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[2117] => c6e43829-649a-4ffd-8dae-978f5eca4e7e
)
Alle prosjekter
Array
(
[Status] => 200
[Result] => Array
(
[Data] => Array
(
[0] => Array
(
[Id] => 807679b6-ab53-48e9-a526-020b975c2e96
[VersionNumber] =>
[AuthorId] => 6b8922c0-0d90-4185-b7ba-f7313d83f5f2
[AuthorName] => jkn@treteknisk.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
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[IsGenericLibrary] =>
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[IsLinked] =>
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[IsLocked] =>
[IsHidden] => 1
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2022-02-22T12:40:45.9636553
[ModifiedOn] => 2022-03-30T18:12:30.4915051
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[1] => Array
(
[Id] => a17a6535-d33d-4416-b1b6-76d2193678ff
[VersionNumber] =>
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[AuthorName] => pieter@norsus.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] =>
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[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
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[IsLocked] =>
[IsHidden] => 1
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2022-09-27T10:54:05.8679086
[ModifiedOn] => 2023-05-31T12:15:38.9816933
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[2] => Array
(
[Id] => 42857b44-1699-43c3-9de7-92a8c9e144f6
[VersionNumber] =>
[AuthorId] => f85f5371-0565-455c-a114-155dacbcb44d
[AuthorName] => pieter@norsus.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] =>
[IsGenericLibrary] =>
[Name] => NORSUS (3.6)
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] => 1
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-02-15T14:12:33.7395643
[ModifiedOn] => 2022-11-14T05:04:48.5935792
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[3] => Array
(
[Id] => b5906475-1e33-45ee-82c5-989bb4c4f64b
[VersionNumber] =>
[AuthorId] => 6b8922c0-0d90-4185-b7ba-f7313d83f5f2
[AuthorName] => jkn@treteknisk.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] =>
[IsGenericLibrary] =>
[Name] => original urenset Norsk Treteknisk Institutt (Forestry, sawlog, spruce at forest road, NO)
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] => 1
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-09-09T07:35:54.8971825
[ModifiedOn] => 2022-03-01T12:31:08.8671262
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[4] => Array
(
[Id] => d744b04c-2c17-4def-8efe-9e4f490bb709
[VersionNumber] =>
[AuthorId] => f85f5371-0565-455c-a114-155dacbcb44d
[AuthorName] => pieter@norsus.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] =>
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[IsLinked] =>
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[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-09-20T13:08:17.8744185
[ModifiedOn] => 2022-10-03T08:48:45.6012971
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[5] => Array
(
[Id] => c62705ee-f6d3-425f-a364-cfd07f78fac7
[VersionNumber] =>
[AuthorId] => f85f5371-0565-455c-a114-155dacbcb44d
[AuthorName] => pieter@norsus.no
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] =>
[IsGenericLibrary] =>
[Name] => TEST
[IsLinked] =>
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[IsNew] =>
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[IsLocked] =>
[IsHidden] => 1
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2020-11-04T08:04:18.087198
[ModifiedOn] => 2022-03-30T12:38:24.9669263
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[6] => Array
(
[Id] => da225fb9-63e4-41d1-b478-3393f95123a5
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => ecoinvent 3.8 Cut-off
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] => 3
[Version] => 8
[CreatedOn] => 2021-09-27T08:58:10.5647668
[ModifiedOn] => 2022-10-17T15:34:37.2594561
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[7] => Array
(
[Id] => 0a13b015-de4d-4e75-ab62-6e624fa0272f
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => EcoInvent 3.5 Cut-off
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] => 3
[Version] => 5
[CreatedOn] => 2018-09-07T18:20:41.7333333
[ModifiedOn] => 2021-06-23T13:55:41.2655716
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[8] => Array
(
[Id] => 1fdbcd64-fab4-462a-95c3-76df2cfa8228
[VersionNumber] =>
[AuthorId] => 27ff5bb3-0978-44f5-ae00-67b7ae4e11de
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Agri-footprint 6 - economic - unit
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 6
[CreatedOn] => 2022-06-22T13:31:17.8186143
[ModifiedOn] => 2022-07-12T14:23:10.2014892
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[9] => Array
(
[Id] => ce4b7e3b-4415-4ae4-96ed-792d9c34f7ab
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Agri-footprint 5.0 | Energy
[IsLinked] =>
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[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-07-26T13:23:33.2852769
[ModifiedOn] => 2021-09-06T16:14:58.1420182
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[10] => Array
(
[Id] => 894f08bc-5577-4df8-8108-9cd1fed4083e
[VersionNumber] =>
[AuthorId] => 27ff5bb3-0978-44f5-ae00-67b7ae4e11de
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Industry Data 2.0 (SimaPro 9.2)
[IsLinked] =>
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[IsNew] =>
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[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-07-22T14:13:10.273582
[ModifiedOn] => 2022-07-01T12:15:48.1497274
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[11] => Array
(
[Id] => ab1e4913-2622-47cb-a970-a41bbd5ca896
[VersionNumber] =>
[AuthorId] => 27ff5bb3-0978-44f5-ae00-67b7ae4e11de
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Industry Data 2.0
[IsLinked] =>
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[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2022-01-12T03:14:13.8280377
[ModifiedOn] => 2022-07-12T14:20:28.9599066
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[12] => Array
(
[Id] => a255378b-624f-4298-aaaa-b4fd08f38c94
[VersionNumber] =>
[AuthorId] => 31f61d09-30d2-41ed-b6c4-06e0c89b3528
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => EcoInvent Products
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
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[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] =>
[CreatedOn] => 2017-10-16T08:27:37.7566667
[ModifiedOn] => 2021-02-03T12:25:30.5248005
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[13] => Array
(
[Id] => ce9674d5-db96-4e05-b339-cebbfd24d6b7
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => ecoinvent 3.6 Cut-off
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
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[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] => 3
[Version] => 6
[CreatedOn] => 2019-09-03T19:19:55.4099991
[ModifiedOn] => 2021-06-28T10:18:29.586249
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[14] => Array
(
[Id] => 756908bd-5823-4c13-8483-d52ef3699ff1
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => USLCI
[IsLinked] =>
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[Type] => Flow
[MajorVersion] =>
[Version] => 1
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[ModifiedOn] => 2022-07-12T14:19:28.7750302
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[15] => Array
(
[Id] => ab2efa02-aac7-4545-b624-d9daacaac383
[VersionNumber] =>
[AuthorId] => 31f61d09-30d2-41ed-b6c4-06e0c89b3528
[AuthorName] =>
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[FlowProjectId] =>
[IsLibrary] => 1
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[Name] => EcoInvent 3.4 Cut-off
[IsLinked] =>
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[ModifiedOn] => 2017-10-17T08:56:27.1966667
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[16] => Array
(
[Id] => 7c83d521-6aa9-44fc-bc29-e373d2597b11
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Agri-footprint 5.0 | Mass
[IsLinked] =>
[IsSentOut] =>
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[IsNew] =>
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[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-07-26T14:15:22.6243598
[ModifiedOn] => 2022-02-24T14:58:20.2289212
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[17] => Array
(
[Id] => 2fa45538-9db6-44e6-ae04-f5d541fec504
[VersionNumber] =>
[AuthorId] => b22be888-45cc-4d6c-bfb3-6ae4b4bebb4d
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => Agri-footprint 5.0 | Economic
[IsLinked] =>
[IsSentOut] =>
[IsArchived] =>
[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] =>
[Version] => 1
[CreatedOn] => 2021-07-26T13:56:30.0092331
[ModifiedOn] => 2021-07-26T13:56:53.5351518
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
[18] => Array
(
[Id] => 6e5702d7-c4b1-4be0-ab5c-f9b7f83e6bc1
[VersionNumber] =>
[AuthorId] => 20d62800-71db-48b1-ae4b-5dbce00e4d71
[AuthorName] =>
[GroupId] => 00000000-0000-0000-0000-000000000000
[FlowProjectId] =>
[IsLibrary] => 1
[IsGenericLibrary] => 1
[Name] => ecoinvent 3.7.1 Cut-off
[IsLinked] =>
[IsSentOut] =>
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[IsNew] =>
[IsCompleted] =>
[IsLocked] =>
[IsHidden] =>
[Type] => Flow
[MajorVersion] => 3
[Version] => 7
[CreatedOn] => 2021-01-20T16:07:43.9139405
[ModifiedOn] => 2021-10-12T16:06:49.7381735
[OwnershipUserId] => 00000000-0000-0000-0000-000000000000
[OwnershipUserName] =>
[Publisher] =>
[Description] =>
[UseWatersheds] =>
[ProjectItems] => Array
(
)
)
)
[Total] => 19
)
[Errors] =>
)
Alle prosjekter slutt
nameNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)
Rådata fra ecospold endpoint for prosjekt 807679b6-ab53-48e9-a526-020b975c2e96
Array
(
[0] => Array
(
[ProjectId] => 807679b6-ab53-48e9-a526-020b975c2e96
[Name] => Planting {NO}
[GeographyId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a
[Geography] => Array
(
[Id] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a
[LibraryId] => 10f1ec30-7d7e-11de-9ae2-0019e336be3a
[Name] => NO
)
[ReferenceProductId] => dfd3df7d-41af-4fd1-aa5f-83ef33fa02e4
[ReferenceProduct] => Array
(
[Id] => dfd3df7d-41af-4fd1-aa5f-83ef33fa02e4
[LibraryId] => dfd3df7d-41af-4fd1-aa5f-83ef33fa02e4
[Name] => Planting {NO}
)
[CreatedOn] => 2022-02-22T12:40:45.9636553
[ModifiedOn] => 2022-02-22T16:33:43.2701657
[ModelingAndValidation] => Array
(
)
[AdministrativeInformation] => Array
(
[DataGenerator_ReferenceToPersonOrEntityGeneratingTheDataSet] => Array
(
[0] => Array
(
[Id] => 6b8922c0-0d90-4185-b7ba-f7313d83f5f2
[LibraryId] => efcb8d4d-fc82-4c72-96fd-8b6ad25d7b52
[Name] => Johann Kristian Næss
[Email] => jkn@treteknisk.no
)
)
[DataEntryBy_ReferenceToPersonOrEntityEnteringTheData] => Array
(
[Id] => 6b8922c0-0d90-4185-b7ba-f7313d83f5f2
[LibraryId] => efcb8d4d-fc82-4c72-96fd-8b6ad25d7b52
[Name] => Johann Kristian Næss
[Email] => jkn@treteknisk.no
)
[PublicationAndOwnership_ReferenceToEntitiesWithExclusiveAccess] => Array
(
[0] => Array
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Dinitrogen monoxide utslipp som reaksjon etter skoggjødsling: "Målrettet gjødsling av skog som Klimatiltak 5.1.3. (Nibio 2014)"
Utslipp av lystgass i norsk skog 2011 1600 kg
Antall dekar gjødslet skog i 2011 5844 dekar
Mengde lystgass per gjødslet dekar 0,273785079 kg/dekar
Gjødsling i skog Øst-Norge 2017 77 456 dekar. Totalt utslipp dinitrogen monixide = 21206,29
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[GeneralComment] => ##General Information##
##Product name##
Sawlog; spruce; measured under bark; mechanical forestry, production mix; at forest road
##Reference flow##
1 m3, under bark
##System boundaries##
List of input processes:
Pre-commercial thinning (previously named Cleaning), Forwarding,
Pesticides, Final harvest,
Fertilizing, Planting, Road building, Road rebuilding,
Seed production, Seedling production, Soil scarification. All of these 11 processes use ecoinvent background process inputs, as well, and we are working on getting them published for easy reuse of this data set.
Road building. Harvest of forest and land transformation. Machine operation and transport of machines. Gravel for road material and transport of gravel.
Road rebuilding. Land transformation. Machine operation and transport of machines. Gravel for road material and transport of gravel.
Soil scarification. Machine use. Transport of machine and personnel between sites.
Planting. Seed and seedling production. Transport plants and personnel.
Pre-commerical thinning (previously "Cleaning"). Power sawing machine use. Transport of personnel.
Pesticide, spreading. Production of pesticide. Spreading with helicopter.
Fertilizing. Production of fertilizer. Transport of fertilizer from production on lorry. Spreading with helicopter. Emissions to air of
dinitrogen monoxide.
Final harvesting. Harvesting machine use. Transport of harvester between sites.
Forwarding. Forwarder including direct emissions, fuel production, production, maintenance, end-of-life of forwarder. Transport of forwarder between harvest areas. Transport of personnel to harvest areas.
##Wastes and end-of-life##
There are no wastes in the core process, upstream processes have waste treatment as in ecoinvent cut-off.
##Biogenic carbon##
Biogenic carbon flows are not included in the core LCI and must be added if relevant for the impact assessment.
##Use advice of the dataset##
If used for EN 15804+A1, additional flows in the LCI must be added for the energy and biogenic carbon. If used for EN 15804:+A2, land use must in addition also be added. Note: if importing into Nordic or German Excel, there is an issue with certain numbers turning into date, for instance 1.29 turning into January the first.
##Technological representativeness##
##Technology description##
Mechanical forestry with harvester and forwarder. Fossil source of diesel.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for the most common harvesting in Norway for softwood. On the west coast of Norway, other harvesting such as with cables might be used. The fuel used in forwarder are dependent on the distance to the road and 500 meters are included, but could be shorter or longer.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2017
##Time representativeness description##
Silviculture activities (fertilizer, Road building, Road rebuilding, Soil scarification, Planting, Cleaning,
Pesticide, spreading) based on 2017. Harvesting fuel use from 2009, but evaluated as representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Based on ecoinvent cut-off.
##LCI allocation methods##
Economic allocation for all forestry processes except forwarding.
Allocation between sawlog and pulpwood has been adjusted to be in line with the requirements in EN 15804. This means economic allocation of silviculture operations and harvesting, but the forwarding is not allocated. It is assumed that the forwarding is not affected of the demand for pulpwood and it is therefore not part of the joint co-production. In silviculture and harvesting, this is assumed to be a joint co-production process. The data has been adjusted from volume allocation to economic allocation for silviculture and harvester as they have been assumed joint co-production. Forwarder are not assumed joint co-production, so this has an equal allocation based on volume. Adjustment of volume allocation to economic is by a factor of 1.3 for sawlog and 0.6 for pulpwood.
##Other comments on methods approaches##
The LCI procedures is with the aim to be used in foreground LCI modelling for EPD according to EN 15804. The inputs from nature are not included from the direct forest activities (land transformation and occupation, biogenic carbon and energy content, biomass). These must be added in a later stage of the LCI when necessary.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied when data has been available. Some purchased services and infrastructure might be left out.
##Infrastructure/capital goods##
Included in harvesting, forwarder and other transport systems. Building infrastructure are not included for plant production.
##Data treatment and extrapolations principles##
Forestry in Norway are based on the report by Timmermann & Dibdiakova (2013). Foreground activity data from Timmermann & Dibdiakova, but otherwise upstream from Ecoinvent v3.6. Silviculture activities based on data from Skogfrøverket, Statistics Norway and Landbruksdirektoratet with 2017 as reference year. Emissions of dinitrogen monoxide with forestry fertilizing are from “Målrettet gjødsling av skog som klimatiltak”, Nibio (2014).
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Lars G. F. Tellnes,
Johann K. Næss
##Access and use restrictions##
Open. Send a mail to Silje Unander at sun(a)treteknisk . no for a CSV copy of the whole data set, that is, including the ecoinvent background processes.
