific aspects are mentioned in the following table.

Table 2- 4: JIRA issues for end-of-life processes


Issue Cate-gory



Documenta-tion of mu-nicipal wastewater treatment datasets

The technology description for EU-28 Municipal waste water treatment (mix) now correctly says, "The waste water composi-tion to the plant represents an average outflow of a mu-nicipality to the treatment plant" instead of "The waste water composi-tion to the plant represents an average outflow of a *chemical industry com-modity* to the treatment plant..."

Does not change the results Several

GC-7782 Bug Sludge drying in wastewater treatment plant

An inconsistency in the cal-culation of the sludge amount coming out of the pre-thickening and dewater-ing was corrected.

Significant changes occur due to this correction. GWP, POCP, EP and PM decrease by about 80%

Several

GC-8490 New New dataset DE: Slag (best case, inert landfill only)

New dataset created for al-ready vitrified slag (landfill only): DE: Slag (best case, inert landfill only) Hazardous waste treatment {ffe97889-7abf-4e2d-9b0a-1917c9729bf2}.

New dataset Extension data-base IXa: end of life


Iron fraction in waste in-cineration plant

Documentation now shows the correct amount of iron waste collected from the waste incinerator.



Documenta-tion for land-fill datasets

Technology description was adapted to better describe the dataset.


36


Issue Cate-gory


with non-con-vertible ma-terial

GC-9040 Bug Harmoniza-tion of water in-and output where a wastewater treatment is used

Datasets using a wastewater treatment plant are now further harmo-nized, so that input water and output use the same geographic country.

Will change the result when looking at regionalized water method, in that case higher im-pact changes are possible

Several

GC-9290 Bug Double flow instance con-nections on End of Life Glass plans

Some End of Life plans had a double flow connection of processes. This has been fixed.

Does not change the results Extension Data-base IXb: end of life parameter-ised models


Documenta-tion: Com-ments on "generic end-of-life" plans and process

Comments in the process GLO: Generic End-of-Life splitter with methodological choices {DBB118F3-A233-4143-8757-373EC8D520C8} now make it clear, that values entered for share of material recy-cled, material with credits and not credits should range from 0-1.

Does not change the results Extension Data-base IXb: end of life parameter-ised models

GC-9366 Improvement Harmoniza-tion of plas-tics incinera-tion of Dis-posal of plas-tics plans

Plastic incineration on dis-posal plans were further harmonized to reflect the country locale.

Changes to results are small Extension Data-base IXb: end of life parameter-ised models

2.12 Inventories for electronic processes

All data and models have been checked by Sphera electronic experts regarding technological upgrades and were identified as still representative for their technology descriptions in 2019. According to Sphera electronic experts, any possible differences when comparing the results of impact categories with the same results for 2019 were due to changes in background data in the metals and energy sector (see correspond-ing chapters in this document).

Table 2- 5: JIRA issues for electronics processes


Issue Cate-gory


GC-7847 New New transis-tor package SOT-883

A new transistor dataset is now available in the Exten-sion database XI: electron-ics: GLO: Transistor signal SOT-883 (SC-101/XQFN3) (0.855 mg) 1.0 x 0.6 x 0.48.

New dataset Extension data-base XI: electron-ics

37


Issue Cate-gory


GC-8045 Editorial Process and flow name harmoniza-tion for ther-mistors

Process and flow names now give the correct diame-ter and thickness.

Does not change the results Extension data-base XI: electron-ics

GC-8413 New Three new IC datasets

Three new IC datasets are now available in the Exten-sion database XI: electron-ics: 1. GLO: IC DFN 10 (22.3 mg) 3x3 mm CMOS logic (14 nm node) 2. GLO: IC QFN 24 (91.9 mg) 4x6 mm CMOS logic (14 nm node) 3. GLO: IC QFN 76 (578.8 mg) 10x11 mm CMOS (14 nm node).

New dataset Extension data-base XI: electron-ics


Documenta-tion of ICs

The following comment was added to all IC datasets: "NOTE: this dataset does not include PWB substrates or any other electronic com-ponents (e.g., capacitors) contained inside the IC package."


GC-8981 Improvement Update of IC specific pro-duction elec-tricity mix (country mix)

Country mix (shares) up-dated with newer infor-mation. EU electricity grid mix added.

Impacts change in GWP, AP, EP, POCP, Blue water, PE less than 1%. Only in few cases for AP/POCP/EP changes around 10%.

Extension data-base XI: electron-ics

GC-9126 Bug Gold bond wire usage in the WLP-CSP datasets

Both IC WLP CSP 196 and 425 datasets were using a CSP chip material declara-tion with bond wires in them. Since however WLP CSP chips are purely flip chips, both chips have been updated to use material declaration from relevant flip chips.

For IC WLP CSP 196 datasets: GWP, AP, EP and POCP: 1-4% re-duction ADPe : 87-94% reduction (due to removal of gold bond wires) For IC WLPCSP 425 datasets: GWP, POCP: 1-5% reduction AP, EP: 94-99% reduction (higher part mass but lower die mass, lower electricity consump-tion) ADPe: >100% increase (mainly coming from the fact that "lami-nate" in material declaration has been mapped to PWB sub-strate (a 2 layer chem. Elec. Au-Ni PWB) causing an increase in gold content]

Extension data-base XI: electron-ics

GC-9150 Improvement Update elec-tronic mate-rial declara-tion quanti-ties in flows for IC WLP CSP 49, 196 and 425

All WLP CSP 196 and 425 flows now have updated material quantities: IC WLP CSP 425 (4.78g) (19x19x1.5mm) CMOS logic (14 nm node){43513281-5e04-40b1-b46c-43e611656a54} IC WLP CSP 196 (209mg) (12x12x1.41mm) CMOS logic (14 nm node){ae9c55cb-ee99-40b6-a672-


