TRANSFORM

A series of transformations are applied to the Life Cycle Inventory (LCI) database to align process performance and technology market shares with the outputs from the Integrated Assessment Model (IAM) scenario.

Sector updates (overview)

The updates below are applied when calling ndb.update() with the corresponding sector name. They change selected parts of ecoinvent; sectors not listed here are left unchanged unless explicitly mapped. The exact updates depend on the IAM model, scenario, year, and the ecoinvent version used.

• electricity: updates electricity generation mixes, technology efficiencies, and regional markets; applies corrections such as hydropower water emissions and PV/wind regionalization where available. Mappings: premise/iam_variables_mapping/electricity.yaml. Data: premise/data/renewables/, premise/data/electricity/.

• fuels: updates fuel supply chains and regional markets, including hydrogen, biofuels, synthetic fuels, and fossil fuels; adjusts efficiencies and losses along the supply chain. Mappings: premise/iam_variables_mapping/fuels.yaml. Data: premise/data/fuels/.

• heat: updates residential/industrial heat markets and technology shares; calibrates heat efficiencies and fuel inputs by region. Mappings: premise/iam_variables_mapping/heat.yaml.

• cement: updates clinker and cement production efficiencies, fuel use, and (where applicable) carbon capture integration; rebuilds regional markets from IAM production volumes. Mappings: premise/iam_variables_mapping/cement.yaml.

• steel: updates primary/secondary steel routes (e.g., BF-BOF, DRI, EAF), efficiencies, and regional market shares from IAM outputs. Mappings: premise/iam_variables_mapping/steel.yaml.

• transport (split by mode in `NewDatabase`): updates vehicle markets and fleets (cars, buses, trucks, ships, rail), technology shares, and energy carriers; adjusts related emissions factors where relevant. Use: cars, two_wheelers, trucks, buses, trains, ships. Mappings: premise/iam_variables_mapping/transport_*.yaml. Data: premise/data/transport/.

• battery: scales battery pack mass by projected energy densities; creates technology-specific and scenario-average battery markets (mobile and stationary). Data: premise/data/battery/energy_density.yaml, premise/data/battery/mobile_scenarios.csv, premise/data/battery/stationary_scenarios.csv.

• metals: updates material intensities and adds/updates mining/refining markets; includes post-allocation corrections for co-mined metals and regional supply shares. Data: premise/data/metals/ (e.g., activities_mapping.yml, metals_db.csv, mining_shares_mapping.xlsx).

• mining: updates waste/tailings handling and regional mining markets where mapped. Data: premise/data/mining/tailings_activities.yaml, premise/data/mining/tailings_topology.yaml.

• biomass: updates biomass supply chains and regional forestry activities. Mappings: premise/iam_variables_mapping/biomass.yaml. Data: premise/data/biomass/.

• cdr: introduces and updates carbon dioxide removal routes (DACCS, BECCS, enhanced weathering, ocean liming) and their regional deployment. Mappings: premise/iam_variables_mapping/carbon_dioxide_removal.yaml. Data: premise/data/cdr/.

• emissions: applies IAM- and GAINS-based emission factors to relevant processes. Data: premise/data/GAINS_emission_factors/.

• renewable: updates wind turbine (and related) inventories and regional deployment. Data: premise/data/renewables/.

• final energy: updates final energy carrier mixes used in downstream sectors. Mappings: premise/iam_variables_mapping/final_energy.yaml. Data: premise/data/energy/.

• external: applies user-provided external scenarios to override or extend IAM data.

Each sector has dedicated configuration files under premise/data and premise/iam_variables_mapping which define variable mappings, technology lists, and default parameters.

Mobile batteries

Inventories for several battery technologies for mobile applications are provided in premise. See EXTRACT/Import of additional inventories/Li-ion batteries for additional information.

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("battery")

Key outputs

• Scales battery pack mass based on projected energy density improvements.

• Creates technology-specific market for battery capacity datasets (1 kWh).

• Creates scenario-average battery capacity markets (LFP/NCx/PLiB/MIX) with time-varying shares.

The table below shows the current specific energy density of different battery technologies.