##Version##
1
##
[Tags] => Sector_Materials production / Wood, Access_open
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process:
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Dinitrogen monoxide utslipp som reaksjon etter skoggjødsling: "Målrettet gjødsling av skog som Klimatiltak 5.1.3. (Nibio 2014)"
Utslipp av lystgass i norsk skog 2011 1600 kg
Antall dekar gjødslet skog i 2011 5844 dekar
Mengde lystgass per gjødslet dekar 0,273785079 kg/dekar
Gjødsling i skog Øst-Norge 2017 77 456 dekar. Totalt utslipp dinitrogen monixide = 21206,29
21206,29 / 7745,6 hektar = 2,737 kg
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[GeneralComment] => ##General Information##
##Product name##
Sawlog; spruce; measured under bark; mechanical forestry, production mix; at forest road
##Reference flow##
1 m3, under bark
##System boundaries##
List of input processes:
Pre-commercial thinning (previously named Cleaning), Forwarding,
Pesticides, Final harvest,
Fertilizing, Planting, Road building, Road rebuilding,
Seed production, Seedling production, Soil scarification. All of these 11 processes use ecoinvent background process inputs, as well, and we are working on getting them published for easy reuse of this data set.
Road building. Harvest of forest and land transformation. Machine operation and transport of machines. Gravel for road material and transport of gravel.
Road rebuilding. Land transformation. Machine operation and transport of machines. Gravel for road material and transport of gravel.
Soil scarification. Machine use. Transport of machine and personnel between sites.
Planting. Seed and seedling production. Transport plants and personnel.
Pre-commerical thinning (previously "Cleaning"). Power sawing machine use. Transport of personnel.
Pesticide, spreading. Production of pesticide. Spreading with helicopter.
Fertilizing. Production of fertilizer. Transport of fertilizer from production on lorry. Spreading with helicopter. Emissions to air of
dinitrogen monoxide.
Final harvesting. Harvesting machine use. Transport of harvester between sites.
Forwarding. Forwarder including direct emissions, fuel production, production, maintenance, end-of-life of forwarder. Transport of forwarder between harvest areas. Transport of personnel to harvest areas.
##Wastes and end-of-life##
There are no wastes in the core process, upstream processes have waste treatment as in ecoinvent cut-off.
##Biogenic carbon##
Biogenic carbon flows are not included in the core LCI and must be added if relevant for the impact assessment.
##Use advice of the dataset##
If used for EN 15804+A1, additional flows in the LCI must be added for the energy and biogenic carbon. If used for EN 15804:+A2, land use must in addition also be added. Note: if importing into Nordic or German Excel, there is an issue with certain numbers turning into date, for instance 1.29 turning into January the first.
##Technological representativeness##
##Technology description##
Mechanical forestry with harvester and forwarder. Fossil source of diesel.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for the most common harvesting in Norway for softwood. On the west coast of Norway, other harvesting such as with cables might be used. The fuel used in forwarder are dependent on the distance to the road and 500 meters are included, but could be shorter or longer.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2017
##Time representativeness description##
Silviculture activities (fertilizer, Road building, Road rebuilding, Soil scarification, Planting, Cleaning,
Pesticide, spreading) based on 2017. Harvesting fuel use from 2009, but evaluated as representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Based on ecoinvent cut-off.
##LCI allocation methods##
Economic allocation for all forestry processes except forwarding.
Allocation between sawlog and pulpwood has been adjusted to be in line with the requirements in EN 15804. This means economic allocation of silviculture operations and harvesting, but the forwarding is not allocated. It is assumed that the forwarding is not affected of the demand for pulpwood and it is therefore not part of the joint co-production. In silviculture and harvesting, this is assumed to be a joint co-production process. The data has been adjusted from volume allocation to economic allocation for silviculture and harvester as they have been assumed joint co-production. Forwarder are not assumed joint co-production, so this has an equal allocation based on volume. Adjustment of volume allocation to economic is by a factor of 1.3 for sawlog and 0.6 for pulpwood.
##Other comments on methods approaches##
The LCI procedures is with the aim to be used in foreground LCI modelling for EPD according to EN 15804. The inputs from nature are not included from the direct forest activities (land transformation and occupation, biogenic carbon and energy content, biomass). These must be added in a later stage of the LCI when necessary.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied when data has been available. Some purchased services and infrastructure might be left out.
##Infrastructure/capital goods##
Included in harvesting, forwarder and other transport systems. Building infrastructure are not included for plant production.
##Data treatment and extrapolations principles##
Forestry in Norway are based on the report by Timmermann & Dibdiakova (2013). Foreground activity data from Timmermann & Dibdiakova, but otherwise upstream from Ecoinvent v3.6. Silviculture activities based on data from Skogfrøverket, Statistics Norway and Landbruksdirektoratet with 2017 as reference year. Emissions of dinitrogen monoxide with forestry fertilizing are from “Målrettet gjødsling av skog som klimatiltak”, Nibio (2014).
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Lars G. F. Tellnes,
Johann K. Næss
##Access and use restrictions##
Open. Send a mail to Silje Unander at sun(a)treteknisk . no for a CSV copy of the whole data set, that is, including the ecoinvent background processes.
##Version##
1
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process name: Planting {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Fertilizing, v2 {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Forwarding {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Pre-commercial thinning {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Sawlog; spruce; measured under bark; mechanical forestry, production mix; at forest roadexists: 2117Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / Wood, Access_openNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / Wood, Access_openNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)existsthis exists. 2117process name: Final harvest {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Road rebuilding {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Pesticides {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Road building {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Seedling production {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Seed production {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniedprocess name: Soil scarification {NO}exists: Norsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)has tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)save_tagsSector_Materials production / WoodNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)does not existsNorsk Treteknisk Institutt (sagtømmer, renset)(Forestry, sawlog, spruce at forest road, NO)accessdeniednameNORSUS (3.8)
Rådata fra ecospold endpoint for prosjekt a17a6535-d33d-4416-b1b6-76d2193678ff
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[ActivityName] => Hot rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Hot rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with the rolling of steel sheets.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the rolling process, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
[Tags] => Sector_Materials production / Metals and semimetals, Access_open, GLAD
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[Name] => Electricity; Norwegian consumption mix, low voltage, to consumer
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[ActivityName] => Electricity; Norwegian consumption mix, low voltage, to consumer
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, low voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for low voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Transformation from high via medium to low voltage is included. Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
This dataset represent low voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level. Ecoinvent 3.8 has been used for the background processes.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
##
[Tags] => Sector_Energy carriers and technologies / Electricity, Access_open, GLAD
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[Name] => NO
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[Name] => CO2 from upgrading of biogas; from food waste and manure; production, distribution, and use
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[CreatedOn] => 2022-09-27T11:01:39.5819231
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[ActivityName] => CO2 from upgrading of biogas; from food waste and manure; production, distribution, and use
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
CO2 from upgrading of biogas; from food waste and manure; production, distribution, and use
##Reference flow##
1 kg
##System boundaries##
CO2 from upgrading of biogas is considered as recyclable material, in line with the methodology of ecoinvent database cut off by classification. This means that CO2 is seen as a recyclable waste flow from production of biogas, and only the distribution and use phase is included in the inventory.
##Wastes and end-of-life##
The inventory only considers CO2 used as a product, there are no waste streams in the system
##Biogenic carbon##
CO2 is emitted as biogenic CO2 during use phase, and is reported separately in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling the use of CO2 from upgrading of biogas distributed in a pipeline. The dataset does not include compression of the CO2.
##Technological representativeness##
##Technology description##
1 kg CO2 produced at an anaerobic digestion plant, transported in pipeline.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county. The CO2 is used in a greenhouse located in close proximity with the biogas plant
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
CO2 is considered as a recyclable flow from production of biogas. Impacts from anaerobic digestion is attributed to production of biogas and digestate, or to the organic waste treatment. Upgrading of the biogas is attributed to biogas production and is considered outside the system boundaries of CO2 as a product.
##Other comments on methods approaches##
Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Piping infrastructure in distribution of CO2 is included
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
##
[Tags] => Access_open, GLAD, Sector_Energy carriers and technologies / Other non-renewable fuels
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[Name] => Heat, for district heating, Norwegian production mix, without waste incineration or ambient heat, at plant
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[Id] => 35dfb0d3-4f13-476d-99e6-1b2fc5aa9e7f
[ActivityName] => Heat, for district heating, Norwegian production mix, without waste incineration or ambient heat, at plant
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Heat, for district heating, Norwegian production mix, without waste incineration or ambient heat, at plant
##Reference flow##
1 kWh
##System boundaries##
Production mix of district heat in Norway in 2021, based on information on energy carriers from Norsk Fjernvarme, minus heat from waste combustion and ambient heat from industry. The activity starts with acquisition of energy carriers, i.e., acquisition and transport of fossil oil and gas, harvesting and transport of woody biomass, and production, transformation from high to low voltage and transmission of electricity. The activity stops at district heat production facility (i.e. distribution is not included).
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset should be used to model the production mix of district heat that is avoided by using heat from waste combustion or ambient heat from industry. Hence, those two energy carriers are removed from the average production mix of heat for district heating, such that they do not replace themselves. This dataset represents the district heat production mix in Norway in the year 2020. Data on heat production and share of different energy carriers are collected from www.fjernkontrollen.no. ecoinvent 3.8 allocation, cut-off by classification is used for the background data.
##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
Allocation cut-off by classification in background data.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure is included
##Data treatment and extrapolations principles##
The energy carriers are given by Norsk Fjernvarme. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For bioenergy, wood logs combustion is used as representative technology. Heat from incineration of waste and ambient heat from industry are removed from the replaced heat, so that they do not replace themselves.
##Data collection period##
2021
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2022). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.09.2022.
##
[Tags] => Sector_Energy carriers and technologies / Heat and steam, Access_open, GLAD
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[Name] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
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[Name] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
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[ActivityName] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
[Tags] => Access_open, GLAD, Sector_Materials production / Other materials
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[Name] => Household waste collection, residual waste, to residual waste sorting facility
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[ActivityName] => Household waste collection, residual waste, to residual waste sorting facility
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[GeneralComment] => ##General Information##
##Product name##
Household waste collection, residual waste, to residual waste sorting facility
##Reference flow##
1 kg
##System boundaries##
The process includes the collection of 1 kg residual waste and end with the delivery of the waste at the waste bunker of the residual waste sorting facility. Regarding the production of biogas used as fuel, the system starts at the biogas upgrading facility.
##Wastes and end-of-life##
Waste treatment and maintenance of the infrastructure are included in the dataset. Further treatment of the collected waste itself is not included in the dataset
##Biogenic carbon##
Biogenic emissions from the use of biogas are accounted for by using a “biogenic” substance such that these can be distinguished from fossil emissions by the impact assessment method.
##Use advice of the dataset##
This dataset should be used to model the impact from residual waste collection in Romerike, Norway. Since residual waste is going to a residual waste sorting facility, only glass & metal and paper & cardboard are collected separately. Collection of these waste fraction is not included in the dataset. Ecoinvent 3.8 is used to model the background processes.
##Technological representativeness##
##Technology description##
77% of the residual waste is collect by waste collection vehicles on biogas, 23% is collected by diesel vehicles. Direct emissions from biogas are modelled using natural gas emission profiles as proxy, but these are adjusted to the lower methane content of biogas.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The dataset is representative for the Romerike district in south-eastern Norway
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2016
##Time representativeness description##
The waste collection routes, amount of residual waste collected, and amount of diesel / biogas used are from 2016 and are collected from Callewaert (2017)
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Besides the allocation principles carried out by ecoinvent cut-off by classification, no other allocation methods are used
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (road, cars, etc.) are included
##Data treatment and extrapolations principles##
None
##Data collection period##
2017
##Administrative Information##
##Data set generator##
Pieter Callewaert
##Access and use restrictions##
Open
##References##
Callewaert, P. (2017). Analysing the sustainability performance and critical improvement factors of urban municipal waste systems. (Masters). NTNU, Trondheim. Retrieved from http://hdl.handle.net/11250/2454900
##
[Tags] => Sector_End-of-life treatment / Waste collection, Access_open, GLAD
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[Name] => Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
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[Name] => Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
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[ActivityName] => Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
##Reference flow##
1 kg dry matter
##System boundaries##
The system includes all upstream processes (extraction, transport and refinement of raw materials and energy) and processes taking place at the biorefinery. Energy and resource use in offices is included. Work travels and commuting to work is not included. Emissions from combustion of waste for steam production are not allocated the user of the heat, rather the producer of the waste, according to the polluter pays principle.
##Wastes and end-of-life##
Treatment of waste is included. Recycling credits outside the biorefinery are not included.
##Biogenic carbon##
Uptake and emissions of biogenic carbon are included.
##Use advice of the dataset##
The aim of the dataset is to document the environmental properties for a product under development. The product was developed in the BACS project (short for BioActive Compounds from Spruce), where the underlying idea was to develop sustainable value added products, processes and applications for compounds extracted from Norway spruce that are bioactive or will stimulate and improve the bioactivity of other compounds in formulations. Preliminary LCA results were used as input to the innovation process by identifying hotspots. The dataset was developed in the WP 8 work package of the BACS research project funded by the Research Council of Norway (consortium agreement no. 295501). Borregaard was the project owner. Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Technological representativeness##
##Technology description##
Modelling of the foreground system of the biorefinery is based on specific data from Borregaard Sarpsborg, Norway. For the main biorefinery (processing of bark for use as input to the BACS biorefinery system), real data for the full-scale biorefinery have been used, hence the data represent the exact technology. For the BACS biorefinery system (processing bark to bark extract), calculated data based on theoretical information, estimates and knowledge from similar processes at the main biorefinery have been used. Data for the input chemicals and processes upstream the main biorefinery at Borregaard Sarpsborg have been found in generic databases.
##Technology Quality level##
Very good.
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Foreground data are specific for the product dataset (Borregaard biorefinery, Sarpsborg, Norway). As far as possible, background data representative for Norwegian conditions have been used.
##Geographical Quality level##
Very good.
##Time related representativeness##
##Reference year##2021
##Time representativeness description##
Specific data are from 2019 and 202. Annual data have been used for the main biorefiney, except for steam, where the input of electricity and natural gas was averaged over a 7-year period. For the BACS biorefinery system, hourly data have been used. Data for production of other raw materials, energy, transport and waste treatment options have been found in the ecoinvent database. Choosing the most correct technology has been prioritised over newer data.
##Time Quality Level##
Very good.
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
For generic data, ecoinvent’s ‘allocation, cut-off by classification’ database have been used. For specific data at the biorefinery, allocation has been avoided as far as possible by analysing the processes on a detailed level. When necessary, energy allocation (based on dry matter content (DM)) has been used at the biorefinery.
##Other comments on methods approaches##
Economic allocation was found unsuitable because several of the allocations have included products that have no market value, and because over time, no product in the main biorefinery acts as the single driving force of the system.
##Completeness##
##Cut off for mass or energy flows##
100% coverage
##Infrastructure/capital goods##
Included
##Data treatment and extrapolations principles##
All results and allocations have been based on dry matter (DM) in the internal flows and final products. Ethanol has been accounted for as DM in both internal flows and final products. For steam input and output, data from the main biorefinery have been used (assumed as ‘large’ compared with the BACS biorefinery system, hence not affected by the consumption in the BACS biorefinery system). Delivery of steam from the BACS biorefinery system back to the main biorefinery is included as avoided burdens.
##Data collection period##
2020-2022
##Administrative Information##
##Data set generator##
Ingunn Saur Modahl
##Access and use restrictions##
Open
##References##
Modahl, I. S. and E. Soldal (2021). The 2019 LCA of products from Borregaard, Sarpsborg. Report no OR.14.21. Link: https://norsus.no/en/publikasjon/the-2019-lca-of-products-from-borregaard-sarpsborg/. Fredrikstad, NORSUS.