38


Issue Cate-gory


1cd073d7c692} IC WLP CSP 196 (209mg) (12x12x1.41mm) flash (45 nm node) {6b84611e-74ae-40fd-b053-8671e75a25b6} IC WLP CSP 425 (4.78g) (19x19x1.5mm) DRAM (57 nm node) {50aaeebf-e131-4189-a0f6-af9eacd452c0} IC WLP CSP 196 (209mg) (12x12x1.41mm) CMOS logic (22 nm node) {b78695c0-b471-4486-ad1e-d0d6a071a39f} IC WLP CSP 196 (209mg) (12x12x1.41mm) DRAM (57 nm node) {9ec8fcae-f02b-4677-ae47-465821ae59f0} IC WLP CSP 425 (4.78g) (19x19x1.5mm) flash (45 nm node) {4fd22e2a-dfa8-4847-a7aa-acb28865b08b} IC WLP CSP 425 (4.78g) (19x19x1.5mm) MPU ge-neric (130 nm node) {04e68adf-9eeb-4cf3-9535-5486099f8b88} IC WLP CSP 425 (4.78g) (19x19x1.5mm) CMOS logic (22 nm node) {bff2c40a-e6e4-4ef7-b5ec-a8008ae33935} IC WLP CSP 196 (209mg) (12x12x1.41mm) MPU ge-neric (130 nm node) {23ae4ee6-4fcb-4fb7-bf5b-4d4a53f367c0}

GC-9257 Bug Flow correc-tion IC WLP CSP 196 (209mg) (12x12x1.41mm)

The amount of materials in the IC WLP CSP 196 (209mg) (12x12x1.41mm) flows are now correct, so that all individual (E) quanti-ties add up to the value in Mass and Electronics un-specified. The corrections involved adding in material quantities from what is clas-sified as "Laminate" in the material declaration.


2.13 Inventories for renewable processes

The datasets, including renewable materials (e.g., crop cultivation), are modelled with a comprehensive agricultural model. The model considers local and regional aspects of climate, soil and farming practices on the technical side. In addition, it considers international guidelines, current scientific literature and

39

available databases on the methodological side. The Sphera agriculture and farming experts maintain and enlarge the model frequently, making it one of the most advanced LCA models related to this topic.

As part of the 2020 annual upgrade, the agrarian and renewable processing datasets have been reviewed and updated based on the most recent information identified by the Sphera experts, considering the as-pects 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 renew-able materials are involved, especially in the cases when 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 was applied.

Table 2- 6: JIRA issues for renewable processes


Issue Cate-gory


GC-7672 Bug Transport of Sheep wool

Now the correct amount of cargo transported is taken into consideration. Please note, the dataset is a Version 2019 dataset and will not be further updated.

As both transport processes now have the same amount of cargo, the impact increased slightly.

Extension data-base XVI: seat covers

GC-7846 Improvement Heavy metals to soil emis-sions in sludge agri-cultural appli-cation

Heavy metal emissions to soil from sewage sludge that is used for agricultural fertilizer is now included in the datasets.

Impacts rise and are now con-sistent with comparable sludge composting processes when look-ing at Toxicity, as formerly this im-pact was not existent or negative due to fertilizer credit.

Several

GC-8265 New New corn wet mill pro-cesses in Food and Feed DB

Aggregated corn wet mill processes are now available in the Extension database XX: Food&Feed. All corn wet mill partly ag-gregated processes were re-named to their products and not the production pro-cess, they now have the open input included in their name.

New dataset Extension data-base XX: food & feed

GC-8331 Improvement Harmonize water con-tent for CN: Corn grain cultivation

Chinese corn grain cultiva-tion is used as an upstream to "CN: Glucose syrup from corn, 68% H2O content". In order to be consistent with the US: Glucose syrup from corn, the water content of the CN: corn was changed from 35% to 20%.

Lower impacts in the core impact categories like AP, EP, climate change. This is due to the lower energy input for the drying process as well as reduced transport amounts (less water must be transported).

Extension data-base XX: food & feed

GC-8357 Improvement Update Euro-pean cultiva-tion produc-tion mix plans

The country share of the EU production and consump-tion mixes plans were taken from FAO and production mixes were adjusted ac-cordingly: EU-28 Corn grains cultiva-tion - production mix, at pro-ducer EU-28 Soybean cultivation - production mix, at producer EU-28 Sugar beet

No major changes except for soy-bean, however this is due to adapt-ing the functional unit to 1 kg

Extension data-base XX: food & feed

40


Issue Cate-gory


cultivation - production mix, at producer EU-28 Winter rape cultiva-tion - production mix, at pro-ducer EU-28 Winter wheat cultiva-tion - production mix, at pro-ducer.

GC-8597 Improvement Harmonized use of cellu-lose

Chemical treated cellulose is now used instead of kraft-liner where applicable.

As the environmental impact of the cellulose process is higher than the one of the Kraftliner, the over-all impact of the affected pro-cesses also is higher. The range is from 1% to over 1000% depending on the dataset and the impact cat-egory.

Several

GC-8693 Improvement Too high ash amount in DDGS flow

The ash content of the DDGS flow (Dried Distillers Grains with Solubles) was harmonized. Most of the other material properties were adapted accordingly.

Carbon content increases a little bit in the flow, and this leads to changes in all processes where the flow is used. Especially acidifi-cation, POCP and climate change were affected.

Several

GC-8733 Bug Harmonize blue water consumption of BR: Euca-lyptus planta-tion

Water usage for eucalyptus plantation was adapted. This affects mainly the elec-tricity from biomass da-tasets.

As the irrigation water contains ni-trate, the N-balance is changed and mainly eutrophication in-creases (plus 145%). Water con-sumption decreases.

Several

GC-8764 Improvement Update of sheep wool yarn

DE: PET/wool 70/30 fabric and DE: PET/wool 70/30 non-woven fabric datasets were updated with Euro-pean version of the sheep wool, as sheep wool from NZ is a Version 2019 da-taset and will not be further updated.

Due to detailed and improved sheep husbandry plan in the back-ground, the LCIA results changed in most impact categories. EP and GWP decrease between 50% to 80%, AP increases be-tween 20% and 30%

Extension data-base XVI: seat covers

GC-8808 Improvement Added transport to Ethylene (Ethene) (bio based from BR sugar cane)

A transport that accounts for the transportation of the Brazilian bioethanol to the destination country is now accounted for. As ethylene is used as precursor also for bioplastics, changes will be seen in those.

Slight increase in all categories Several

GC-8814 Bug Primary en-ergy from so-lar to biogas from landfill

Primary energy from solar is now included in the biogas from landfill dataset

Primary energy from renewable re-sources [MJ] increases for the countries that use biogas from landfill within their bioenergy: DE: + 13% GB:+ 296% NL:+ 23%

Several

GC-8822 Bug Negative acidification due to nega-tive amount of NH3 emis-sions

Parameters in the agrarian models for IT: Carrots tur-nip, at field (87% H2O con-tent) and US: Poplar (field border) (50% H2O content) were harmonized and now give correct values.