Type	Specific energy density (current) [kWh/kg cell]	BoP mass share [%]	Battery energy density [kWh/kg battery]
Li-ion, NMC111	0.18	73%	0.13
Li-ion, NMC523	0.20	73%	0.15
Li-ion, NMC622	0.24	73%	0.18
Li-ion, NMC811	0.28	71%	0.20
Li-ion, NMC955	0.34	71%	0.24
Li-ion, NCA	0.28	71%	0.20
Li-ion, LFP	0.16	80%	0.13
Li-ion, LiMn2O4	0.11	80%	0.09
Li-ion, LTO	0.05	64%	0.03
Li-sulfur, Li-S	0.15	75%	0.11
Li-oxygen, Li-O2	0.36	55%	0.20
Sodium-ion, SiB	0.16	75%	0.12

And the table below shows the projected (2050) specific energy density of different battery technologies.

Type	Specific energy density (2050) [kWh/kg cell]	BoP mass share [%]	Battery energy density [kWh/kg battery]
Li-ion, NMC111	0.2	73%	0.15
Li-ion, NMC523	0.22	73%	0.16
Li-ion, NMC622	0.26	73%	0.19
Li-ion, NMC811	0.34	71%	0.24
Li-ion, NMC955	0.38	71%	0.27
Li-ion, NCA	0.34	71%	0.24
Li-ion, LFP	0.22	80%	0.18
Li-ion, LiMn2O4	0.11	73%	0.08
Li-ion, LTO	0.05	64%	0.03
Li-sulfur, Li-S	0.34	75%	0.26
Li-oxygen, Li-O2	0.93	55%	0.51
Sodium-ion, SiB	0.20	75%	0.15

premise adjusts the mass of battery packs throughout the database to reflect progress in specific energy density (kWh/kg cell).

For example, in 2050, the mass of NMC811 batteries (cells and Balance of Plant) is expected to be 0.5/0.22 = 2.3 times lower for a same energy capacity. The report of changes shows the new mass of battery packs for each activity using them.

The target values used for scaling can be modified by the user. The YAML file is located under premise/data/battery/energy_density.yaml.

For each battery technology premise creates a market dataset that represents the supply of 1 kWh of electricity stored in a battery of the given technology.

The table below shows the market for battery capacity datasets created by premise.

Name	Location	Kg per kWh in 2020 (kg/kWh)	Kg per kWh in 2050 (kg/KWh)
market for battery capacity, Li-ion, LFP	GLO	8.6	6.22
market for battery capacity, Li-ion, LTO	GLO	18.4	18.4
market for battery capacity, Li-ion, Li-O2	GLO	5.05	3.37
market for battery capacity, Li-ion, LiMn2O4	GLO	8.75	8.75
market for battery capacity, Li-ion, NCA	GLO	5.03	4.14
market for battery capacity, Li-ion, NMC111	GLO	7.61	6.85
market for battery capacity, Li-ion, NMC523	GLO	6.85	6.23
market for battery capacity, Li-ion, NMC622	GLO	5.71	5.27
market for battery capacity, Li-ion, NMC811	GLO	5.03	4.14
market for battery capacity, Li-ion, NMC955	GLO	4.14	3.71
market for battery capacity, Li-sulfur, Li-S	GLO	8.89	3.92
market for battery capacity, Sodium-Nickel-Cl	GLO	8.62	8.62
market for battery capacity, Sodium-ion, SiB	GLO	8.33	6.54

Changing the target values in the YAML file will change the scaling factors and the mass of battery packs per kWh in the database.

Finally, premise also create a technology-average dataset for mobile batteries according to four scenarios provided in Degen et al, 2023.:

Name	Location	Description
market for battery capacity (LFP scenario)	GLO	LFP dominates the market for mobile batteries.
market for battery capacity (NCx scenario)	GLO	NCA and NCM dominate the market for mobile batteries.
market for battery capacity (PLiB scenario)	GLO	Post-lithium batteries dominate the market for mobile batteries.
market for battery capacity (MIX scenario)	GLO	A mix of lithium and post-lithium batteries dominates the market.

These datasets provide 1 kWh of battery capacity, and the technology shares are adjusted over time with values found under https://github.com/polca/premise/blob/master/premise/data/battery/scenario.csv.

Stationary batteries

Inventories for several battery technologies for stationary applications are provided:

• Lithium-ion batteries (NMC-111, NMC-622, NMC-811, LFP)

• Lead-acid batteries

• Vanadium redox flow batteries (VRFB)

Key outputs

• Scales stationary battery pack mass based on projected energy densities.

• Creates technology-specific stationary market for battery capacity datasets (1 kWh).