Modahl, I.S., Brekke, A. and Valente, C. (2022): Sustainability of BACS products (BioActive Compounds from Spruce). Report no. OR.17.22 (confidential). Fredrikstad, Norway.
##
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[Email] => pieter@norsus.no
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[Id] => 8385d2de-e8aa-44f7-a5e9-437df661eb6e
[ActivityName] => Stainless steel, casted; Primary and secondary production, at plant; 30% primary material, 70% recycled material
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Stainless steel, casted; Primary and secondary production, at plant; 30% primary material, 70% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of stainless steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
[Tags] => Sector_Materials production / Metals and semimetals, Access_open, GLAD
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[Name] => Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
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[Name] => Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
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[CreatedOn] => 2022-09-27T11:01:39.5819231
[ModifiedOn] => 2022-10-13T07:35:36.4708075
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[Email] => pieter@norsus.no
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[ActivityName] => Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
##Reference flow##
1 kWh
##System boundaries##
Includes pre-treatment of food waste at the biogas facility, anaerobic digestion, upgrading, distribution by truck and use of biogas in a combustion engine. Collection of food waste is not included in the dataset. Collection of manure is included in the system boundaries. Incineration and transport of reject from pre-treatment of food waste is included. No substitution of avoided products included.
##Wastes and end-of-life##
Includes transport and combustion of the pre-treatment reject.
##Biogenic carbon##
Biogenic methane and CO2 emissions are reported separately by in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling the use of biogas in transport applications. When using manure for biogas production, the storage time (and related emissions) of manure is reduced. This effect is not included in the dataset. Furthermore, this dataset focusses on the impact on climate change. One should therefore be cautious in using this dataset to analyse other impact categories, especially toxicity.
##Technological representativeness##
##Technology description##
1 kWh upgraded biogas from household waste and manure. As input material in the biogas facility, approximately 50% comes from household waste and 50% from manure sourced from surrounding farms. Production process: 1) Mechanical pretreatment to remove unwanted objects and reduction of particle size: screw press, grinder, hydro cyclone. 2) Sterilization process for 1 hour at 70 °C 3) Anaerobic digestion: Hydraulic retention time (HRT) 36.5 days. 4) Upgrading: water scrubber. Emissions from driving: EURO 5.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county. Manure is transported from local farms.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Processes that are common to biogas and biofertilizer from digestate (transport of manure, pre-treatment, incineration of pre-treatment reject, anaerobic digesting) are allocated between the end products based on dry mass. Treatment (incineration) of plastic in reject from pre-treatment is not included.
##Other comments on methods approaches##
Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (biogas plant) included, based on literature data from Brogaard et al., 2015, Quantifying capital goods of organic waste treatment, Table 6. Service life of the biogas plant is assumed to be 30 years.
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
Brogaard, L. K., Petersen, P. H., Nielsen, P. D. & Christensen, T. H. (2015). Quantifying capital
goods for biological treatment of organic waste. Waste management & research: the journal
of the International Solid Wastes and Public Cleansing Association, ISWA, 33(2), 96-106.
https://doi.org/10.1177/0734242X14565212
##
[Tags] => Access_open, GLAD, Sector_Energy carriers and technologies / Other non-renewable fuels
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[ProjectId] => a17a6535-d33d-4416-b1b6-76d2193678ff
[Name] => Cold rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
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[Name] => Cold rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
)
[CreatedOn] => 2022-10-03T07:39:20.5657314
[ModifiedOn] => 2022-10-13T07:37:00.3927367
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[ActivityName] => Cold rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
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Cold rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with the rolling of steel sheets.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the rolling process, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
[Tags] => Sector_Materials production / Metals and semimetals, Access_open, GLAD
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[ProjectId] => a17a6535-d33d-4416-b1b6-76d2193678ff
[Name] => Low alloyed steel, aluzinc; galvanized, at plant; 43% zinc, 55% aluminium and 2% silicon; galvanized layer amounts to 6% of steel mass; 96% primary material, 4% recycled material
[GeographyId] => 0723d252-7e2a-11de-9820-0019e336be3a
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[Name] => Low alloyed steel, aluzinc; galvanized, at plant; 43% zinc, 55% aluminium and 2% silicon; galvanized layer amounts to 6% of steel mass; 96% primary material, 4% recycled material
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Low alloyed steel, aluzinc; galvanized, at plant; 43% zinc, 55% aluminium and 2% silicon; galvanized layer amounts to 6% of steel mass; 96% primary material, 4% recycled material
##Reference flow##
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##System boundaries##
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##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the galvanization process, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The shares of recycled and virgin materials are adjusted based on the use of technologies in the country (World steel association, 2021).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation done by ecoinvent
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
[Tags] => Sector_Materials production / Metals and semimetals, Access_open, GLAD
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[Name] => Anaerobic digestion, food waste, at plant
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[ActivityName] => Anaerobic digestion, food waste, at plant
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Anaerobic digestion, food waste, at plant
##Reference flow##
1 tonne
##System boundaries##
Includes pre-treatment of food waste at the biogas facility and the anaerobic digestion process. The pre-treatment includes incineration of reject which consist of organic material, plastic collection bags and mis-sortings. The inventory does not include downstream processes for the secondary products (biogas, digestate and CO2 as a product) or avoided emissions as a consequence of the secondary products substituting other products. Transport of the organic waste to the biogas plant is not included.
##Wastes and end-of-life##
The inventory represents the service of treating 1 tonne of organic waste. The dataset includes incineration of reject from pre-treatment.
##Biogenic carbon##
Biogenic methane and CO2 emissions are reported separately by in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling waste treatment of organic waste by anaerobic digestion. The transport of the organic waste to the biogas plant is not included, as this is dependent on where the waste is generated. If the purpose of the study is to compare treatment options (e.g. anaerobic digestion and energy recovery), the avoided emissions obtained when the generated secondary products substitute other products should be included. 1 tonne of food waste can be assumed to generate: 131 Nm3 raw (non upgraded) biogas, 6.7 kg N of liquid biofertilizer and 5.2 kg CO2 (delivered to greenhouse).
##Technological representativeness##
##Technology description##
Production process: 1) Mechanical pretreatment to remove unwanted objects and reduction of particle size: screw press, grinder, hydro cyclone. 2) Sterilization process for 1 hour at 70 °C 3) Anaerobic digestion: Hydraulic retention time (HRT) 36.5 days.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Cut off for mass or energy flows
##Other comments on methods approaches##
Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (biogas plant) included, based on literature data from Brogaard et al., 2015, Quantifying capital goods of organic waste treatment, Table 6. Service life of the biogas plant is assumed to be 30 years.
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
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[GeneralComment] => ##General Information##
##Product name##
Margarine, 80% fat, packaged, at consumer
##Reference flow##
1 kg of packaged product
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of margarine, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
The amount of waste from the production site amounts to 88.7% for material recycling, and 11.3% for energy recycling. The amount of waste is allocated to the products based on the economic value of total turnover.
Disposal of consumer packaging is based on the average waste system for household waste for 2019 reported by Grønt Punkt Norway, 2020, plastic: 33.5% material recycling, 66.4% incineration; cardboard: 50.3% material recycling and 49.6% combustion with energy utilization.
##Biogenic carbon##
##Use advice of the dataset##
If this dataset is used as a proxy for other margarine products, it is important to take the fat content of the current product into account. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
A number of raw materials are included in margarine production, each of which has its own production process and is transported to margarine production in Norway. Generic data is used for raw materials production, but the data is adapted to the country of origin's energy sources and specific data where this is stated by the manufacturer. The type of transport depends on where the raw material is produced. Transport of the raw materials can be divided into transport from the farm to raw material processing and to margarine production. The logistics for the various raw materials is complex and some raw materials are not grown close to where the raw material is processed. The overseas agricultural products are transported from the farm or plantation to the first processing stage and from there to the port to be shipped to Europe, where further processing takes place. Production and transport of packaging is also included. In the margarine production, energy is used to process the raw material for margarine, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian margarine production, 60% fat
The data quality and representativeness are good for most processes. Data from Ecoinvent and Agri-footprint have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Country-specific data is used for the raw materials that are included with more than 1%. Specific data is used for the amount of raw materials and packaging and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2019
##Time representativeness description##
The reference year for the margarine production process is 2019. For upstream and downstream processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for margarine.
##LCI allocation methods##
For raw materials for plant and feed production, economic allocation has been used for co-products on the farm (e.g. rapeseed oil and rapeseed meal) and follows the PEFCR for feed.
Allocation between milk and meat on the farm was based on biophysical principles according to PEFCR Dairy products. For the production of margarine, inflows and outflows were allocated based on the products' dry matter content, according to PEFCR Dairy products.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2019
##Administrative Information##
##Data set generator##
Hanne Møller, Simon Saxegård
##Access and use restrictions##
##References##
NORSUS report OR 55.20 (closed)
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[Tags] => Access_open, GLAD, Sector_Materials production / Food and renewable raw materials
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##Use advice of the dataset##
In Norwegian: Åkerbønner, bondebønner, hestebønner.
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##Technology description##
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##Technology Quality level## Good
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Norway
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##Geographical Quality level## Very Good
##Time related representativeness##
##Reference year##
2017
##Time representativeness description##
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##Time Quality Level##
Very Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
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##Completeness##
##Cut off for mass or energy flows##
Very small impacts such as packaging for seeds, pesticides and fertilizer not included.
##Infrastructure/capital goods##
Included buildings and machinery for primary production. Building is modelled on an actual building. Machinery impacts comes from Ecoinvent but based on a report showing the amount of machinery on a number of Norwegian farms.
##Data treatment and extrapolations principles##
Based on survey and expert advice.
##Data collection period##
2017-2018
##Administrative Information##
##Data set generator##
Erik Svanes
##Access and use restrictions##
Closed, please contact pieter@norsus to get access to the dataset.
##References##
##
[Tags] => Access_closed, GLAD, Sector_Materials production / Food and renewable raw materials
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##Wastes and end-of-life##
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##Biogenic carbon##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Asia
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Asian production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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Asia
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Good
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##Reference year##
2020
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##Time Quality Level##
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Attributional
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##Other comments on methods approaches##
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##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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AF 25/10 2021. Process for household plastic waste sorted at source per functional unit. The functional unit is 1 kg household plastic waste that is packed in bales and ready to be collected at the local municipality collection point for further transport to sorting and recycling.
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[ActivityName] => Electricity; Norwegian consumption mix, high voltage, to consumer
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, high voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for high voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
These datasets represent high voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
##
[Tags] => Sector_Energy carriers and technologies / Electricity, Access_open, GLAD
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[Name] => Milk from dual purpose production; at farmgate
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[ActivityName] => Milk from dual purpose production; at farmgate
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Milk production; at farmgate; Norway
##Reference flow##
1 kg fat and protein corrected milk (FPCM)
##System boundaries##
Cradle to farm gate, includes fertiliser production and application, imported and domestic feed production, average farm data of dual-purpose production in terms of scale, feeding regimes and manure management systems.
##Wastes and end-of-life##
Wastes and end-of-life is not included.
##Biogenic carbon##
Biogenic carbon is from methane from enteric fermentation and manure storage. Soil organic carbon is not included.
##Use advice of the dataset##
When using data, it is important to be aware of the use of allocation method used, which can greatly affect the results. For feed and plant production, economic allocation has been used for co-products on the farm (e.g., rapeseed oil and rapeseed meal) and follows the PEFCR for feed (FEFAC, 2018).
Allocation between milk and meat on the farm is based on biophysical principles according to PEFCR Dairy products (European Dairy Association, 2018) and which has also previously been recommended by the International Dairy Federation. Ecoinvent 3.9.1 (cut-off by classification) and Agrifootprint 6.3 is used for the background data.
##Technological representativeness##
##Technology description##
The data represented typical Norwegian cattle herds of dual-purpose production in terms of scale and feeding regimes with production levels corresponding to average milk yields, growth performance, and beef production in Norway in 2021. The dual-purpose dairy production was based on production data of Norwegian Red (NR) obtained from the Norwegian Dairy Herd Recording System (NDHRS). Milk yield 8,550 kg FPCM dairy cow-1. Carcass production per cow including bulls and surplus heifers was 273 kg per year, bull age at slaughter 17.8 months, heifer age at calving 25.6 month. Concentrate feed intake per dairy cow was 2,614 kg DM (dry matter)/LU (livestock unit).
Energy requirements for all animal categories (cows, heifer, bull) were obtained using the Nordic feed evaluation system (NorFor; Volden, 2011) through TINE Optifor. Diet compositions for dairy cattle were available through TINE Mjølkonomi®, an economic tool for milk producers. The composition of typical concentrate feeds for dairy cows, heifers and young bulls was given by Felleskjøpet Fôrutvikling. Manure was assumed to be deposited on pasture during summer (pasture season typically from mid-May to mid-September). During housing, the proportion of manure management system was considered for each animal category. Manure was applied on ley area during spring. Silage dry matter (DM) yield (6320 kg DM ha-1) and the use of fertilizer (159 kg N ha-1), lime (51.9 kg ha-1), herbicides (1.9 L glyphosate ha-1 and 560 ml MCPA; 2-methyl-4-chlorophenoxyacetic acid ha-1) (NIBIO, 2018; Statistics Norway, 2012; TINE, 2022), and diesel (8.02 L ha-1) (Korsaeth et al., 2016) for a typical Norwegian farm was made available through TINE. The ley and pasture area corresponded to the calculated forage requirements.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for average milk production in Norway. There is large variation between the regions and therefore these average data should not be used in analyses at regional or farm level.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
The reference year for the beef production is 2021, using time specific data for milk yield concentrate feeds. For background processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Background data based on ecoinvent cut-off.
##LCI allocation methods##
For feed production, economic allocation has been used for co-products on the farm and follows the PEFCR for feed. For allocation between meat and milk products at the farm, biophysical allocation was used according to the PEFCR for dairy products (European Dairy Association, 2018): using mass of fat and protein corrected milk (FPCM) and kg live weight sold per year converted to carcass weight.
##Other comments on methods approaches##
Emissions of methane, dinitrogen monoxide, ammonia, nitrogen dioxide and carbon dioxide are calculated based on the IPCC (2006). The emissions include enteric fermentation, manure storage, direct and indirect emissions from application of manure and mineral fertiliser, leaching and runoff and crop residues returned to soils and emissions from application of limestone.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied.
##Infrastructure/capital goods##
Some purchased services might be left out.
##Data treatment and extrapolations principles##
##Data collection period##
2019-2022
##Administrative Information##
##Data set generator##
Hanne Møller, Stine Samsonstuen
##Access and use restrictions##
open
##References##
NORSUS report: Life cycle assessment of meat -climate change, on assignment for Nortura
##
[Tags] => Access_open, GLAD, Sector_Materials production / Food and renewable raw materials
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[Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
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[Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
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[CreatedOn] => 2022-09-27T11:03:25.7495503
[ModifiedOn] => 2022-10-13T07:39:03.0409976
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[ActivityName] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
[Tags] => Access_open, GLAD, Sector_Materials production / Other materials
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[Name] => Waste treatment of household plastic packaging waste, put on market
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[CreatedOn] => 2023-03-14T14:26:44.6838051
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[ActivityName] => Waste treatment of household plastic packaging waste, put on market
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Waste treatment of household plastic packaging waste, put on market.
##Reference flow##
1 tonne plastic packaging material put on market.