AP and EP is now positive Extension data-base XX: food & feed


Documenta-tion for DE: Rape seed datasets

The datasets for DE: Rape seeds at field and farm bor-der now have a table show-ing values of the cultivation

Does not change the results Extension data-base XX: food & feed

41


Issue Cate-gory



Documenta-tion for CN: Glucose syrup from corn, 68% H2O content

The documentation is adapted in order to show that corn as biomass input with a water content of 20% is used.

Does not change the results Extension data-base XX: food & feed

2.14 Inventories for transport processes

In this year’s upgrade, harmonization for transport datasets used throughout the database was improved.

Table 2- 7: JIRA issues for transport processes


Issue Cate-gory


GC-7567 Improvement Harmoniza-tion of up-dated ship settings in datasets

The utilization rate of the ships is now harmonized for all datasets using ship transport.

Where only container ships are used: All categories: -39% Otherwise impact changes are much lower to insignificant.

Professional database

GC-8254 Bug Update "Mass-in-duced fuel consumption of automo-tive part (NEDC)"

Following emission factors were applied specific to the fuel chosen in the dataset to be consistent to our transportation datasets: Diesel: 3.161 kg CO2 / per kg fuel Gasoline: 3.137 kg CO2 / per kg fuel Former emission factor was 3.124 for diesel and gaso-line.

Slight rise of GWP (about 1%). Extension data-base XVI: seat covers

GC-8325 Improvement Update emis-sions factor for trains

Based on literature source, emission factors for NMVOC, NOx, PM and CO were updated. Additionally a new free pa-rameter was introduced to set different emission standards for locomotives.

For the default Global average: GWP: insignificant POCP: -6% All toxicity quantities: -18%


GC-8328 Improvement Replace pre-vious ver-sions of transport with current ones

Previous versions of transport datasets were re-placed by current ones.

Generally, the impacts decrease to a smaller degree

All

GC-8891 Bug Scaling of railway tracks

The dataset DE: Railway track {96CC07D8-87C4-4A7E-BD71-4767ADBCE2B7} had a scaling which was a factor 73000 too high.

Very large decrease occurs. In av-erage the decrease is a factor 1E5 for every impact category. As the dataset is not used in background of other GaBi datasets, no other datasets are affected.



Documenta-tion: different DWT for ships

A table with the most com-mon DWT of ships was added to the documenta-tion of ship transport da-tasets.

Does not change the results Professional database

42

2.15 Inventories for construction processes

Foreground data and models were checked by Sphera construction experts regarding technological up-grades and passed. Identified technology improvements were updated in the database. In total, 5 new EPDs datasets have been included in the extension database XIV: construction materials.

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

DE Lucofin 1411ECO/1455 ECO - Lucobit AG (A1-A3) Lucobit AG {ca2ecb5b-9216-4619-a4d4-48d1f5467dad}

DE Lucobit 1233 - Lucobit AG (A1-A3) Lucobit AG {37d271d5-3095-46a2-9d8d-53cf778c6121}

DE Lucobit 1210A - Lucobit AG (A1-A3) Lucobit AG {e64a92b0-4013-47af-a8da-f065499532eb}

DE Lucofin 1410M, 1411M, 1412M, 1455M - Lucobit AG (A1-A3)

Lucobit AG {274b6063-7b79-48e0-9e19-3e49c516d91d}

DE Lucobit 1235 - Lucobit AG (A1-A3) Lucobit AG {420fa530-2166-4d0d-a368-010a0966786f}

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 2019 and 2020 for construction.

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

Table 2- 8: JIRA issues for construction processes


Issue Cate-gory



Country spe-cific natural stone slabs

Natural stone slabs are now modelled more country specific. For Brazil and China now only truck transport is considered. Electricity for cutting the stones is now country specific.

Impacts decrease by 50% to 60% in all categories

Professional data-base Extension data-base XIV: con-struction materi-als

GC-7670 Editorial Spelling of Vac-uum Insulation Panel dataset

The dataset 'Vacuum-Insulation-Panel (unlaminated) (A1-A3)' was misspelled and now reads 'Insulation' instead of 'Isolation'.

Does not change the results Extension data-base XIV: con-struction materi-als

GC-8157 Bug CO2 correction for UA: strip parquet

UA: Strip parquet now has a cor-rect CO2 balance.

Changes “GWP incl. “Biogenic” by plus 120%, the dataset now re-flects the carbon uptake.

Extension data-base XIV: con-struction materi-als


Reference year of VDZ from 2009 to 2015

Reference source and link up-dated, and now points to correct 2015 report.

Does not change the results Professional data-base Extension data-base XIV: con-struction materi-als

43


Issue Cate-gory



Slag tap granu-late documen-tation

Documentation of DE: Slag-tap granulate (EN15804 A1-A3) {a9725df3-6c69-40fb-ba96-065fbef96001} now describes why dataset has no impact, as well as the need for the user to model the transport.

Does not change the results Extension data-base XIV: con-struction materi-als


Update EU-28: Polyisocyanu-rate (PIR high-density foam)

Precursor for PIR was updated. GWP decreases by 8%, AP by 11% and EP by 23%

Professional data-base


End of Life bath- and shower tub enamel

The material credited for enamel bathtub was changed from stainless steel to carbon steel.

This reduces the credits given by a factor of about 70%.



EPDM seal for Aluminium pro-file - split and correct plan be-tween A1-A3 and EoL

EPDM seal for Aluminium profile updated and corrected in pro-duction and EoL. EoL modules C4 and D were newly created to have the refer-ence flow in the input - the for-mer ones put to outdated which had the reference flow in the output.

Production data (A1-A3) decrease due to updated transport and electricity by -3% (GWP) up to -30% (AP) EoL dataset C4 and D huge changes since Landfill was re-placed by Incineration, e.g.: C4: +190% GWP; -100% EP D: about 30 times higher credit all over



DE: Polyure-thane rigid foam (PUR) (EN15804 A1-A3) dataset

DE: Polyurethane rigid foam (PUR) (EN15804 A1-A3) dataset is updated. Flame retardant and water input added. CO2 emis-sions from foaming reaction with water is added.