• Creates scenario-average stationary markets (CONT, TC) with time-varying shares.

As for batteries for mobile applications, premise adjusts the mass of battery packs throughout the database to reflect progress in specific energy density (kWh/kg cell). The current specific energy densities are given in the table below.

Type	Specific energy density (current) [kWh/kg cell]	BoP mass share [%]	Battery energy density [kWh/kg battery]
Li-ion, NMC111	0.15	73%	0.11
Li-ion, NMC622	0.20	73%	0.15
Li-ion, NMC811	0.22	71%	0.16
Li-ion, LFP	0.14	73%	0.10
Sodium-ion, SiB	0.16	75%	0.12
Lead-acid	0.03	80%	0.02
VRFB	0.02	75%	0.02

The future specific energy densities are given in the table below.

Type	Specific energy density (2050) [kWh/kg cell]	BoP mass share [%]	Battery energy density [kWh/kg battery]
Li-ion, NMC111	0.2	73%	0.15
Li-ion, NMC811	0.5	71%	0.36
Li-ion, NCA	0.35	71%	0.25
Li-ion, LFP	0.25	73%	0.18
Sodium-ion, SiB	0.22	75%	0.17
Lead-acid	0.04	80%	0.03
VRFB	0.04	75%	0.03

The target values used for scaling can be modified by the user. The YAML file is located under premise/data/battery/energy_density.yaml.

For each battery technology premise creates a market dataset that represents the supply of 1 kWh of electricity stored in a battery of the given technology.

The table below shows the market for battery capacity datasets created by premise.

Name	Location	Kg per kWh in 2020 (kg/kWh)	Kg per kWh in 2050 (kg/KWh)
market for battery capacity, Li-ion, LFP, stationary	GLO	8.6	6.22
market for battery capacity, Li-ion, NMC111, stationary	GLO	7.61	6.85
market for battery capacity, Li-ion, NMC523, stationary	GLO	6.85	6.23
market for battery capacity, Li-ion, NMC622, stationary	GLO	5.71	5.27
market for battery capacity, Li-ion, NMC811, stationary	GLO	5.03	4.14
market for battery capacity, Li-ion, NMC955, stationary	GLO	4.14	3.71
market for battery capacity, Sodium-Nickel-Chloride, Na-NiCl, stationary	GLO	8.62	8.62
market for battery capacity, Sodium-ion, SiB, stationary	GLO	8.33	6.54
market for battery capacity, lead acid, rechargeable, stationary	GLO	33.33	28.60
market for battery capacity, redox-flow, Vanadium, stationary	GLO	51.55	25.00

Changing the target values in the YAML file will change the scaling factors and the mass of battery packs per kWh in the database.

Finally, premise also create a technology-average dataset for stationary batteries according to three scenarios provided in Schlichenmaier & Naegler, 2022:

Name	Location	Description
market for battery capacity, stationary (CONT scenario)	GLO	LFP and NMC dominate the market for stationary batteries.
market for battery capacity, stationary (TC scenario)	GLO	Vanadium Redox Flow batteries dominate the market for stationary batteries.

market for battery capacity, stationary (CONT scenario) supplies any storage capacity needed in high voltage electricity markets.

Metals

premise updates the material intensities of energy and transport technologies, with a particular focus on critical raw materials. The goal is to ensure that both current and future datasets accurately reflect the evolving material requirements of key technologies, such as wind turbines and batteries. Key processes include collecting and processing material intensity data, adding new metal production inventories, applying post-allocation corrections for co-mined metals, and constructing global markets for mined and refined metals.

The workflow for updating material intensities in premise consists of the following steps:

• Data collection: Material intensity data is sourced from literature and stored in structured files.

• Data processing: The collected data is processed to align with the database, including unit conversions and mapping to relevant datasets.

• Inventories: Additional inventories for metals production (e.g., Cobalt, Lithium, Vanadium) are added to the database.

• Post-allocation correction: Multifunctional processes (e.g., co-mining) are adjusted to ensure proper mass balance.

• Markets creation: Global supply markets for mined and refined metals are built, reflecting current and future regional contributions.

Key outputs

• Updates material intensity factors for mapped technologies.

• Adds/updates mining and refining inventories for critical metals.

• Applies post-allocation corrections for co-mined metals and rebuilds global markets with regional shares.