##System boundaries##
Starts with one tonne plastic packaging waste put on market in Norway. Approx. 50% of the PPW is discarded in the residual waste bin and is incinerated at a local incineration plant in Norway. The remaing part is either separated by households (36%) and collected by municipalities for further waste treatment, or sent to at a mixed-waste material recovery facility (18%). Separated PPW is transported by train to Germany for further sorting and recycling. Sorting residues are incinerated in Germany. The inventory does not include avoided emissions because of secondary products substituting other products.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset should be used when modelling treatment of household plastic packaging waste in Norway. Assumed material composition: PET bottles: 4.5%, HDPE: 6.6%, PP: 12.3%, PE film: 40.6%, PET trays: 7.8%, PS: 3.7%, PP and other film: 9.4%, Other rigid plastic: 1.6%, Black rigid plastic: 6.6%, PE laminate: 7.0%
If the purpose is to compare different waste treatment options (e.g. recycling and energy recovery), the avoided emissions should be included in each system. 1 tonne plastic packaging material results in the production of 102 kg PO, 24 kg PP, 4 kg PS, 4 kg PET, 19 kg HPDE and 74 kg LDPE granulate in addition to the production of 16555 MJ heat and 2474 MJ electricity in Norway and 1582 MJ heat and 754 MJ electricity in Germany.
##Technological representativeness##
##Technology description##
PPW is either source separated by households or collected together with mixed waste and separated in a mixed-waste MRF afterward. In the dataset, 82% of the generated PPW is covered by a system that relies on source separation, 18% is separated from mixed waste in a mixed-waste MRF. The separated PPW is sorted and recycled in three different facilities in Germany. More information about the facilities is available in Furberg et al. (2022).
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The Producer Responsibility Organisation (PRO) managing the system covers 80% of Norway population, the remaining 20% of the population (mainly Oslo) is covered by another PRO which is not included in the dataset. Site specific data is collected for the mixed waste MRFs in Norway and for the three facilities in Germany. For the background processes, ecoinvent v3.8 cut-off is used.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Generated PPW, source separation efficiency, transport data and division of PPW between different treatment facilities in Germany is from 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Generic building infrastructure is included, sorting machines are not included due to lack of data
##Data treatment and extrapolations principles##
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Pieter Callewaert
##Access and use restrictions##
Open
##References##
Furberg, A., Callewaert, P. & Lyng, K.-A. (2022). Life cycle assessment of household plastic waste treatment in Norway (OR 07.22). NORSUS. https://norsus.no/wp-content/uploads/OR-07.22-Life-cycle-assessment-of-household-plastic-waste-treatment-in-Norway.pdf
##
[Tags] => Sector_End-of-life treatment / Material recycling, Access_open, GLAD
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[Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state
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[Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state
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[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
[Tags] => Access_open, GLAD, Sector_Materials production / Other materials
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[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, medium voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for medium voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Transformation from high to medium voltage is included. Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
This dataset represents medium voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level. Ecoinvent 3.8 has been used for the background processes.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
##
[Tags] => Sector_Energy carriers and technologies / Electricity, Access_open, GLAD
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[Name] => Knot extract; without water, in 60% solution state; from Norway Spruce biorefinery, at plant
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[ActivityName] => Knot extract; without water, in 60% solution state; from Norway Spruce biorefinery, at plant
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Knot extract; without water, in 60% solution state; from Norway Spruce biorefinery, at plant
##Reference flow##
1 kg dry matter
##System boundaries##
The system includes all upstream processes (extraction, transport and refinement of raw materials and energy) and processes taking place at the biorefinery. Energy and resource use in offices is included. Work travels and commuting to work is not included. Emissions from combustion of waste for steam production are not allocated the user of the heat, rather the producer of the waste, according to the polluter pays principle.
##Wastes and end-of-life##
Treatment of waste is included. Recycling credits outside the biorefinery are not included.
##Biogenic carbon##
Uptake and emissions of biogenic carbon are included.
##Use advice of the dataset##
The aim of the dataset is to document the environmental properties for a product under development. The product was developed in the BACS project (short for BioActive Compounds from Spruce), where the underlying idea was to develop sustainable value added products, processes and applications for compounds extracted from Norway spruce that are bioactive or will stimulate and improve the bioactivity of other compounds in formulations. Preliminary LCA results were used as input to the innovation process by identifying hotspots. The dataset was developed in the WP 8 work package of the BACS research project funded by the Research Council of Norway (consortium agreement no. 295501). Borregaard was the project owner. Ecoinvent 3 – allocation, cut-of by classification, version 3.8 was used as backgrund database.
##Technological representativeness##
##Technology description##
Modelling of the foreground system of the biorefinery is based on specific data from Borregaard Sarpsborg, Norway. For the main biorefinery (processing of knots for use as input to the BACS biorefinery system), real data for the full-scale biorefinery have been used, hence the data represent the exact technology. For the BACS biorefinery system (processing knots to knot extract), calculated data based on theoretical information, estimates and knowledge from similar processes at the main biorefinery have been used. Data for the input chemicals and processes upstream the main biorefinery at Borregaard Sarpsborg have been found in generic databases.
##Technology Quality level##
Very good.
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Foreground data are specific for the product dataset (Borregaard biorefinery, Sarpsborg, Norway). As far as possible, background data representative for Norwegian conditions have been used.
##Geographical Quality level##
Very good.
##Time related representativeness##
##Reference year##2021
##Time representativeness description##
Specific data are from 2019 and 2021. Annual data have been used for the main biorefiney, except for steam, where the input of electricity and natural gas was averaged over a 7-year period. For the BACS biorefinery system, hourly data have been used. Data for production of other raw materials, energy, transport and waste treatment options have been found in the ecoinvent database. Choosing the most correct technology has been prioritised over newer data.
##Time Quality Level##
Very good.
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
For generic data, ecoinvent’s ‘allocation, cut-off by classification’ database have been used. For specific data at the biorefinery, allocation has been avoided as far as possible by analysing the processes on a detailed level. When necessary, energy allocation (based on dry matter content (DM)) has been used at the biorefinery.
##Other comments on methods approaches##
Economic allocation was found unsuitable because several of the allocations have included products that have no market value, and because over time, no product in the main biorefinery acts as the single driving force of the system.
##Completeness##
##Cut off for mass or energy flows##
100% coverage
##Infrastructure/capital goods##
Included
##Data treatment and extrapolations principles##
All results and allocations have been based on dry matter (DM) in the internal flows and final products. Ethanol has been accounted for as DM in both internal flows and final products. For steam input and output, data from the main biorefinery have been used (assumed as ‘large’ compared with the BACS biorefinery system, hence not affected by the consumption in the BACS biorefinery system). Delivery of steam from the BACS biorefinery system back to the main biorefinery is included as avoided burdens.
##Data collection period##
2020-2022
##Administrative Information##
##Data set generator##
Ingunn Saur Modahl
##Access and use restrictions##
Open
##References##
Modahl, I. S. and E. Soldal (2021). The 2019 LCA of products from Borregaard, Sarpsborg. Report no OR.14.21. Link: https://norsus.no/en/publikasjon/the-2019-lca-of-products-from-borregaard-sarpsborg/. Fredrikstad, NORSUS.
Modahl, I.S., Brekke, A. and Valente, C. (2022): Sustainability of BACS products (BioActive Compounds from Spruce). Report no. OR.17.22 (confidential). Fredrikstad, Norway.
##
[Tags] => Access_open, Sector_Materials production / Organic chemicals , GLAD
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[CreatedOn] => 2023-04-12T11:09:43.5541723
[ModifiedOn] => 2023-04-12T11:15:12.9805428
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[Id] => 6e94cf6d-78ef-4324-b052-d83de6d63879
[ActivityName] => Margarine, 60% fat, packaged, at consumer
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Margarine, 60% fat, packaged, at consumer
##Reference flow##
1 kg of packaged product.
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of margarine, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
The amount of waste from the production site amounts to 88.7% for material recycling, and 11.3% for energy recycling. The amount of waste is allocated to the products based on the economic value of total turnover.
Disposal of consumer packaging is based on the average waste system for household waste for 2019 reported by Grønt Punkt Norway, 2020, plastic: 33.5% material recycling, 66.4% incineration; cardboard: 50.3% material recycling and 49.6% combustion with energy utilization.
##Biogenic carbon##
##Use advice of the dataset##
If this dataset is used as a proxy for other margarine products, it is important to take the fat content of the current product into account. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
A number of raw materials are included in margarine production, each of which has its own production process and is transported to margarine production in Norway. Generic data is used for raw materials production, but the data is adapted to the country of origin's energy sources and specific data where this is stated by the manufacturer. The type of transport depends on where the raw material is produced. Transport of the raw materials can be divided into transport from the farm to raw material processing and to margarine production. The logistics for the various raw materials is complex and some raw materials are not grown close to where the raw material is processed. The overseas agricultural products are transported from the farm or plantation to the first processing stage and from there to the port to be shipped to Europe, where further processing takes place. Production and transport of packaging is also included. In the margarine production, energy is used to process the raw material for margarine, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian margarine production, 60% fat
The data quality and representativeness are good for most processes. Data from Ecoinvent and Agri-footprint have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Country-specific data is used for the raw materials that are included with more than 1%. Specific data is used for the amount of raw materials and packaging and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2019
##Time representativeness description##
The reference year for the margarine production process is 2019. For upstream and downstream processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for margarine.
##LCI allocation methods##
For raw materials for plant and feed production, economic allocation has been used for co-products on the farm (e.g. rapeseed oil and rapeseed meal) and follows the PEFCR for feed.
Allocation between milk and meat on the farm was based on biophysical principles according to PEFCR Dairy products. For the production of margarine, inflows and outflows were allocated based on the products' dry matter content, according to PEFCR Dairy products.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2019
##Administrative Information##
##Data set generator##
Hanne Møller, Simon Saxegård
##Access and use restrictions##
##References##
NORSUS report OR 55.20 (closed)
##
[Tags] => Access_open, GLAD, Sector_Materials production / Food and renewable raw materials
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[Name] => Low alloyed steel, casted; Primary and secondary production, at plant; 95% primary material, 5% recycled material
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[ActivityName] => Low alloyed steel, casted; Primary and secondary production, at plant; 95% primary material, 5% recycled material
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[GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted; Primary and secondary production, at plant; 95% primary material, 5% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Asia and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to Asian heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Asia (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Asia
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Asian production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
[Tags] => Sector_Materials production / Metals and semimetals, Access_open, GLAD
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[Name] => Potato salad, packaged, at consumer
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[CreatedOn] => 2023-04-12T11:12:57.3864103
[ModifiedOn] => 2023-04-12T11:14:08.9655644
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[ActivityName] => Potato salad, packaged, at consumer
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[GeneralComment] => ##General Information##
##Product name##
Potato salad, packaged, at consumer
##Reference flow##
1 kg of packaged product
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of potato salad, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
The amount of waste from the production site was not included in the analysis as it was assessed to be very low.
Disposal of consumer packaging is based on the average waste system for household waste for 2019 reported by Grønt Punkt Norway, 2020, plastic: 23.5% material recycling, 76,5% combustion with energy utilization.
##Biogenic carbon##
##Use advice of the dataset##
It is important to remember that if this dataset represents potato salad produced in Scandinavia and if used as a proxy for potato salad produced elsewhere it is important to note that the dataset may not be representative for that region. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
Several raw materials are included in potato salad production, each of which has its own production process and is transported to the production site in Norway. Specific datasets were produced for some ingredients and for others Agrifootprint and Ecoinvent were used. The type of transport depends on where the raw material is produced. Production and transport of packaging is also included. In the manufacturing of potato salad, energy is used to process the raw materials for the salad, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian production of potato salad
The data quality and representativeness are good for most processes. Data from Ecoinvent, Agri-footprint and WorldFood Database have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Specific data is used for the amount of raw materials, amounts of packaging material and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
The reference year for the potato salad production process is 2020. For upstream and downstream processes, the time representativeness varies, as Ecoinvent 3.8, Agrifootprint 2.0 and World Food Database, has been used.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for potato salad.
##LCI allocation methods##
Economic allocation has been used.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Anna Woodhouse, Erik Svanes
##Access and use restrictions##
NORSUS report OR.01.22 (Closed)
##
[Tags] => Access_open, GLAD, Sector_Materials production / Food and renewable raw materials
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[ActivityName] => Beef from dual purpose production; at farmgate
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[GeneralComment] => ##General Information##
##Product name##
Beef from dual purpose production; at farmgate; Norway
##Reference flow##
1 kg beef slaughter weight
##System boundaries##
Cradle to farm gate, includes fertiliser production and application, imported and domestic feed production, average farm data of dual-purpose production in terms of scale, feeding regimes and manure management systems.
##Wastes and end-of-life##
Wastes and end-of-life is not included.
##Biogenic carbon##
Biogenic carbon is from methane from enteric fermentation and manure storage. Soil organic carbon is not included.
##Use advice of the dataset##
When using data, it is important to be aware of the use of allocation method used, which can greatly affect the results. For feed and plant production, economic allocation has been used for co-products on the farm (e.g., rapeseed oil and rapeseed meal) and follows the PEFCR for feed (FEFAC, 2018).
Allocation between milk and meat on the farm is based on biophysical principles according to PEFCR Dairy products (European Dairy Association, 2018) and which has also previously been recommended by the International Dairy Federation. Ecoinvent 3.9.1 (cut-off by classification) and Agrifootprint 6.3 is used for the background data.
##Technological representativeness##
##Technology description##
The data represented typical Norwegian cattle herds of dual-purpose production in terms of scale and feeding regimes with production levels corresponding to average milk yields, growth performance, and beef production in Norway in 2021. The dual-purpose dairy production was based on production data of Norwegian Red (NR) obtained from the Norwegian Dairy Herd Recording System (NDHRS). Milk yield 8,550 kg FPCM dairy cow-1. Carcass production per cow including bulls and surplus heifers was 273 kg per year, bull age at slaughter 17.8 months, heifer age at calving 25.6 month. Concentrate feed intake per dairy cow was 2,614 kg DM (dry matter)/LU (livestock unit).
Energy requirements for all animal categories (cows, heifer, bull) were obtained using the Nordic feed evaluation system (NorFor; Volden, 2011) through TINE Optifor. Diet compositions for dairy cattle were available through TINE Mjølkonomi®, an economic tool for milk producers. The composition of typical concentrate feeds for dairy cows, heifers and young bulls was given by Felleskjøpet Fôrutvikling. Manure was assumed to be deposited on pasture during summer (pasture season typically from mid-May to mid-September). During housing, the proportion of manure management system was considered for each animal category. Manure was applied on ley area during spring. Silage dry matter (DM) yield (6320 kg DM ha-1) and the use of fertilizer (159 kg N ha-1), lime (51.9 kg ha-1), herbicides (1.9 L glyphosate ha-1 and 560 ml MCPA; 2-methyl-4-chlorophenoxyacetic acid ha-1) (NIBIO, 2018; Statistics Norway, 2012; TINE, 2022), and diesel (8.02 L ha-1) (Korsaeth et al., 2016) for a typical Norwegian farm was made available through TINE. The ley and pasture area corresponded to the calculated forage requirements.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for average dual-purpose milk and beef production in Norway. There is large variation between the regions and therefore these average data should not be used in analyses at regional or farm level.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
The reference year for the beef production is 2021, using time specific data for carcass production, milk yield concentrate feeds. For background processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Background data based on ecoinvent cut-off.
##LCI allocation methods##
For feed production, economic allocation has been used for co-products on the farm and follows the PEFCR for feed. For allocation between meat and milk products at the farm, biophysical allocation was used according to the PEFCR for dairy products (European Dairy Association, 2018): using kg live weight sold per year converted to carcass weight and the mass of fat and protein corrected milk (FPCM).
##Other comments on methods approaches##
Emissions of methane, dinitrogen monoxide, ammonia, nitrogen dioxide and carbon dioxide are calculated based on the IPCC (2006). The emissions include enteric fermentation, manure storage, direct and indirect emissions from application of manure and mineral fertiliser, leaching and runoff and crop residues returned to soils and emissions from application of limestone.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied.