All categories increase between 5 - 10% Water consumption increases by 20%



Documentation for concrete data sets

Documentation of concrete da-tasets now mention the cement type used, as well as the clinker content of the cement.

Does not change the results Professional data-base Extension data-base XIV: con-struction materi-als

GC-9219 Bug Peat resource flows in EN15804+A1 ADP fossil quantity

Characterization factors of the 4 peat flows were checked in the original documents. Also, this topic and way forward was dis-cussed with Wo. Characterization factors were adjusted according to the EN 15804 documentation.

Characterization factor for flow Peat ecoinvent {9905abb6-9879-47ad-a9ab-78dc1166e089} was corrected from 8.7 to 8.4 accord-ing to EN 15804 resource use

All

GC-9230 Bug Correction of EN15804 Cli-mate Change (biogenic)

The characterization factor of EN15804 Climate Change (bio-genic) {6C1F6766-0995-45B9-B5A4-213A32421020} for the flow Carbon dioxide [Re-newable resources] {6DD366CE-2F08-408B-99BF-3C5860B67600} is now "1" instead of "-1".

Will change the result for this spe-cific LCIA Sub indicator substan-tially when relevant amounts of bi-ogenic CO2 are used in a process, e.g. for biobased products. The overall EN 15804 GWP results are unchanged.

All

44


Issue Cate-gory



Electricity cred-ited for produc-tion residues of laminated tim-ber

In the production process of three-layer and five-layer lami-nated timber, wood production residues are thermally recycled. The electricity credited was changed from the standard electricity grid mix to an electric-ity from biomass to reflect that wood is incinerated.

GWP incl. biogenic decreases by 10%. GWP excl. biogenic in-creases by about 80%, ADPf by 49%, POCP decreases by 26%, ODP increases by 1000%. Since the grid mix was a credit (with standard grid mix), this is explain-able, as -> less emissions from nuclear energy as credit -> im-pacts rise.


2.16 Inventories for US regional processes

The datasets in the US extension database were checked by Sphera experts for their technological validity and have passed.

10 datasets were added to the Extension database XVII: full US.

Table 2- 9: JIRA issues for US regional processes


Issue Cate-gory


GC-1117 New Secondary

LDPE and

HDPE datasets

for US

Two new datasets are now avail-

able in the Extension database

XVII: full US:

US: Polyethylene low density

granulate (LDPE/PE-LD) second-

ary

US: Polyethylene high density

granulate (HDPE/PE-HD) sec-

ondary.

New dataset Professional da-

tabase

Extension data-

base XVII: full US

GC-7663 Improve-

ment

US water input

flow for CPA

corrugate and

containerboard

datasets

In both processes, the flow "Wa-

ter, turbine use, unspecified nat-

ural origin" has been replaced

with the flow "Fresh water to tur-

bine, regionalized, CA".

Very strong decrease in all water

impact categories, as the previous

flow incorrectly counted in the wa-

ter categories. For blue water con-

sumption, containerboard de-

creases from 1190kg to 3.8 kg.

Professional da-

tabase

Extension data-

base XVII: full US

GC-7689 Editorial Naming of

USLCI plans

and processes

Plan and processes names have

been updated and now have re-

gion or country code in the

name.

Does not change the results Extension data-

base XVIII: NREL

USLCI integrated

GC-8027 Bug Harmonization

of transport

value for USLCI

dataset

The transport distance for RNA:

Fertilizer, stover, 2022 now

uses the correct conversion fac-

tor from tkm to kgkm. Transport

Impacts will change only negligible

for the plan RNA: Fertilizer, stover,

2022.

Extension data-

base XVIII: NREL

USLCI integrated

45


Issue Cate-gory


distance increased by a factor

of 1000.

GC-8036 Improve-

ment

Scaling factor

set in the

USLCI Plans

Some processes on USLCI plans

were not fixed with a scaling fac-

tor, this has now been changed.

No changes in values, only pro-

cesses on plans set to a fixed

scaling.

Extension data-

base XVIII: NREL

USLCI integrated

GC-8264 Editorial Harmonize

naming of PET

datasets

Renaming of “US PET resin” to

“US PET bottle grade granulate”.

Renaming of biobased PET pro-

cesses including bottle grade in

the process name.

Renaming of “US Recycling of

plastic” processes to “Plastic

granulate secondary”.


base XVII: full US

GC-8637 New Asphalt binder

dataset for

North America

Four new datasets for RNA as-

phalt binders are now available

in the Professional database:

1. Asphalt binder, 3.5% styrene-

butadiene-styrene (SBS)

2. Asphalt binder, no additives

3. Asphalt binder, 0.5% poly-

phosphoric acid (PPA)

4. Asphalt binder, 8% ground

rubber tire (GRT)


tabase

GC-8697 Improve-

ment

Naming: in-

clude abbrevia-

tion "PET" in

dataset name

The US PET dataset {043FC939-

8EFF-409B-AC6B-

7609312AB447} is now called

"Polyethylene terephthalate bot-

tle grade granulate (PET) via

PTA".


base XVII: full US

GC-8825 Bug USLCI Palm

kernel oil cor-

rection

The transport distances in the

dataset RNA: Palm kernel oil,

processed, at plant {f7e77753-

a6f5-4b8a-a0fc-baa7df95646a}

was corrected based on LCI

available at https://www.lcac-

ommons.gov/lca-

For the plan “RNA: Palm kernel

oil, processed, at plant”, the im-

pacts will slightly increase

Extension data-

base XVIII: NREL

USLCI integrated

46


Issue Cate-gory


collaboration/National_Renewa-

ble_Energy_Labora-

tory/USLCI/dataset/PRO-

CESS/21ed2ebb-54bf-324d-

9865-af8265d2d49c.

GC-8885 Documenta-

tion

Documenta-

tion: US EPA re-

gional

wastewater

treatment

All 30 US EPA municipal waste

water data sets now have cor-

rect region and EPA region map

in documentation.

Change made - a) geographical

representativeness field was up-

dated to mention the appropri-

ate EPA region number (as per

the dataset's name)

b) a US map labelled based on

EPA regions was added to im-

ages.