##Infrastructure/capital goods##
Some purchased services might be left out.
##Data treatment and extrapolations principles##
##Data collection period##
2019-2022
##Administrative Information##
##Data set generator##
Hanne Møller, Stine Samsonstuen
##Access and use restrictions##
open
##References##
NORSUS report: Life cycle assessment of meat -climate change, on assignment for Nortura
##
[Tags] => Access_open, GLAD, Sector_Materials production / Food and renewable raw materials
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[ActivityName] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state
[Type] => System
[GeneralComment] => ##General Information##
##Product name## Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state
##Reference flow## 1kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 – 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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[ActivityName] => Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
[Type] => System
[GeneralComment] => ##General Information##
##Product name##
Wood chips, beech; wet, manual harvest, hardwood forestry; at forest road
##Reference flow##
1 kg DM
##System boundaries##
The dataset represents manual harvest of beech in France. The dataset covers stand establishment (production of seedlings in an unheated greenhouse and planting), tending and cleaning, thinning, manual final harvest and chipping. Construction and maintenance of infrastructure, like machinery and forest roads, are included. The system ends with wood chips at forest road. The activities over one rotation period are included.
The activity starts with site preparation assuming establishment of the forest via planting, including seedling production and covers all process related to forest management, including site preparation, planting, tending, young growth tending, clearing, thinning, and harvesting operations including the processing of wood fuel to chips, bundles and chopped wood (logs for energy) over one rotation period. It also covers the maintenance and construction of forest roads. This activity ends with the assortments at the forest road and includes eventual drying before transportation.
##Wastes and end-of-life##
##Biogenic carbon##
Biogenic carbon included as resource input from nature (uptake of carbon dioxide in air)
##Use advice of the dataset##
The dataset should be used to analyze the impact from beech wood chips that originate from thinning operations in France. Measured as dry mass
##Technological representativeness##
##Technology description##
For the chipping in the stand, a productivity of 25 m3 bulked/PMH is assumed (based on Cremer & Velazquesz). For wood chips chipped in the stand, a productivity of the forwarding of 70 Sm3 bulked/PMH with a diesel consumption of 9.5 l/h of the forwarder is assumed. 24.4% thinning (mechanical) and 76.6% (manual) final harvest
##Technology Quality level##Fair
##Geographical representativeness##
##Location## France (FR)
##Geographical representativeness description##
The dataset is based on dataset on harvest of hardwood in Germany from Wernet et al. (2016). The dataset has been adjusted to reflect beech forests, and beech forest management in France. The adjustments include changes in the harvesting methods to mirror the relative share of manual harvesting vs mechanized harvesters in the eastern parts on France, the allocation between clear-cut and thinning, and the area used for different practices (Pelletier, 2017).
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2017
##Time representativeness description## Data represent 2017 for the specifications of French beech management while original ecoinvent data are largely based on a data source from 2009. Data are still considered representative due to small developments in forest management and harvest operations.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation based on mass.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
All known inputs are included.
##Infrastructure/capital goods##
Construction and maintenance of roads and machinery are included
##Data treatment and extrapolations principles##
In communication with French Institute of Technology for Forest-based and Furniture Sectors (FCBA), it was recommended to use the ecoinvent (v3.8) dataset for beech from Germany as representative for beech from the eastern parts of France. The ecoinvent database provides well documented process data and is widely used in Europe. The data set “hardwood forestry, beech, sustainable forest management” was selected. This dataset covers the production and harvesting of 1 m3 of stemwood, beech, solid, under bark, plus the relative share of energy wood from slash from sustainable forest management as the prevailing management practices in Germany (Wernet et al., 2016). Changes were made to allocation principles, harvesting regimes, and area used to reflect French beech forestry (Pelletier, 2017).
##Data collection period##
2017 - 2021
##Administrative Information##
##Data set generator##
Andreas Brekke
##Access and use restrictions##
Open
##References##
Cremer, T. and B. Velazques-Marti (2007): Evaluation of two harvesting systems for the supply of woodchips in Norway spurce forest affected by bark beetle. Croation Journal of Forest Engineering, 28(2): 145-155.
Pelletier, C. (2017). Analyse environnementale et économique des filières bois-énergie. Université de Lorraine, Retrieved from https://tel.archives-ouvertes.fr/tel-01765854/document (2017LORR0331)
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., & Weidema, B. (2016). The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, 21(9), 1218-1230. doi:10.1007/s11367-016-1087-8
##
[Tags] => Sector_Materials production / Wood, Access_open, GLAD
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[ActivityName] => Low alloyed steel, casted; Primary and secondary production, at plant; 90% primary material, 10% recycled material
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[GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted; Primary and secondary production, at plant; 90% primary material, 10% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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[Name] => Heat, for district heating, Norwegian production mix, at plant
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[ActivityName] => Heat, for district heating, Norwegian production mix, at plant
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[GeneralComment] => "##General Information##
##Product name##
Heat, for district heating, Norwegian production mix, at plant
##Reference flow##
1 kWh
##System boundaries##
Production mix of district heat in Norway in 2021, based on information on energy carriers from Norsk Fjernvarme. The activity starts with acquisition of energy carriers, i.e., waste reception at gate, acquisition and transport of fossil oil and gas, harvesting and transport of woody biomass, and production, transformation from high to low voltage and transmission of electricity. The activity stops at district heat production facility (i.e. distribution is not included).
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset represents the district heat production mix in Norway in the year 2021. Data on heat production and share of different energy carriers are collected from www.fjernkontrollen.no. ecoinvent 3.8 allocation, cut-off by classification is used for the background data.
##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
Allocation cut-off by classification in background data.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure is included
##Data treatment and extrapolations principles##
The energy carriers are given by Norsk Fjernvarme. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Data collection period##
2021
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2022). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.09.2022.
##
[Tags] => Sector_Energy carriers and technologies / Heat and steam, Access_open, GLAD
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process:
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[GeneralComment] => ##General Information##
##Product name##
Hot rolled steel; Primary and secondary production, at plant; 90% primary material, 10% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with the rolling of steel sheets.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the rolling process, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, low voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for low voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Transformation from high via medium to low voltage is included. Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
This dataset represent low voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level. Ecoinvent 3.8 has been used for the background processes.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
##
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[GeneralComment] => ##General Information##
##Product name##
CO2 from upgrading of biogas; from food waste and manure; production, distribution, and use
##Reference flow##
1 kg
##System boundaries##
CO2 from upgrading of biogas is considered as recyclable material, in line with the methodology of ecoinvent database cut off by classification. This means that CO2 is seen as a recyclable waste flow from production of biogas, and only the distribution and use phase is included in the inventory.
##Wastes and end-of-life##
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##Biogenic carbon##
CO2 is emitted as biogenic CO2 during use phase, and is reported separately in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling the use of CO2 from upgrading of biogas distributed in a pipeline. The dataset does not include compression of the CO2.
##Technological representativeness##
##Technology description##
1 kg CO2 produced at an anaerobic digestion plant, transported in pipeline.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county. The CO2 is used in a greenhouse located in close proximity with the biogas plant
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
CO2 is considered as a recyclable flow from production of biogas. Impacts from anaerobic digestion is attributed to production of biogas and digestate, or to the organic waste treatment. Upgrading of the biogas is attributed to biogas production and is considered outside the system boundaries of CO2 as a product.
##Other comments on methods approaches##
Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Piping infrastructure in distribution of CO2 is included
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
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[GeneralComment] => ##General Information##
##Product name##
Heat, for district heating, Norwegian production mix, without waste incineration or ambient heat, at plant
##Reference flow##
1 kWh
##System boundaries##
Production mix of district heat in Norway in 2021, based on information on energy carriers from Norsk Fjernvarme, minus heat from waste combustion and ambient heat from industry. The activity starts with acquisition of energy carriers, i.e., acquisition and transport of fossil oil and gas, harvesting and transport of woody biomass, and production, transformation from high to low voltage and transmission of electricity. The activity stops at district heat production facility (i.e. distribution is not included).
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
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##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
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##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
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##Data collection period##
2021
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2022). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.09.2022.
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[Name] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
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[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Piano; without water; at plant; in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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##Reference flow##
1 kg
##System boundaries##
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##Wastes and end-of-life##
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##Biogenic carbon##
Biogenic emissions from the use of biogas are accounted for by using a “biogenic” substance such that these can be distinguished from fossil emissions by the impact assessment method.
##Use advice of the dataset##
This dataset should be used to model the impact from residual waste collection in Romerike, Norway. Since residual waste is going to a residual waste sorting facility, only glass & metal and paper & cardboard are collected separately. Collection of these waste fraction is not included in the dataset. Ecoinvent 3.8 is used to model the background processes.
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##Technology description##
77% of the residual waste is collect by waste collection vehicles on biogas, 23% is collected by diesel vehicles. Direct emissions from biogas are modelled using natural gas emission profiles as proxy, but these are adjusted to the lower methane content of biogas.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The dataset is representative for the Romerike district in south-eastern Norway
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2016
##Time representativeness description##
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##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Besides the allocation principles carried out by ecoinvent cut-off by classification, no other allocation methods are used
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (road, cars, etc.) are included
##Data treatment and extrapolations principles##
None
##Data collection period##
2017
##Administrative Information##
##Data set generator##
Pieter Callewaert
##Access and use restrictions##
Open
##References##
Callewaert, P. (2017). Analysing the sustainability performance and critical improvement factors of urban municipal waste systems. (Masters). NTNU, Trondheim. Retrieved from http://hdl.handle.net/11250/2454900
##
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[Name] => Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
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[Name_BaseName] => Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
[GeneralComment] => ##General Information##
##Product name##
Bark extract; without water, in 98% solution state; from Norway Spruce biorefinery, at plant
##Reference flow##
1 kg dry matter
##System boundaries##
The system includes all upstream processes (extraction, transport and refinement of raw materials and energy) and processes taking place at the biorefinery. Energy and resource use in offices is included. Work travels and commuting to work is not included. Emissions from combustion of waste for steam production are not allocated the user of the heat, rather the producer of the waste, according to the polluter pays principle.
##Wastes and end-of-life##
Treatment of waste is included. Recycling credits outside the biorefinery are not included.
##Biogenic carbon##
Uptake and emissions of biogenic carbon are included.
##Use advice of the dataset##
The aim of the dataset is to document the environmental properties for a product under development. The product was developed in the BACS project (short for BioActive Compounds from Spruce), where the underlying idea was to develop sustainable value added products, processes and applications for compounds extracted from Norway spruce that are bioactive or will stimulate and improve the bioactivity of other compounds in formulations. Preliminary LCA results were used as input to the innovation process by identifying hotspots. The dataset was developed in the WP 8 work package of the BACS research project funded by the Research Council of Norway (consortium agreement no. 295501). Borregaard was the project owner. Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Technological representativeness##
##Technology description##
Modelling of the foreground system of the biorefinery is based on specific data from Borregaard Sarpsborg, Norway. For the main biorefinery (processing of bark for use as input to the BACS biorefinery system), real data for the full-scale biorefinery have been used, hence the data represent the exact technology. For the BACS biorefinery system (processing bark to bark extract), calculated data based on theoretical information, estimates and knowledge from similar processes at the main biorefinery have been used. Data for the input chemicals and processes upstream the main biorefinery at Borregaard Sarpsborg have been found in generic databases.
##Technology Quality level##
Very good.
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Foreground data are specific for the product dataset (Borregaard biorefinery, Sarpsborg, Norway). As far as possible, background data representative for Norwegian conditions have been used.
##Geographical Quality level##
Very good.
##Time related representativeness##
##Reference year##2021
##Time representativeness description##
Specific data are from 2019 and 202. Annual data have been used for the main biorefiney, except for steam, where the input of electricity and natural gas was averaged over a 7-year period. For the BACS biorefinery system, hourly data have been used. Data for production of other raw materials, energy, transport and waste treatment options have been found in the ecoinvent database. Choosing the most correct technology has been prioritised over newer data.
##Time Quality Level##
Very good.
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
For generic data, ecoinvent’s ‘allocation, cut-off by classification’ database have been used. For specific data at the biorefinery, allocation has been avoided as far as possible by analysing the processes on a detailed level. When necessary, energy allocation (based on dry matter content (DM)) has been used at the biorefinery.
##Other comments on methods approaches##
Economic allocation was found unsuitable because several of the allocations have included products that have no market value, and because over time, no product in the main biorefinery acts as the single driving force of the system.
##Completeness##
##Cut off for mass or energy flows##
100% coverage
##Infrastructure/capital goods##
Included
##Data treatment and extrapolations principles##
All results and allocations have been based on dry matter (DM) in the internal flows and final products. Ethanol has been accounted for as DM in both internal flows and final products. For steam input and output, data from the main biorefinery have been used (assumed as ‘large’ compared with the BACS biorefinery system, hence not affected by the consumption in the BACS biorefinery system). Delivery of steam from the BACS biorefinery system back to the main biorefinery is included as avoided burdens.
##Data collection period##
2020-2022
##Administrative Information##
##Data set generator##
Ingunn Saur Modahl
##Access and use restrictions##
Open
##References##
Modahl, I. S. and E. Soldal (2021). The 2019 LCA of products from Borregaard, Sarpsborg. Report no OR.14.21. Link: https://norsus.no/en/publikasjon/the-2019-lca-of-products-from-borregaard-sarpsborg/. Fredrikstad, NORSUS.
Modahl, I.S., Brekke, A. and Valente, C. (2022): Sustainability of BACS products (BioActive Compounds from Spruce). Report no. OR.17.22 (confidential). Fredrikstad, Norway.
##
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[Name] => Stainless steel, casted; Primary and secondary production, at plant; 30% primary material, 70% recycled material
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[Name_BaseName] => Stainless steel, casted; Primary and secondary production, at plant; 30% primary material, 70% recycled material
[GeneralComment] => ##General Information##
##Product name##
Stainless steel, casted; Primary and secondary production, at plant; 30% primary material, 70% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of stainless steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent European production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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[GeneralComment] => ##General Information##
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Compressed upgraded biogas (biomethane/CBG); from food waste and manure; production, distribution, and use, minimum 97% methane
##Reference flow##
1 kWh
##System boundaries##
Includes pre-treatment of food waste at the biogas facility, anaerobic digestion, upgrading, distribution by truck and use of biogas in a combustion engine. Collection of food waste is not included in the dataset. Collection of manure is included in the system boundaries. Incineration and transport of reject from pre-treatment of food waste is included. No substitution of avoided products included.
##Wastes and end-of-life##
Includes transport and combustion of the pre-treatment reject.
##Biogenic carbon##
Biogenic methane and CO2 emissions are reported separately by in the LCI using biogenic substances.
##Use advice of the dataset##
This dataset should be used when modelling the use of biogas in transport applications. When using manure for biogas production, the storage time (and related emissions) of manure is reduced. This effect is not included in the dataset. Furthermore, this dataset focusses on the impact on climate change. One should therefore be cautious in using this dataset to analyse other impact categories, especially toxicity.
##Technological representativeness##
##Technology description##
1 kWh upgraded biogas from household waste and manure. As input material in the biogas facility, approximately 50% comes from household waste and 50% from manure sourced from surrounding farms. Production process: 1) Mechanical pretreatment to remove unwanted objects and reduction of particle size: screw press, grinder, hydro cyclone. 2) Sterilization process for 1 hour at 70 °C 3) Anaerobic digestion: Hydraulic retention time (HRT) 36.5 days. 4) Upgrading: water scrubber. Emissions from driving: EURO 5.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county. Manure is transported from local farms.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Processes that are common to biogas and biofertilizer from digestate (transport of manure, pre-treatment, incineration of pre-treatment reject, anaerobic digesting) are allocated between the end products based on dry mass. Treatment (incineration) of plastic in reject from pre-treatment is not included.
##Other comments on methods approaches##
Ecoinvent 3 – allocation, cut-off by classification, version 3.8 was used as background database.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure (biogas plant) included, based on literature data from Brogaard et al., 2015, Quantifying capital goods of organic waste treatment, Table 6. Service life of the biogas plant is assumed to be 30 years.
##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
Brogaard, L. K., Petersen, P. H., Nielsen, P. D. & Christensen, T. H. (2015). Quantifying capital
goods for biological treatment of organic waste. Waste management & research: the journal
of the International Solid Wastes and Public Cleansing Association, ISWA, 33(2), 96-106.
https://doi.org/10.1177/0734242X14565212
##
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##Wastes and end-of-life##
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##Biogenic carbon##
Not relevant
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This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter), the raw material (pig iron) production and the rolling process, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
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##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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##Reference flow##
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##System boundaries##
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##Wastes and end-of-life##
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##Biogenic carbon##
Not relevant
##Use advice of the dataset##
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· The shares of recycled and virgin materials are adjusted based on the use of technologies in the country (World steel association, 2021).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies adjusted to represent European production.
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Good
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##Reference year##
2021
##Time representativeness description##
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##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation done by ecoinvent
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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[Name_BaseName] => Anaerobic digestion, food waste, at plant
[GeneralComment] => ##General Information##
##Product name##
Anaerobic digestion, food waste, at plant
##Reference flow##
1 tonne
##System boundaries##
Includes pre-treatment of food waste at the biogas facility and the anaerobic digestion process. The pre-treatment includes incineration of reject which consist of organic material, plastic collection bags and mis-sortings. The inventory does not include downstream processes for the secondary products (biogas, digestate and CO2 as a product) or avoided emissions as a consequence of the secondary products substituting other products. Transport of the organic waste to the biogas plant is not included.
##Wastes and end-of-life##
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##Biogenic carbon##
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##Use advice of the dataset##
This dataset should be used when modelling waste treatment of organic waste by anaerobic digestion. The transport of the organic waste to the biogas plant is not included, as this is dependent on where the waste is generated. If the purpose of the study is to compare treatment options (e.g. anaerobic digestion and energy recovery), the avoided emissions obtained when the generated secondary products substitute other products should be included. 1 tonne of food waste can be assumed to generate: 131 Nm3 raw (non upgraded) biogas, 6.7 kg N of liquid biofertilizer and 5.2 kg CO2 (delivered to greenhouse).
##Technological representativeness##
##Technology description##
Production process: 1) Mechanical pretreatment to remove unwanted objects and reduction of particle size: screw press, grinder, hydro cyclone. 2) Sterilization process for 1 hour at 70 °C 3) Anaerobic digestion: Hydraulic retention time (HRT) 36.5 days.
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The biogas facility is located in the Vestfold and Telemark county.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2018
##Time representativeness description##
Report was published in 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
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Cut off for mass or energy flows
##Other comments on methods approaches##
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##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
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##Data treatment and extrapolations principles##
##Data collection period##
2018
##Administrative Information##
##Data set generator##
Kari-Anne Lyng, NORSUS
##Access and use restrictions##
Open
##References##
Lyng, K.-A., Saxegård, S., 2020, Livsløpsvurdering av produktene og tjenestene til Den Magiske Fabrikken. OR.23.20. NORSUS, Kråkerøy. Available from: https://norsus.no/publikasjon/livslopsvurdering-av-produktene-og-tjenestene-til-den-magiske-fabrikken/ (In Norwegian)
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[Name_BaseName] => Margarine, 80% fat, packaged, at consumer
[GeneralComment] => ##General Information##
##Product name##
Margarine, 80% fat, packaged, at consumer
##Reference flow##
1 kg of packaged product
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of margarine, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
The amount of waste from the production site amounts to 88.7% for material recycling, and 11.3% for energy recycling. The amount of waste is allocated to the products based on the economic value of total turnover.
Disposal of consumer packaging is based on the average waste system for household waste for 2019 reported by Grønt Punkt Norway, 2020, plastic: 33.5% material recycling, 66.4% incineration; cardboard: 50.3% material recycling and 49.6% combustion with energy utilization.
##Biogenic carbon##
##Use advice of the dataset##
If this dataset is used as a proxy for other margarine products, it is important to take the fat content of the current product into account. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
A number of raw materials are included in margarine production, each of which has its own production process and is transported to margarine production in Norway. Generic data is used for raw materials production, but the data is adapted to the country of origin's energy sources and specific data where this is stated by the manufacturer. The type of transport depends on where the raw material is produced. Transport of the raw materials can be divided into transport from the farm to raw material processing and to margarine production. The logistics for the various raw materials is complex and some raw materials are not grown close to where the raw material is processed. The overseas agricultural products are transported from the farm or plantation to the first processing stage and from there to the port to be shipped to Europe, where further processing takes place. Production and transport of packaging is also included. In the margarine production, energy is used to process the raw material for margarine, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian margarine production, 60% fat
The data quality and representativeness are good for most processes. Data from Ecoinvent and Agri-footprint have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Country-specific data is used for the raw materials that are included with more than 1%. Specific data is used for the amount of raw materials and packaging and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2019
##Time representativeness description##
The reference year for the margarine production process is 2019. For upstream and downstream processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for margarine.
##LCI allocation methods##
For raw materials for plant and feed production, economic allocation has been used for co-products on the farm (e.g. rapeseed oil and rapeseed meal) and follows the PEFCR for feed.
Allocation between milk and meat on the farm was based on biophysical principles according to PEFCR Dairy products. For the production of margarine, inflows and outflows were allocated based on the products' dry matter content, according to PEFCR Dairy products.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2019
##Administrative Information##
##Data set generator##
Hanne Møller, Simon Saxegård
##Access and use restrictions##
##References##
NORSUS report OR 55.20 (closed)
##
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[Name_BaseName] => Faba beans (Vicus Fabia); dried, 15% water; at farmgate
[GeneralComment] => ##General Information##
##Product name##
Faba beans (Vicus Fabia); dried, 15% water; at farmgate
##Reference flow##
1 kg dried faba beans
##System boundaries##
The system ends at farmgate. Growing and drying included. Includes production of fertilizers, pesticides, diesel, limestone, seeds, machinery, buildings. Also includes the effects of soil mineralisation, calculated with the ICBM model, as CO2 emissions and N2O emissions.
##Wastes and end-of-life##
##Biogenic carbon##
Biogenic carbon is not accounted for separately.
##Use advice of the dataset##
In Norwegian: Åkerbønner, bondebønner, hestebønner.
Precrop effects are not included.
##Technological representativeness##
##Technology description##
The process is representative for conventional agriculture. The technology is representative for faba beans growing in Norway. Due to lack of data, a simplified model for drying has been used. Also, fuel use has been estimated using a model (the DRIFT model) using field operations data (ploughing, harrowing, spraying, etc). Expert advice has been used to corroborate data from survey of farmers.
##Technology Quality level## Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Data is representative for Norway since it’s based on a survey conducted in Norway and supported by expert advice from NLR and NIBIO. All regions were faba beans are grown in Norway are included.
##Geographical Quality level## Very Good
##Time related representativeness##
##Reference year##
2017
##Time representativeness description##
Farmers and experts are asked to give data on a representative year. They were asked to give the answers for 2017 if that was a representative year.
##Time Quality Level##
Very Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
No allocation done because plant residues are left on the field and worked into the soil.
##Other comments on methods approaches##
Emissions of dinitrogen monoxide, ammonia, nitrogen dioxide and carbon dioxide are calculated based on the IPCC (2006). The emissions include direct and indirect emissions from application of mineral fertiliser, leaching and runoff and crop residues returned to soils and emissions from application of limestone. Changes in soil organic carbon (SOC) is modelled using the ICBM method.
##Completeness##
##Cut off for mass or energy flows##
Very small impacts such as packaging for seeds, pesticides and fertilizer not included.
##Infrastructure/capital goods##
Included buildings and machinery for primary production. Building is modelled on an actual building. Machinery impacts comes from Ecoinvent but based on a report showing the amount of machinery on a number of Norwegian farms.
##Data treatment and extrapolations principles##
Based on survey and expert advice.
##Data collection period##
2017-2018
##Administrative Information##
##Data set generator##
Erik Svanes
##Access and use restrictions##
Closed, please contact pieter@norsus to get access to the dataset.
##References##
##
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
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##Location##
Asia
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Good
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##Reference year##
2020
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
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2020
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See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
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Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
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[Name_BaseName] => Electricity; Norwegian consumption mix, high voltage, to consumer
[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, high voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for high voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
These datasets represent high voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
##
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[Name_BaseName] => Milk from dual purpose production; at farmgate
[GeneralComment] => ##General Information##
##Product name##
Milk production; at farmgate; Norway
##Reference flow##
1 kg fat and protein corrected milk (FPCM)
##System boundaries##
Cradle to farm gate, includes fertiliser production and application, imported and domestic feed production, average farm data of dual-purpose production in terms of scale, feeding regimes and manure management systems.
##Wastes and end-of-life##
Wastes and end-of-life is not included.
##Biogenic carbon##
Biogenic carbon is from methane from enteric fermentation and manure storage. Soil organic carbon is not included.
##Use advice of the dataset##
When using data, it is important to be aware of the use of allocation method used, which can greatly affect the results. For feed and plant production, economic allocation has been used for co-products on the farm (e.g., rapeseed oil and rapeseed meal) and follows the PEFCR for feed (FEFAC, 2018).
Allocation between milk and meat on the farm is based on biophysical principles according to PEFCR Dairy products (European Dairy Association, 2018) and which has also previously been recommended by the International Dairy Federation. Ecoinvent 3.9.1 (cut-off by classification) and Agrifootprint 6.3 is used for the background data.
##Technological representativeness##
##Technology description##
The data represented typical Norwegian cattle herds of dual-purpose production in terms of scale and feeding regimes with production levels corresponding to average milk yields, growth performance, and beef production in Norway in 2021. The dual-purpose dairy production was based on production data of Norwegian Red (NR) obtained from the Norwegian Dairy Herd Recording System (NDHRS). Milk yield 8,550 kg FPCM dairy cow-1. Carcass production per cow including bulls and surplus heifers was 273 kg per year, bull age at slaughter 17.8 months, heifer age at calving 25.6 month. Concentrate feed intake per dairy cow was 2,614 kg DM (dry matter)/LU (livestock unit).
Energy requirements for all animal categories (cows, heifer, bull) were obtained using the Nordic feed evaluation system (NorFor; Volden, 2011) through TINE Optifor. Diet compositions for dairy cattle were available through TINE Mjølkonomi®, an economic tool for milk producers. The composition of typical concentrate feeds for dairy cows, heifers and young bulls was given by Felleskjøpet Fôrutvikling. Manure was assumed to be deposited on pasture during summer (pasture season typically from mid-May to mid-September). During housing, the proportion of manure management system was considered for each animal category. Manure was applied on ley area during spring. Silage dry matter (DM) yield (6320 kg DM ha-1) and the use of fertilizer (159 kg N ha-1), lime (51.9 kg ha-1), herbicides (1.9 L glyphosate ha-1 and 560 ml MCPA; 2-methyl-4-chlorophenoxyacetic acid ha-1) (NIBIO, 2018; Statistics Norway, 2012; TINE, 2022), and diesel (8.02 L ha-1) (Korsaeth et al., 2016) for a typical Norwegian farm was made available through TINE. The ley and pasture area corresponded to the calculated forage requirements.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for average milk production in Norway. There is large variation between the regions and therefore these average data should not be used in analyses at regional or farm level.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
The reference year for the beef production is 2021, using time specific data for milk yield concentrate feeds. For background processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Background data based on ecoinvent cut-off.
##LCI allocation methods##
For feed production, economic allocation has been used for co-products on the farm and follows the PEFCR for feed. For allocation between meat and milk products at the farm, biophysical allocation was used according to the PEFCR for dairy products (European Dairy Association, 2018): using mass of fat and protein corrected milk (FPCM) and kg live weight sold per year converted to carcass weight.
##Other comments on methods approaches##
Emissions of methane, dinitrogen monoxide, ammonia, nitrogen dioxide and carbon dioxide are calculated based on the IPCC (2006). The emissions include enteric fermentation, manure storage, direct and indirect emissions from application of manure and mineral fertiliser, leaching and runoff and crop residues returned to soils and emissions from application of limestone.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied.
##Infrastructure/capital goods##
Some purchased services might be left out.
##Data treatment and extrapolations principles##
##Data collection period##
2019-2022
##Administrative Information##
##Data set generator##
Hanne Møller, Stine Samsonstuen
##Access and use restrictions##
open
##References##
NORSUS report: Life cycle assessment of meat -climate change, on assignment for Nortura
##
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[Name] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
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[Name_BaseName] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Forte; without water; at plant; in 10% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 10% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
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[Name_BaseName] => Waste treatment of household plastic packaging waste, put on market
[GeneralComment] => ##General Information##
##Product name##
Waste treatment of household plastic packaging waste, put on market.
##Reference flow##
1 tonne plastic packaging material put on market.
##System boundaries##
Starts with one tonne plastic packaging waste put on market in Norway. Approx. 50% of the PPW is discarded in the residual waste bin and is incinerated at a local incineration plant in Norway. The remaing part is either separated by households (36%) and collected by municipalities for further waste treatment, or sent to at a mixed-waste material recovery facility (18%). Separated PPW is transported by train to Germany for further sorting and recycling. Sorting residues are incinerated in Germany. The inventory does not include avoided emissions because of secondary products substituting other products.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset should be used when modelling treatment of household plastic packaging waste in Norway. Assumed material composition: PET bottles: 4.5%, HDPE: 6.6%, PP: 12.3%, PE film: 40.6%, PET trays: 7.8%, PS: 3.7%, PP and other film: 9.4%, Other rigid plastic: 1.6%, Black rigid plastic: 6.6%, PE laminate: 7.0%
If the purpose is to compare different waste treatment options (e.g. recycling and energy recovery), the avoided emissions should be included in each system. 1 tonne plastic packaging material results in the production of 102 kg PO, 24 kg PP, 4 kg PS, 4 kg PET, 19 kg HPDE and 74 kg LDPE granulate in addition to the production of 16555 MJ heat and 2474 MJ electricity in Norway and 1582 MJ heat and 754 MJ electricity in Germany.
##Technological representativeness##
##Technology description##
PPW is either source separated by households or collected together with mixed waste and separated in a mixed-waste MRF afterward. In the dataset, 82% of the generated PPW is covered by a system that relies on source separation, 18% is separated from mixed waste in a mixed-waste MRF. The separated PPW is sorted and recycled in three different facilities in Germany. More information about the facilities is available in Furberg et al. (2022).
##Technology Quality level##
Very good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
The Producer Responsibility Organisation (PRO) managing the system covers 80% of Norway population, the remaining 20% of the population (mainly Oslo) is covered by another PRO which is not included in the dataset. Site specific data is collected for the mixed waste MRFs in Norway and for the three facilities in Germany. For the background processes, ecoinvent v3.8 cut-off is used.
##Geographical Quality level##
Very good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Generated PPW, source separation efficiency, transport data and division of PPW between different treatment facilities in Germany is from 2020.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Generic building infrastructure is included, sorting machines are not included due to lack of data
##Data treatment and extrapolations principles##
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Pieter Callewaert
##Access and use restrictions##
Open
##References##
Furberg, A., Callewaert, P. & Lyng, K.-A. (2022). Life cycle assessment of household plastic waste treatment in Norway (OR 07.22). NORSUS. https://norsus.no/wp-content/uploads/OR-07.22-Life-cycle-assessment-of-household-plastic-waste-treatment-in-Norway.pdf
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[Name_BaseName] => Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state
[GeneralComment] => ##General Information##
##Product name##
Microfibrillated cellulose, Exilva Forte; without water; at plant; in 2% solution state
##Reference flow## 1 kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Forte grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway.