Does not change the results Professional da-

tabase

Extension data-

base XVII: full US

GC-9128 Bug US trucks (VIUS

& Smartway) -

emission fac-

tors correction

Emission factors now only con-

sider the fuel type diesel, as op-

posed to a sum of all fuel types.

AP and EP values show the main

reduction, as nitrogen dioxide and

nitrogen monoxide were changed

the most (in terms of emission

factor value)

GWP: negligible impact change

AP: 0 - 59% reduction across the

40 datasets

EP: 0 - 59% reduction across the

40 datasets

Professional da-

tabase

Extension data-

base XVII: full US

GC-9326 New Two new North

American cop-

per pipe and

sheet datasets

Two datasets from the Copper

Development Association (CDA)

are now available in the Profes-

sional database:

RNA: Copper sheet

RNA: Copper tube


tabase

Extension data-

base XVII: full US

GC-9402 Documenta-

tion

Documenta-

tion. US ABS

reference for

wastewater

treatment plant

Does not change the results Professional da-

tabase

Extension data-

base XVII: full US

47

2.17 Inventories for India regional processes

52 datasets have been added to the Extension database XXI: India and 60 to the separately available Indian database 2020.

A variety of datasets are now available, among others six new regional electricity mixes for different parts of India (East, North, North East, South, West and NPP), two fertilizers, different landfills, organic and inor-ganic intermediate products, several plastics and some construction materials.

Table 2- 10: JIRA issues for India regional processes


Issue Cate-gory


GC-7671 Improve-

ment

Steel screw in

Plaster board

Steel screw removed, as they

were part of the installation

which was not in the scope of

this model.

Negligible impact changes in all

categories: < 1%

Indian database

Extension data-

base XXI: India

GC-8253 Bug Indian Alumin-

ium ingot

The electricity consumption of

the electrolysis (prebaked) was

corrected (was factor 100 too

low). This also affects all prod-

ucts using aluminium, such as

sheets or profiles

Impact categories increase

Acidification Potential (AP) [kg SO2

eq.] +160% to +190%%

Eutrophication Potential (EP) [kg

Phosphate eq.] +150% to +190%

Global Warming Potential (GWP

100 years), excel biogenic carbon

[kg CO2 eq.] +120% to +150%

Indian database

Extension data-

base XXI: India

48

3 Industry data in GaBi

Even though 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 updated by Sphera in the annual upgrade without permission. Sphera must keep these processes identical to those in the GaBi Databases 2019 Edition until the associations decide to update and make them available. However, sev-eral additional associations now use the GaBi Databases to reach global customers.

New industry data added in GaBi Databases 2020 Edition:

From AI (Asphalt Institute)

(http://www.asphaltinstitute.org)

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

RNA Asphalt binder, 3.5% styrene-butadiene-styrene (SBS) {bbf3dc45-a370-405e-8996-7358b7f1841c}

RNA Asphalt binder, no additives {f8ce5199-1666-452b-a267-8797c94d0560}

RNA Asphalt binder, 0.5% polyphosphoric acid (PPA) {28d7d56e-a751-492c-9bb1-08f19ea4ac37}

RNA Asphalt binder, 8% ground rubber tire (GRT) {72d5a381-8cae-4e1d-b0a3-26cc43b69867}

From CDA (Copper Development Association Inc.)

(https://www.copper.org)


RNA Copper sheet {430f04d0-1419-42fd-9930-fba3b5348829}

RNA Copper tube {7426e30c-499a-4663-b2c8-34dce3085ca5}

From ZIA (Zircon Industry Association)

(https://www.zircon-association.org)


GLO Zircon sand mix {35f08b23-292d-48ee-924e-3d43019e3732}

http://www.asphaltinstitute.org/

https://www.copper.org/

https://www.zircon-association.org/

49

4 General continuous improvements

4.1 Editorial JIRA Track-ing Num-ber

Issue Cate-gory


GC-7360 Improvement New icon for flows for "limited use"

A new icon now highlights flows which are categorized as "limited use only". Please see the docu-ment "Modelling Principles 2020" for further information on this topic.


All

GC-7844 Documentation Documentation of unit processes

Unit processes now have an adapted documentation, reflecting that it is not a cradle-to-gate da-taset and that no allocation and background data is used.


Several

GC-9334 GC-9287

Improvement Naming of partly aggregated (p-agg) processes with open product flow(s)

Processes were named more clearly by adding " - open in-put [name of upstream material] " in the process name.


Several

4.2 LCIA Methods, Normalization and Weighting factors

In this chapter, JIRA issues for LCIA and Normalization and Weighting are listed. The relevance depends on the use by users; most of the affected flows contribute little to GaBi datasets.


Issue Cate-gory


GC-8476 Improvement Combine flows "Dust (combus-tion) and "Dust PM10"

The flow "Dust (combustion)" was merged into the flow "Dust (PM10)" and is now not used anymore.

Impacts increase in processes that pre-viously used the flow “Dust (combus-tion)”

All

GC-8478 Bug Harmonize charac-terization factors for "Pit gas"

The flow "Pit gas in MJ" now has coherent characterization factors as "Pit gas in kg". The characteriza-tion factors were adjusted to fit 1 MJ pit gas.

If the flow Pit gas in MJ is used, the impacts will in-crease, in propor-tion.

All

GC-8497 Bug Characterization factor for Nitrogen (N-compounds) in EF2.0 Eutrophica-tion marine

The characterization factor for Ni-trogen (N-compounds) in EF2.0 Eu-trophication marine has been set to zero to align with the method as published.

Only in single cases the impact will de-crease

All

GC-8724 Bug Harmonization of characterization factor of biotic me-thane for ISO 14067 GWP and IPCC AR5 GWP

The GWP characterization factors for biotic methane are now the same for ISO14067 and for IPCC AR5, it was changed from 28 to 30 kgCO2-eq. in ISO14067.

Increases the im-pact of GWP incl. Bi-ogenic by about 5%

All

GC-8881 Bug Weighting for GWP in EF2.0

GWP is now weighted only with the subcategories in EF2.0.

If weighting was used, impacts will decrease

All

GC-8882 Bug Unit for Ionizing ra-diation in ReCiPe

The unit for Ionizing radiation is now kBq C-60 eq. to air, instead of Bq C-60 eq. to air.


All

50


Issue Cate-gory


The calculations and conversions remain correct.

GC-8895 New New AWARE 1.2c method

Four new AWARE 1.2c quantities are now available.