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 - 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
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[Name] => Electricity; Norwegian consumption mix, medium voltage, to consumer
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[Name_BaseName] => Electricity; Norwegian consumption mix, medium voltage, to consumer
[GeneralComment] => ##General Information##
##Product name##
Electricity; Norwegian consumption mix, medium voltage, to consumer
##Reference flow##
1 kWh
##System boundaries##
Consumption mixes for medium voltage electricity used in Norway in 2020, based on high voltage electricity mix given by NVE (2021) (Norwegian Water Resources and Energy Directorate). This high voltage mix includes electricity production in Norway and in the countries Norway exchanges electricity with (Denmark, Sweden, Netherlands, Finland and Russia).
Transformation from high to medium voltage is included. Distribution network, direct emissions to air (sulfur hexafluoride, dinitrogen monoxide and methane) and electricity losses are accounted for, using ecoinvent data.
##Wastes and end-of-life##
Waste treatment is included in the background processes. Market processes for end-of-life treatment of infrastructure have been used. Cut-off modelling has been used, and no recycling credits have been included.
##Biogenic carbon##
Biological methane emissions from reservoirs have been included. These are reported as biogenic in the LCI.
##Use advice of the dataset##
This dataset represents medium voltage electricity used at consumer in Norway. Users should be aware to choose the correct voltage level. Ecoinvent 3.8 has been used for the background processes.
##Technological representativeness##
##Technology description##
Electricity from gas, coal, other fossil (assumed oil) and other renewable (assumed wood chips) have been assumed from combined heat and power plants. Hydro power imported from Sweden has been assumed as 100% run-of-river. Norwegian hydro power has been assumed as 76% reservoir and 24% run-of-river according to Silva and Modahl (2019).
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Calculation of the high voltage electricity mix is based on the following by NVE (2020):
- Imported electricity is assumed produced in the country from which it is imported.
- Exported electricity from Norway is assumed produced in Norway.
- Import and export it is assumed that the electricity is crossing one country border only.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
To calculate the Norwegian high voltage mix, the net import and export for each country has been calculated by the hour throughout one year and then summed (NVE 2020). The electricity mix is specific for Norway in 2020, while the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network) are from ecoinvent 3.8 and may be older.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Not applicable in the foreground system. For the different electricity production processes (background processes), see ecoinvent 3.8 – allocation, cut-off.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
100% is included in the foreground modelling. For the background processes (electricity produced by different technologies using different energy carriers, transformation and transmission network), see ecoinvent 3.8 – allocation, cut-off.
##Infrastructure/capital goods##
Infrastructure for dams, turbines, cables, equipment, buildings, roads etc is included in the background processes (electricity produced by different technologies using different energy carriers, transformation and distribution network).
##Data treatment and extrapolations principles##
The dataset is based on the high voltage electricity consumption mix given by NVE (2020), which is then transformed to medium and low voltage by using ecoinvent 3.8 – allocation, cut-off background processes.
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Generated by Ingunn Saur Modahl
##Access and use restrictions##
##References##
NVE (2021): Hvor kommer strømmen fra? (Where does the electricity come from?) Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), published 15.06.2020, updated 02.07.2021. Assessed 20.09.2021. Link: https://www.nve.no/energiforsyning/kraftproduksjon/hvor-kommer-strommen-fra/?ref=mainmenu#:~:text=Norge%20er%20en%20del%20av,hovedsak%20kom%20fra%20fornybare%20energikilder
Silva, M. and Modahl, I.S. (2019): The inventory and life cycle data for Norwegian hydroelectricity. Ostfold Research (now NORSUS), AR 01.19, public, May 2019 (based on AR 02.15 public memo). Link: https://norsus.no/publikasjon/the-inventory-and-life-cycle-data-for-norwegian-hydroelectricity/
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[Name] => Knot extract; without water, in 60% solution state; from Norway Spruce biorefinery, at plant
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[GeneralComment] => ##General Information##
##Product name##
Knot extract; without water, in 60% solution state; from Norway Spruce biorefinery, at plant
##Reference flow##
1 kg dry matter
##System boundaries##
The system includes all upstream processes (extraction, transport and refinement of raw materials and energy) and processes taking place at the biorefinery. Energy and resource use in offices is included. Work travels and commuting to work is not included. Emissions from combustion of waste for steam production are not allocated the user of the heat, rather the producer of the waste, according to the polluter pays principle.
##Wastes and end-of-life##
Treatment of waste is included. Recycling credits outside the biorefinery are not included.
##Biogenic carbon##
Uptake and emissions of biogenic carbon are included.
##Use advice of the dataset##
The aim of the dataset is to document the environmental properties for a product under development. The product was developed in the BACS project (short for BioActive Compounds from Spruce), where the underlying idea was to develop sustainable value added products, processes and applications for compounds extracted from Norway spruce that are bioactive or will stimulate and improve the bioactivity of other compounds in formulations. Preliminary LCA results were used as input to the innovation process by identifying hotspots. The dataset was developed in the WP 8 work package of the BACS research project funded by the Research Council of Norway (consortium agreement no. 295501). Borregaard was the project owner. Ecoinvent 3 – allocation, cut-of by classification, version 3.8 was used as backgrund database.
##Technological representativeness##
##Technology description##
Modelling of the foreground system of the biorefinery is based on specific data from Borregaard Sarpsborg, Norway. For the main biorefinery (processing of knots for use as input to the BACS biorefinery system), real data for the full-scale biorefinery have been used, hence the data represent the exact technology. For the BACS biorefinery system (processing knots to knot extract), calculated data based on theoretical information, estimates and knowledge from similar processes at the main biorefinery have been used. Data for the input chemicals and processes upstream the main biorefinery at Borregaard Sarpsborg have been found in generic databases.
##Technology Quality level##
Very good.
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Foreground data are specific for the product dataset (Borregaard biorefinery, Sarpsborg, Norway). As far as possible, background data representative for Norwegian conditions have been used.
##Geographical Quality level##
Very good.
##Time related representativeness##
##Reference year##2021
##Time representativeness description##
Specific data are from 2019 and 2021. Annual data have been used for the main biorefiney, except for steam, where the input of electricity and natural gas was averaged over a 7-year period. For the BACS biorefinery system, hourly data have been used. Data for production of other raw materials, energy, transport and waste treatment options have been found in the ecoinvent database. Choosing the most correct technology has been prioritised over newer data.
##Time Quality Level##
Very good.
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
For generic data, ecoinvent’s ‘allocation, cut-off by classification’ database have been used. For specific data at the biorefinery, allocation has been avoided as far as possible by analysing the processes on a detailed level. When necessary, energy allocation (based on dry matter content (DM)) has been used at the biorefinery.
##Other comments on methods approaches##
Economic allocation was found unsuitable because several of the allocations have included products that have no market value, and because over time, no product in the main biorefinery acts as the single driving force of the system.
##Completeness##
##Cut off for mass or energy flows##
100% coverage
##Infrastructure/capital goods##
Included
##Data treatment and extrapolations principles##
All results and allocations have been based on dry matter (DM) in the internal flows and final products. Ethanol has been accounted for as DM in both internal flows and final products. For steam input and output, data from the main biorefinery have been used (assumed as ‘large’ compared with the BACS biorefinery system, hence not affected by the consumption in the BACS biorefinery system). Delivery of steam from the BACS biorefinery system back to the main biorefinery is included as avoided burdens.
##Data collection period##
2020-2022
##Administrative Information##
##Data set generator##
Ingunn Saur Modahl
##Access and use restrictions##
Open
##References##
Modahl, I. S. and E. Soldal (2021). The 2019 LCA of products from Borregaard, Sarpsborg. Report no OR.14.21. Link: https://norsus.no/en/publikasjon/the-2019-lca-of-products-from-borregaard-sarpsborg/. Fredrikstad, NORSUS.
Modahl, I.S., Brekke, A. and Valente, C. (2022): Sustainability of BACS products (BioActive Compounds from Spruce). Report no. OR.17.22 (confidential). Fredrikstad, Norway.
##
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[Name] => Margarine, 60% fat, packaged, at consumer
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[GeneralComment] => ##General Information##
##Product name##
Margarine, 60% fat, packaged, at consumer
##Reference flow##
1 kg of packaged product.
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of margarine, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
The amount of waste from the production site amounts to 88.7% for material recycling, and 11.3% for energy recycling. The amount of waste is allocated to the products based on the economic value of total turnover.
Disposal of consumer packaging is based on the average waste system for household waste for 2019 reported by Grønt Punkt Norway, 2020, plastic: 33.5% material recycling, 66.4% incineration; cardboard: 50.3% material recycling and 49.6% combustion with energy utilization.
##Biogenic carbon##
##Use advice of the dataset##
If this dataset is used as a proxy for other margarine products, it is important to take the fat content of the current product into account. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
A number of raw materials are included in margarine production, each of which has its own production process and is transported to margarine production in Norway. Generic data is used for raw materials production, but the data is adapted to the country of origin's energy sources and specific data where this is stated by the manufacturer. The type of transport depends on where the raw material is produced. Transport of the raw materials can be divided into transport from the farm to raw material processing and to margarine production. The logistics for the various raw materials is complex and some raw materials are not grown close to where the raw material is processed. The overseas agricultural products are transported from the farm or plantation to the first processing stage and from there to the port to be shipped to Europe, where further processing takes place. Production and transport of packaging is also included. In the margarine production, energy is used to process the raw material for margarine, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian margarine production, 60% fat
The data quality and representativeness are good for most processes. Data from Ecoinvent and Agri-footprint have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Country-specific data is used for the raw materials that are included with more than 1%. Specific data is used for the amount of raw materials and packaging and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2019
##Time representativeness description##
The reference year for the margarine production process is 2019. For upstream and downstream processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for margarine.
##LCI allocation methods##
For raw materials for plant and feed production, economic allocation has been used for co-products on the farm (e.g. rapeseed oil and rapeseed meal) and follows the PEFCR for feed.
Allocation between milk and meat on the farm was based on biophysical principles according to PEFCR Dairy products. For the production of margarine, inflows and outflows were allocated based on the products' dry matter content, according to PEFCR Dairy products.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2019
##Administrative Information##
##Data set generator##
Hanne Møller, Simon Saxegård
##Access and use restrictions##
##References##
NORSUS report OR 55.20 (closed)
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[GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted; Primary and secondary production, at plant; 95% primary material, 5% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Asia and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to Asian heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Asia (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Asia
##Geographical representativeness description##
Global and RoW ecoinvent processes for steel production technologies have been adjusted to represent Asian production.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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[GeneralComment] => ##General Information##
##Product name##
Potato salad, packaged, at consumer
##Reference flow##
1 kg of packaged product
##System boundaries##
The life cycle analysis is from cradle to grave and include the following phases: raw materials (ingredients), packaging, transport of raw materials and packaging, production of potato salad, distribution, consumption and waste management and recycling of packaging waste.
The reference flow is defined as 1 kg of packaged product (product weight) to the consumer.
##Wastes and end-of-life##
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##Biogenic carbon##
##Use advice of the dataset##
It is important to remember that if this dataset represents potato salad produced in Scandinavia and if used as a proxy for potato salad produced elsewhere it is important to note that the dataset may not be representative for that region. Ecoinvent 3.8 (cut-off by classification) and Agrifootprint 5.0 is used for the background data.
##Technological representativeness##
##Technology description##
Several raw materials are included in potato salad production, each of which has its own production process and is transported to the production site in Norway. Specific datasets were produced for some ingredients and for others Agrifootprint and Ecoinvent were used. The type of transport depends on where the raw material is produced. Production and transport of packaging is also included. In the manufacturing of potato salad, energy is used to process the raw materials for the salad, which is packaged and distributed to wholesale, retail and consumers. The consumption phase includes transport from retail to consumer and standard values from PEFCR Dairy Products; 62% drive 5 km by passenger car, 5% drive 5 km by van and 33% either walk or cycle and have no impact. Electricity for cooling of the product in the household is included. Disposal of consumer packaging is based on the average waste system for household waste, food waste not included.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for Norwegian production of potato salad
The data quality and representativeness are good for most processes. Data from Ecoinvent, Agri-footprint and WorldFood Database have been used where it has not been possible to obtain specific information. These two databases can have quite different results for some processes and a thorough assessment has been made when choosing a process. In general, ecoinvent data has been used for chemicals and energy. For agricultural products, Agri-footprint has more precise data at, for example, crop level when compared with FAO statistics.
Specific data is used for the amount of raw materials, amounts of packaging material and for the production process.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
The reference year for the potato salad production process is 2020. For upstream and downstream processes, the time representativeness varies, as Ecoinvent 3.8, Agrifootprint 2.0 and World Food Database, has been used.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
This life cycle analysis is in line with ISO standards 14040-44 and 14025. The main principles of Product Environmental Footprint Category Rules (PEFCR) for Dairy Products (European Dairy Association, 2018) and Product Environmental Footprint Category Rules (PEFCR) Feed for food producing animals has been followed since there are no separate product-specific rules (PCR) for potato salad.
##LCI allocation methods##
Economic allocation has been used.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
Cut off for raw materials below 1% by weight of the product
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period##
2020
##Administrative Information##
##Data set generator##
Anna Woodhouse, Erik Svanes
##Access and use restrictions##
NORSUS report OR.01.22 (Closed)
##
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[Name] => Beef from dual purpose production; at farmgate
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[Name_BaseName] => Beef from dual purpose production; at farmgate
[GeneralComment] => ##General Information##
##Product name##
Beef from dual purpose production; at farmgate; Norway
##Reference flow##
1 kg beef slaughter weight
##System boundaries##
Cradle to farm gate, includes fertiliser production and application, imported and domestic feed production, average farm data of dual-purpose production in terms of scale, feeding regimes and manure management systems.
##Wastes and end-of-life##
Wastes and end-of-life is not included.
##Biogenic carbon##
Biogenic carbon is from methane from enteric fermentation and manure storage. Soil organic carbon is not included.
##Use advice of the dataset##
When using data, it is important to be aware of the use of allocation method used, which can greatly affect the results. For feed and plant production, economic allocation has been used for co-products on the farm (e.g., rapeseed oil and rapeseed meal) and follows the PEFCR for feed (FEFAC, 2018).
Allocation between milk and meat on the farm is based on biophysical principles according to PEFCR Dairy products (European Dairy Association, 2018) and which has also previously been recommended by the International Dairy Federation. Ecoinvent 3.9.1 (cut-off by classification) and Agrifootprint 6.3 is used for the background data.
##Technological representativeness##
##Technology description##
The data represented typical Norwegian cattle herds of dual-purpose production in terms of scale and feeding regimes with production levels corresponding to average milk yields, growth performance, and beef production in Norway in 2021. The dual-purpose dairy production was based on production data of Norwegian Red (NR) obtained from the Norwegian Dairy Herd Recording System (NDHRS). Milk yield 8,550 kg FPCM dairy cow-1. Carcass production per cow including bulls and surplus heifers was 273 kg per year, bull age at slaughter 17.8 months, heifer age at calving 25.6 month. Concentrate feed intake per dairy cow was 2,614 kg DM (dry matter)/LU (livestock unit).