New dataset All

GC-8974 Bug Characterization factors for Me-thane (biotic) in EF 3.0

The flow methane (biotic) {4D97DE87-E1DF-4B8A-871B-8A4CDF38359D} now has the cor-rect EF 3.0 characterization factors for Ecotoxicity freshwater (Organic), Ecotoxicity freshwater, Non-cancer human health effects (Organic) and Non-cancer human health ef-fects.

Impacts for those categories will slightly increase

All

GC-8979 Improvement Characterization factor for Methane in EF 3.0

The flow Methane {2A531B07-ACE1-4103-82E5-701DF69686F6} has the charac-terization factor for "EF 3.0 Non-cancer human health effects" re-moved, as this was not listed in the official tables.

Impacts will slightly decrease

All

GC-9223 Improvement Flow type change of radioactive waste flows to "Output"

The flow type of the 4 radioactive waste flows: High radioactive waste [Radioac-tive waste] Low radioactive wastes [Radioac-tive waste] Medium radioactive wastes [Radio-active waste] Radioactive tailings [Radioactive waste] now have the flow type "Output" in-stead of "None".


All

GC-9310 Improvement ReCiPe 1.08 water depletion factors for water scarcity flows

Water flows for different water scarcity classes are now character-ized in ReCiPe 1.08 water deple-tion for River, ground and lake wa-ter (everything not going through a turbine) * extreme scarcity * high scarcity * low scarcity * medium scarcity * moderate scarcity * OECD average scarcity.

If those flows are used to a relevant extent, the impacts will relevantly in-crease

All

GC-9345 Improvement Characterization of mixed ore flows in EF 3.0

When exporting a dataset with ILCD, flows with mixed ore content are mapped to the individual ore flows. In order to get consistent re-sult calculations with an exported dataset and within GaBi, flows with mixed ore content are now charac-terized according to their ore con-tent.

Impacts will in-crease when these ore flows were ex-tensively used and when looking at ADPe in EF 3.0

All

GC-9346 Bug EF 3.0 factors to Phosphorus-pent-oxide flows

Phosphorus-pent-oxide [Inorganic emissions to air] and Phosphorus-pent-oxide [Inorganic emissions to sea water] now have the correct factors for EF 3.0.

When looking at a balance view in GaBi, impacts will increase when these ore flows are used. For an ILCD

All

51


Issue Cate-gory


Ecotoxicity freshwater (Inorganic) & EF 3.0 Ecotoxicity freshwater.

export no impact change occurs, as flow-mapping is un-changed.

GC-9429 Improvement EN 15804+A1 and EN 15804+A2 quantities update

The quantities EN15804+A1 and +A2 are now sorted in the order as required by an EPD (including num-bering and folders). En15804+A2 now also has the additional LCI quantities as already present in +A1. The quantities PERM and PENRM now only have the generic proxy flow quantified.


All

GC-9497 Bug C_total_wt value of flow "Tall oil (raw product)"

The flow "Tall oil (raw product) {357612AD-48BB-433F-A6A8-1711CE01BDCF} now has the cor-rect C_total_wt value. It is now the same as the value for C_bio-gen_wt: 0.79.

Only changes val-ues if c_total_wt was used for calcu-lations

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 detailed.


Issue Cate-gory


GC-8280 New New DE: Electricity grid mix 2018 da-taset

A new DE: Electricity grid mix 2018 dataset is now available in the ex-tension database II: energy.

New dataset Extension db II energy

GC-8441 Bug Primary Energy Demand for en-ergy from biogas

In biogas from landfill, renewable primary energy was added. Af-fected datasets are electricity from biogas, steam from biogas.

+43% primary en-ergy total for biogas for bioenergy DE, +6685% primary energy total for bio-gas for bioenergy GB +100% for primary energy total for bio-gas for bioenergy NL

Several

GC-8443 Bug EU-28: Electricity from peat correc-tion

A bug in the dataset was corrected: the share of electricity from peat is now 100% instead of 1%.

Since the share in-creases by almost factor 100, the overall results in-crease significantly, e.g. GWP from 0.01 kg CO2-eq. to 1.2 kg CO2-eq. As the share of electricity from peat in the grid mix is very little, this has no relevant impact on the over-all grid mix, how-ever.

Professional database Extension data-base II: energy

52


Issue Cate-gory


GC-8512 Improvement Update of nuclear energy production

The nuclear energy production was updated (nuclear recycling pro-cess, Russian enrichment process and creation of the MOX produc-tion process).

Almost no changes in potential impact, only ODP increases by 10 to 20%

Several

GC-8770 Improvement Updated Geother-mal infrastructure

Infrastructure for geothermal plants (infrastructure) was up-dated.

GWP varies be-tween 9% to 14%, in EP these changes can be found be-tween 59% to 104%

Several

GC-8848 New New "electricity from XX" datasets for production mixes

Three new datasets are now avail-able: - ZA: Electricity from solar thermal (in Extension database II: energy) - HU: Electricity from hard coal (In Professional Db) - JP: Electricity from biogas (In Pro-fessional Db).

New dataset Professional database Extension data-base II: energy

53

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, Eurostat: Energy Database - Production of electricity and derived heat by type of fuel [nrg_bal_peh], Luxembourg, 2019

IEA, International Energy Agency Data services: World Energy Balances, World Energy Statistics, Electricity Information (2019 edition), Paris, 2019

Sphera, “GaBi Database & Modelling Principles”, 2020

Sphera, “Introduction to Water Assessment in GaBi”, 2020

Sphera, “The GaBi refinery model”, 2020

Sphera, “The Agricultural LCA Model Documentation”, 2020

Sphera, “Land Use Change Emissions in GaBi Documentation”, 2020

Sphera, “Documentation of land use inventory in GaBi”, 2020

Sphera, “Documentation for Passenger Vehicle Processes”, 2020

Sphera, “Documentation for Duty Vehicle Processes”, 2020

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

54

Annex I: “Version 2019” discontinued datasets – Explanations and Recommendations

For various reasons, there are a few processes in the Databases 2020 Edition that are no longer appropriate. These have been moved into a folder

called “Version 2019.” 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) Sphera is committed not to provide information that is not up-to-date, and, at the same time,

ii) Sphera 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.

Please note, processes that will no longer be updated (in the Version 2019 folder), as well as flows with limited use are now marked with a separate

icon in the database:

55

Version 2019 processes Alternative process to be used instead

Country Process name Type Source Process GUID (can be en-tered in the search tool) Country Process Name Source GUID

RNA Alumina production p-agg AA {98ba3733-fa5f-4cc7-bfe5-76c317f7ea70}

If relevant, please contact data on demand from Sphera for alternative processes

RER Ammonia agg PlasticsEurope {0dd5b338-5b1a-4c81-bc58-b4b1cd67c554}

EU-28 Ammonia (NH3) Fertilizers Europe {F1C2E4CD-0D6C-40D2-A518-C0BF1CE39CE5}

RNA Bauxite agg AA {50e47acc-8694-4b84-8876-15d8ce3d4462}


RNA Casting (aluminum) p-agg AA {e806b139-fa4c-4995-9816-4b4d6b73612d}


EU-28 Chimney stainless steel single-wall (EN15804 C3-C4) (RETIRED)

agg ts {a7fa3a1e-523a-4273-b24d-29951d107928}

EU-28 Chimney stainless steel single-wall (EN15804 C3-C4)

ts {A79AC378-6EA7-4354-AFD8-AD02CF159BA9}

RER Crude oil agg PlasticsEurope {2bd5eee0-aecd-4ce0-87c3-9c0de9a55039}


US Electricity from biomass (solid) (Alaska)

agg ts {44317200-fd01-4862-98ea-bfd0bf99a0f0}

No new dataset, because no electricity from biomass produced anymore in Alaska

LV Electricity from heavy fuel oil (HFO)

agg ts {b7b80885-f86e-4a90-80b0-b7a1fe93aec0}

No new dataset, because no electricity from HFO produced anymore in Latvia

US Electricity from natural gas (Hawaii)

agg ts {9765ceef-c88c-41c3-9f70-84e1b82c8d8f}

No new dataset, because no electricity from natural gas produced anymore in Hawaii

ID Electricity from waste agg ts {a1d09cf0-ac72-425d-833a-c29168ba0a8a}

No new dataset, because no electricity from waste produced anymore in Indonesia

US Electricity from waste (Texas)

agg ts {b74e937e-08e7-41c2-b270-28784eecb4e2}

No new dataset, because no electricity from waste produced anymore in Texas

RNA Electrolysis (Aluminum) p-agg AA {4014bad4-7825-4a6a-b003-3c5090cb6092}


DE EPDM seals for aluminium section (EN15804 C4) (RETIRED)

agg ts {39563f1f-2bbb-47e2-8410-11e38f194f78}

DE EPDM seals for aluminium sec-tion (EN15804 C3)

ts {FE3A3D81-5673-4AD0-86CF-84DB129764D7}

DE EPDM seals for aluminium section (EN15804 D) (RE-TIRED)

agg ts {f148b20e-55ba-4767-9a71-712104d1cb0c}

DE EPDM seals for aluminium sec-tion (EN15804 D)

ts {11954CDB-60FE-4696-9284-5C9D4BA8309D}

DE EPDM seals for aluminium section, thermally sepa-rated (EN15804 C4) (RE-TIRED)

agg ts {fcdd0854-754e-47d5-acae-11c6a65e343c}

DE EPDM seals for aluminium sec-tion, thermally separated (EN15804 C3)

ts {6D6D7A08-A139-4368-824F-6303BB0138C7}

DE EPDM seals for aluminium section, thermally sepa-rated (EN15804 D) (RE-TIRED)

agg ts {c8de255d-35cb-49c9-b693-450fd9c90fc1}

DE EPDM seals for aluminium sec-tion, thermally separated (EN15804 D)

ts {4C327C94-05AB-4461-BD52-29ECF2AF8D1F}

US Hexamethylenediamine (HMDA; from adipic acid via adiponitrile)

agg ts {20853ba6-d754-4457-9a43-cbc4f1a32a5d}

US Hexamethylenediamine (HMDA; from butadiene via adiponitrile)

ts {8DD5A55E-3F80-4DE8-8E22-F335CACD607C}

GB Hexamethylenediamine (HMDA; from adipic acid via adiponitrile)

agg ts {4c8831b6-3deb-4da0-8aab-1b3e2e063fdd}

GB Hexamethylenediamine (HMDA; from butadiene via adiponitrile)

ts {C47F69CD-3DC9-4DDE-AC17-10D79B42C5FA}

DE Hexamethylenediamine (HMDA; via adipic acid)

agg ts {fef2d2d3-299d-461f-8ddc-161e59460b8d}

DE Hexamethylenediamine (HMDA; from butadiene via adiponitrile)

ts {2EA1DE34-DFC9-4F19-9D84-441C158BA627}

56



Hexamethylenediamine (HMDA; from acrylonitrile via adiponi-trile)

{DB0D7B4D-8F62-43D0-B6F1-AFE37B28D595}

IN Hexamethylenediamine (HMDA; via adipic acid)

agg ts {fc71b63a-bb43-4ab0-84e7-ec62037b739f}

IN Hexamethylene diamine (HMDA; from butadiene via adiponitrile)

ts {E98888A7-0722-4531-A1F6-08B3F676B308}

RER Hydrogen (cracker) agg PlasticsEurope {0eb8722e-09c7-4ab9-8bf7-2e9636694a7a}


RER Hydrogen (electrolysis) agg PlasticsEurope {0f8a60c0-6b26-4560-992e-0a3a4772ddf9}