Energy requirements for all animal categories (cows, heifer, bull) were obtained using the Nordic feed evaluation system (NorFor; Volden, 2011) through TINE Optifor. Diet compositions for dairy cattle were available through TINE Mjølkonomi®, an economic tool for milk producers. The composition of typical concentrate feeds for dairy cows, heifers and young bulls was given by Felleskjøpet Fôrutvikling. Manure was assumed to be deposited on pasture during summer (pasture season typically from mid-May to mid-September). During housing, the proportion of manure management system was considered for each animal category. Manure was applied on ley area during spring. Silage dry matter (DM) yield (6320 kg DM ha-1) and the use of fertilizer (159 kg N ha-1), lime (51.9 kg ha-1), herbicides (1.9 L glyphosate ha-1 and 560 ml MCPA; 2-methyl-4-chlorophenoxyacetic acid ha-1) (NIBIO, 2018; Statistics Norway, 2012; TINE, 2022), and diesel (8.02 L ha-1) (Korsaeth et al., 2016) for a typical Norwegian farm was made available through TINE. The ley and pasture area corresponded to the calculated forage requirements.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Representative for average dual-purpose milk and beef production in Norway. There is large variation between the regions and therefore these average data should not be used in analyses at regional or farm level.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
The reference year for the beef production is 2021, using time specific data for carcass production, milk yield concentrate feeds. For background processes, the time representativeness varies, but in general the data is representative for the period 1998-2018.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional. Background data based on ecoinvent cut-off.
##LCI allocation methods##
For feed production, economic allocation has been used for co-products on the farm and follows the PEFCR for feed. For allocation between meat and milk products at the farm, biophysical allocation was used according to the PEFCR for dairy products (European Dairy Association, 2018): using kg live weight sold per year converted to carcass weight and the mass of fat and protein corrected milk (FPCM).
##Other comments on methods approaches##
Emissions of methane, dinitrogen monoxide, ammonia, nitrogen dioxide and carbon dioxide are calculated based on the IPCC (2006). The emissions include enteric fermentation, manure storage, direct and indirect emissions from application of manure and mineral fertiliser, leaching and runoff and crop residues returned to soils and emissions from application of limestone.
##Completeness##
##Cut off for mass or energy flows##
No cut-off has been applied.
##Infrastructure/capital goods##
Some purchased services might be left out.
##Data treatment and extrapolations principles##
##Data collection period##
2019-2022
##Administrative Information##
##Data set generator##
Hanne Møller, Stine Samsonstuen
##Access and use restrictions##
open
##References##
NORSUS report: Life cycle assessment of meat -climate change, on assignment for Nortura
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[Name] => Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state
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[GeneralComment] => ##General Information##
##Product name## Microfibrillated cellulose, Exilva Piano; without water; at plant; in 2% solution state
##Reference flow## 1kg dry product
##System boundaries## Production of microfibrillated cellulose (MFC) of the Exilva Piano grade, in a 2% concentration at the Exilva industrial MFC plant in Sarpsborg, Norway. The functional unit is 1 kg dry product at factory gate, without packaging.
The modelling of the Exilva plant has been part of work package 7 (WP7) of the H2020 BBI JU Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). The modelling of the Borregaard biorefinery and the Exilva site have been documented in the following reports: Modahl and Soldal (2015) and Modahl, Brekke, Valente and Soldal (2016). The 2011 status of the specialty cellulose was published as a scientific paper by Modahl, Brekke and Valente (2015). Exilva MFC comes in four grades: Piano Light, Piano, Forte and Forte Plus, and all qualities are produced both as a 2% suspension and a 10% paste.
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset## MFC has many different application areas where different characteristics are emphasised. If the dataset is used for comparing Exilva MFC to other materials for the same purpose, special attention should be directed to product formulations and amounts. Necessary data to perform toxicity assessments has not been collected. Data are given for the active substance of the product. It is, however, sold and used with relative large amounts of water. Hence, make sure you calculate the correct amount of active substance. An example: you are transporting 1 kg of 2% Exilva. The amount of active substance is 20g. To calculate the correct transport volume, you will need to transport 20g of active substance and 980g water.
##Technological representativeness##
##Technology description## Production of the specialty cellulose, which is the main raw material for the MFC, takes place in an advanced biorefinery which also produces lignosulfonates, bioethanol, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Further processing of the specialty cellulose to MFC is made in a full-scale industrial plant built in 2016 (Exilva site). The main raw material of the biorefinery is Norway Spruce from Norway and Sweden. Specific data has been used for all activities at the Borregaard biorefinery and Exilva sites. Data for the biorefinery are from 2015 and data for the Exilva plant are from 2016 and 2018. The Ecoinvent database (version 3.8 allocation cut-off by classification) has been used for background processes. The biorefinery and the Exilva plant have been modelled on a detailed level, avoiding allocation to a large extent. Energy allocation has been used when necessary. An attributional approach has been used. To be in line with the EPDs made for the biorefinery's products (including the specialty cellulose), the following system boundaries have been applied: Norwegian electricity production mix has been used for electricity used in Norway, emissions from combustion of waste for heat production at the biorefinery are not allocated the user of the heat, rather the producer of the waste, and emissions from combustion of waste oil and biogas have been allocated the user. Infrastructure is included (in contrast to the EPDs, where infrastructure in the form of production equipment with an expected lifetime over three years, buildings and capital goods are not included except for energy carriers). The LCA software SimaPro (version 9.2.0) was used to model the system.
##Technology Quality level## Very good
##Geographical representativeness##
##Location## Norway
##Geographical representativeness description## Data for the biorefinery is collected from, and specific for, the given biorefinery in Sarpsborg, Norway
##Geographical Quality level## Very good
##Time related representativeness##
##Reference year## 2016
##Time representativeness description## Data for the Exilva plant is from 2016, and for the biorefinery data for the major flows are from 2015 (specialty cellulose as raw material). Data used are based on annual numbers. For the biorefinery these numbers could change somewhat from year to year but the burdens per kg of specialty cellulose would not vary much. Changes in the energy carrier mix of the biorefinery could, however, alter resource use, emissions and waste generation. For the Exilva plant (microfibrillation of specialty cellulose), flows per kg of product will be quite constant.
##Time Quality Level## Very good
##Methodological appropriateness and consistency##
##LCI method principle## Attributional
##LCI allocation methods##
##Other comments on methods approaches## The biorefinery (producing specialty cellulose as a raw material for the microfibrillated cellulose) and the Exilva plant have been modelled on a detailed level, largely avoiding the need for allocation. Energy allocation has been used when necessary. For steam and hot water, the enthalpy has been used. Infrastructure is included. Emissions from combustion of waste for heat production at the biorefinery are not allocated to the user of the heat, rather to the producer of the waste. Emissions from combustion of waste oil and biogas have been allocated to the user.
##Completeness##
##Cut off for mass or energy flows## No cut off in the foreground system (Exilva plant) and the biorefinery producing specialty cellulose as a raw material to the Exilva plant.
##Infrastructure/capital goods##
##Data treatment and extrapolations principles##
##Data collection period## 2008 – 2018
##Administrative Information##
##Data set generator## Ingunn Saur Modahl
##Access and use restrictions## Free
##References##
Modahl, I.S. and Soldal, E. (2015): The 2015 LCA of products from the wood-based biorefinery at Borregaard, Sarpsborg. Results for cellulose, ethanols, lignosulfonates, vanillin, sodium hypochlorite, sodium hydroxide and hydrochloric acid. Ostfold Research, OR 11.15, April 2016.
Modahl, I.S., Brekke, A. and Valente, C. (2015): Environmental assessment of chemical products from a Norwegian biorefinery. Journal of Cleaner Production 94 (2015) 247-259.
Modahl, I.S., Brekke, A., Valente, C., and Soldal, E. (2016): E-LCA and S-LCA of the Exilva MFC process. AR 10.16 Ostfold Research. Deliverable report D7.2 from work package 7 of the H2020 Exilva project 'Flagship demonstration of an integrated plant towards large scale supply and market assessment of MFC' (Grant Agreement No 709746). Confidential. November 2016.
##
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1 kg DM
##System boundaries##
The dataset represents manual harvest of beech in France. The dataset covers stand establishment (production of seedlings in an unheated greenhouse and planting), tending and cleaning, thinning, manual final harvest and chipping. Construction and maintenance of infrastructure, like machinery and forest roads, are included. The system ends with wood chips at forest road. The activities over one rotation period are included.
The activity starts with site preparation assuming establishment of the forest via planting, including seedling production and covers all process related to forest management, including site preparation, planting, tending, young growth tending, clearing, thinning, and harvesting operations including the processing of wood fuel to chips, bundles and chopped wood (logs for energy) over one rotation period. It also covers the maintenance and construction of forest roads. This activity ends with the assortments at the forest road and includes eventual drying before transportation.
##Wastes and end-of-life##
##Biogenic carbon##
Biogenic carbon included as resource input from nature (uptake of carbon dioxide in air)
##Use advice of the dataset##
The dataset should be used to analyze the impact from beech wood chips that originate from thinning operations in France. Measured as dry mass
##Technological representativeness##
##Technology description##
For the chipping in the stand, a productivity of 25 m3 bulked/PMH is assumed (based on Cremer & Velazquesz). For wood chips chipped in the stand, a productivity of the forwarding of 70 Sm3 bulked/PMH with a diesel consumption of 9.5 l/h of the forwarder is assumed. 24.4% thinning (mechanical) and 76.6% (manual) final harvest
##Technology Quality level##Fair
##Geographical representativeness##
##Location## France (FR)
##Geographical representativeness description##
The dataset is based on dataset on harvest of hardwood in Germany from Wernet et al. (2016). The dataset has been adjusted to reflect beech forests, and beech forest management in France. The adjustments include changes in the harvesting methods to mirror the relative share of manual harvesting vs mechanized harvesters in the eastern parts on France, the allocation between clear-cut and thinning, and the area used for different practices (Pelletier, 2017).
##Geographical Quality level##
Fair
##Time related representativeness##
##Reference year##
2017
##Time representativeness description## Data represent 2017 for the specifications of French beech management while original ecoinvent data are largely based on a data source from 2009. Data are still considered representative due to small developments in forest management and harvest operations.
##Time Quality Level##
Fair
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
Allocation based on mass.
##Other comments on methods approaches##
##Completeness##
##Cut off for mass or energy flows##
All known inputs are included.
##Infrastructure/capital goods##
Construction and maintenance of roads and machinery are included
##Data treatment and extrapolations principles##
In communication with French Institute of Technology for Forest-based and Furniture Sectors (FCBA), it was recommended to use the ecoinvent (v3.8) dataset for beech from Germany as representative for beech from the eastern parts of France. The ecoinvent database provides well documented process data and is widely used in Europe. The data set “hardwood forestry, beech, sustainable forest management” was selected. This dataset covers the production and harvesting of 1 m3 of stemwood, beech, solid, under bark, plus the relative share of energy wood from slash from sustainable forest management as the prevailing management practices in Germany (Wernet et al., 2016). Changes were made to allocation principles, harvesting regimes, and area used to reflect French beech forestry (Pelletier, 2017).
##Data collection period##
2017 - 2021
##Administrative Information##
##Data set generator##
Andreas Brekke
##Access and use restrictions##
Open
##References##
Cremer, T. and B. Velazques-Marti (2007): Evaluation of two harvesting systems for the supply of woodchips in Norway spurce forest affected by bark beetle. Croation Journal of Forest Engineering, 28(2): 145-155.
Pelletier, C. (2017). Analyse environnementale et économique des filières bois-énergie. Université de Lorraine, Retrieved from https://tel.archives-ouvertes.fr/tel-01765854/document (2017LORR0331)
Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., & Weidema, B. (2016). The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, 21(9), 1218-1230. doi:10.1007/s11367-016-1087-8
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[GeneralComment] => ##General Information##
##Product name##
Low alloyed steel, casted; Primary and secondary production, at plant; 90% primary material, 10% recycled material
##Reference flow##
1 kg
##System boundaries##
Cradle-to-gate. Starts with the production of raw material (pig iron) and ends with steel making process and casting with oxygen blast furnace and electric converter technologies.
##Wastes and end-of-life##
Processing waste is included, EoL waste of the steel not accounted for.
##Biogenic carbon##
Not relevant
##Use advice of the dataset##
This dataset should be used to model the use of steel produced in Europe and uses ecoinvent 3.8 processes in the background. More precisely,
· For both steel processing technologies (oxygen blast furnace and electric converter) and the raw material (pig iron) production, the ecoinvent processes have been adjusted to European heat and electricity inputs.
· The share of recycled and virgin materials is adjusted based on the use of technologies in the country (World steel association, 2019).
The dataset generator kindly invites LCA practitioners to share their dataset, if they claim to have a better dataset for this reference product.
##Technological representativeness##
##Technology description##
The share of recycled and virgin materials is based on the use of production technologies in Europe (oxygen blast furnace used for recycled material and electric converter used for virgin material) based on statistics from World Steel Association (2021). For further description of the technologies, see the description of the ecoinvent datasets.
##Technology Quality level##
Fair
##Geographical representativeness##
##Location##
Europe
##Geographical representativeness description##
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##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2020
##Time representativeness description##
Elements modified from the ecoinvent datasets are based on yearly World Steel published data, representative for 2020.
##Time Quality Level##
Good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure included
##Data treatment and extrapolations principles##
See documentation of the ecoinvent processes for further details about the modified datasets.
##Data collection period##
##Administrative Information##
##Data set generator##
Irmeline de Sadeleer
##Access and use restrictions##
Open
##References##
World Steel Association, word steel in figures 2021
Silva, de Sadeleer & Rønning (2021). EPD data to LCA.no. CF, CCF and CCCF Panel Switchgears. OR.29.21
##
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[GeneralComment] => "##General Information##
##Product name##
Heat, for district heating, Norwegian production mix, at plant
##Reference flow##
1 kWh
##System boundaries##
Production mix of district heat in Norway in 2021, based on information on energy carriers from Norsk Fjernvarme. The activity starts with acquisition of energy carriers, i.e., waste reception at gate, acquisition and transport of fossil oil and gas, harvesting and transport of woody biomass, and production, transformation from high to low voltage and transmission of electricity. The activity stops at district heat production facility (i.e. distribution is not included).
##Wastes and end-of-life##
##Biogenic carbon##
##Use advice of the dataset##
This dataset represents the district heat production mix in Norway in the year 2021. Data on heat production and share of different energy carriers are collected from www.fjernkontrollen.no. ecoinvent 3.8 allocation, cut-off by classification is used for the background data.
##Technological representativeness##
##Technology description##
Average technology for production and transmission of electricity in Norway. Heat from the other sources based on average European technologies. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Technology Quality level##
Good
##Geographical representativeness##
##Location##
Norway
##Geographical representativeness description##
Total heat production and share of sources are representative for Norway. Background data are representative for European technology.
##Geographical Quality level##
Good
##Time related representativeness##
##Reference year##
2021
##Time representativeness description##
Annual production.
##Time Quality Level##
Very good
##Methodological appropriateness and consistency##
##LCI method principle##
Attributional
##LCI allocation methods##
##Other comments on methods approaches##
Allocation cut-off by classification in background data.
##Completeness##
##Cut off for mass or energy flows##
##Infrastructure/capital goods##
Infrastructure is included
##Data treatment and extrapolations principles##
The energy carriers are given by Norsk Fjernvarme. One technology is selected to represent each category of energy carriers. For fuel oil, light fuel oil is selected. For ambient heat, heat pump is selected. For bioenergy, wood logs combustion is used as representative technology.
##Data collection period##
2021
##Administrative Information##
##Data set generator##
Ellen Soldal
##Access and use restrictions##
Open
##References##
Norsk Fjernvarme (2022). Fjernkontrollen.no. [online] Available at https://www.fjernkontrollen.no/ Webpage. Access date: 27.09.2022.
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[Id] => 65afb7b5-93be-470b-8c68-f9c750db65d7
[LibraryId] => 93a60a56-a3c8-11da-a746-0800200b9a66
[Name] => Mass