RER Hydrogen (steam reform-ing from natural gas)

agg PlasticsEurope {779b3c8a-3a90-4e62-b06c-8aae323b6f39}


RER Hydrogen cyanide (prussic acid)

agg PlasticsEurope {26f67654-ff1e-4a01-9d75-7c2f5e3a874a}


RER Naphtha agg PlasticsEurope {d1fbc2f7-a420-40af-98bc-42c16ec21948}

EU-28 Naphtha at refinery ts {34613A4C-0E48-41B9-AA04-94C6F25E235A}

RER Natural gas agg PlasticsEurope {26446743-1022-4a58-91e9-c98c0e67c633}

EU-28 Natural gas mix ts {C6387E19-933F-4726-A7AD-7A8050AA418C}

DE Nylon salt 63% solution, AH salt (HMDA from adipic acid)

agg ts {37bb3872-70da-4503-a122-c480a1276a15}

DE Nylon salt 63% solution, AH salt (HMDA from acrylonitrile)

ts {5FD868C8-B851-400C-B564-23DFA3D6302F}

MY Palm kernel oil, refined (incl. LUC as fossil CO2)

agg ts {52cfe283-9d50-40a3-ad4b-415f8618b776}

MY Palm kernel oil, refined ts {237FA381-3230-4487-A2B5-7CA42414786A}

MY Palm oil, refined (incl. LUC as fossil CO2)

agg ts {5e668170-75f1-4462-9886-9fa93a201b15}

MY Palm oil, refined ts {BF74C24A-7D7E-46E5-9D3D-9252F569AA1B}

RER Pentane agg PlasticsEurope {d03d6dd2-31db-48b6-8d23-5133fd470b14}


RER Phenol agg PlasticsEurope {42be48ab-ad47-41b3-922f-e403638575d4}

EU-28 Phenol ts {96E90BA0-518F-4611-B0C2-4494A2D7224F}

DE Polyamide 6.6 Granulate (PA 6.6) (HMDA from adipic acid)

agg ts {59a9a11c-2368-465b-8ca5-cdd5df2ccd95}

DE Polyamide 6.6 granulate (PA 6.6) (HMDA from butadiene) Polyamide 6.6 granulate (PA 6.6) (HMDA from acrylonitrile)

ts {F011F43A-9018-4B98-A34E-CD4E711F4928} {5927ADEA-71FF-442C-9468-9CBC05748253}

RER Polyethylene tereph-thalate, granulate, bottle grade, at plant

agg PlasticsEurope {84854d79-77da-4794-9d2a-f108f7e91741}

EU-28 Polyethylene terephthalate bot-tle grade granulate (PET) via PTA

ts {4B2420B3-8F56-45F1-984D-173A9298EF4A}

DE Sheep wool yarn (from New Zealand (NZ) sheep wool)

agg ts {e5ed0c0c-9e44-4a6a-8eb4-ff71c197576b}

DE Sheep wool yarn (from Europe (EU) sheep wool)

ts {7BBA263B-69CE-4C8A-97A8-31F8046A95E2}

BR Soybean at field border (13% H2O content) (incl. LUC as fossil CO2)

agg ts {4cee6dd6-eaf1-4dc4-ad56-40075434f248}

BR Soybean at field border (13% H2O content)

ts {6837E0FB-D270-43E6-B82D-6CE3181CA65E}

BR Soybean meal (incl. LUC as fossil CO2) (wet mill) (economic allocation)

agg ts {ac082153-95e1-4b01-8ca4-96b10055b96d}

BR Soybean meal (wet mill) (eco-nomic allocation)

ts {3D4C5361-C011-414D-A27A-C4FCE8458095}

BR Soybean oil (incl. LUC as fossil CO2) (wet mill) (eco-nomic allocation)

agg ts {f81abba4-f753-440f-94d6-ea9bb1709097}

BR Soybean oil (wet mill) (economic allocation)

ts {ECF054AA-6E75-44A4-A95A-468B8F08AF94}

57



RER Steam (mp) agg PlasticsEurope {06d4bd44-9247-44a5-bff0-ff3939ffbfca}


RER Terephthalic acid agg PlasticsEurope {a1ec09b7-1d9c-44b8-9ab7-2cda4c30d810}


EU-28 Waste incineration of pa-per fraction in municipal solid waste (MSW)

p-agg ELCD/CEWEP {77b31dcd-5acd-47bf-8b6e-d9eadfd8b136}


EU-28 Waste incineration of glass/inert material

p-agg ELCD/CEWEP {60815257-bdaa-495a-a8da-163c5ed2439d}


58

Annex II: EPDs with expired validity

Country Process name Type Source Process GUID (Can be entered in the search tool)

EU-25 Rubber flooring smooth EN 1817 agg ERFMI {ad76e6db-ffdb-4bc5-8a68-3ea1bd06d359}

DE Chipboard (average) agg ts-EPD {5be2a1d7-b4e8-4309-91c3-f88ab5c7aa1c}

DE Chipboard Eurospan - Egger agg ts-EPD {30db63bf-23f8-4332-bd3d-31d11a70452b}

DE Chipboard Eurospan - Egger agg ts-EPD {0f9705f0-12d3-4341-aecb-18ad55fb6ea8}

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}

DE Technical textile VALMEX® FR 1000 - Mehler Texnologies (C2) agg ts-EPD {d5bb65f0-1f63-469d-9653-de3b59fe0028}

DE Chipboard (average) agg ts-EPD {0d98e99d-9ff4-46b1-adf0-b638f97e114a}

RNA Leak barrier, asphalt shingle roofing system component - ARMA (A1-A3) agg ts-EPD {8104830f-9b8c-4779-920d-9b50d9a58cb5}

US Reinforced ethylene propylene diene monomer roofing membrane [45mils] (A1-A3) - Carlisle

agg ts-EPD {43e83d5f-e4aa-483f-99ab-f29298344a61}

DE Texlon System 100% Recycling - Vector Foiltec (C3) p-agg ts-EPD {9510fb94-60ca-4878-bf3a-2b3efdcd039c}

DE Production (A1-A3) Lucobit 1235 (ECB) agg ts-EPD {779527de-9e1f-48d4-b226-26a93923312b}

RNA Painting of aluminum extrusion, AEC p-agg ts-EPD {0b0415f1-57ec-48eb-a245-852eda74c403}

DE Facing bricks/clinker bricks/quater bricks (BV Ziegel) (A1-A3) agg ts-EPD {3d14509a-d8c6-4996-9366-47bfb22e7d8e}

DE Concrete admixtures – Set accelerators - Deutsche Bauchemie e.V. (DBC) (A1-A3) p-agg ts-EPD {168c2bfe-388d-4a77-b2bb-a81b5e0baa49}

DE Stone wool - Rockwool agg ts-EPD {4c7f15d9-f7e0-4c11-84f5-29985b11d958}

DE Production (A1-A3) Lucobit 1210A (ECB) agg ts-EPD {5683c209-e00e-4cbe-a92d-cdeb3cf1519b}

EU-28 Special mortar (Bulwark - joint mortar) - IWM (A1-A3) agg ts-EPD {963bef67-aebb-4dac-b0f8-bb28f3a26d99}

59

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