To update the material intensities in the database, run the following code:

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("metals")

Data collection and processing

Distributions for material intensities, derived from a comprehensive literature collection, are provided in SI_2_Material_requirements.xlsx. From this database, metals_db.csv is created, which premise uses to update the material intensities for each technology.

The mapping file that associate metal intensities to datasets to be updated can be found in activity_mapping.yml.

To convert the units in metals_db.csv to the units used in ecoinvent (e.g., converting [kg metal/kW] to [kg metal/kg battery]), premise uses the conversion factors found in conversion_factors.csv.

Finally, premise uses the data under metal_products.csv to refine the activity in ecoinvent to be updated, select the specific metal product (e.g., boric oxide for boron used in wind turbine magnets) and convert the intensities to the relevant compound (e.g., 1kg of Boron is converted to 86.19 kg of B2O3).

Inventories

premise provides inventories for the following metals:

• Cobalt.

• Germanium, as a co-product from zinc mine operation, based on the unallocated dataset in ecoinvent.

• Graphite.

• Iridium, as a co-product from PGM mine operation, based on the unallocated dataset in ecoinvent.

• Lithium.

• Rhenium, as a co-product from copper mine operation, based on the unallocated dataset in ecoinvent.

• Ruthenium, as a co-product from PGM mine operation, based on the unallocated dataset in ecoinvent.

• and Vanadium.

The inventories are provided under premise/data/additional_inventories

Post-allocation correction

Regarding the co-production of metals in multifunctional processes (i.e., co-mining of metals), premise modifies the database to allocate according to physical mass balance: extraction of individual elements in the ore is fully attributed to the production of the respective metal; while other elementary and intermediate flows follow an economic allocation, which is the default option to deal with multi-functionality in ecoinvent. As discussed in Berger et al. (2023), this approach ensures a correct mass balance.

For example, the amount of platinum resource included in the dataset representing the mining of 1 kg of platinum is set to 1 kg, while the amount of other metals (e.g., palladium, rhodium) is set to zero. For metal-bearing intermediates, such as beryllium hydroxide, chromite ore concentrate, or titanium dioxide products, the target resource flow is set to the target-metal content of 1 kg of the reference product instead of being forced to 1 kg. If no explicit content factor is known for an ore, concentrate, or mineral product, the original target resource amount in the dataset is retained while the other co-mined in-ground resource flows are set to zero.

The correction is data-driven and applied directly from the transformed database. premise scans non-market datasets from the mining-share mapping and additional extraction-like datasets with kilogram-scale natural resource::in ground exchanges. It then matches the in-ground resource flow to the dataset reference product. Only these elementary resource flows are modified: technosphere exchanges and other biosphere exchanges are left unchanged.

The matched target flow is set to the target-metal content of the reference product, and other co-mined in-ground resource flows are set to zero. For mapped pure-metal suppliers that do not contain a target resource flow, the missing in-ground metal flow is added at 1 kg per kg of reference product. Generic carrier intermediates whose resource attribution is handled by downstream metal-specific datasets, such as platinum group metal concentrate, have their direct metal resource flows cleared to avoid double counting. Explicit product-content factors are used to resolve otherwise ambiguous metal-bearing products such as copper-cobalt ore; broader labels such as lead-zinc concentrates remain ambiguous unless an explicit factor exists for that product.

The correction no longer relies on static YAML correction tables. If the target flow is missing or ambiguous for a mapped metal-bearing dataset that cannot be inferred safely, premise raises an error instead of silently applying a partial correction.

The markets are relinked to metals-consuming activities throughout the database.

Mining and refining markets creation

premise builds global supply markets for several mined and refined metals. In these markets, the contribution of different mining and refining regions corresponds to their current market shares. Following this approach, the supply from different regions for a specific metal will be directly proportional to the country-level contributions to the global market. These shares are derived from various sources, mainly BGS and USGS, in addition to data from van den Brink_ et al. (2022) for Antimony refining. For certain markets where data was available, premise incorporates projections from BNEF regarding the development of future mining and refining projects to forecast the market shares’ evolution up to 2030.

The file used to build the global supply markets for mined and refined metals is mining_shares_mapping.xlsx.

Additionally, global metal supply markets modeled account for the average transport distances and modes of transport for the different metals from producer to consumer. These data are retrieved from UNCTAD.

Average transport distance and modes of transport for each producer can be found under: transport_markets_data.

Mining

To update the mining practices in the database, run the following code:

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("mining")

Key outputs

• Adds alternative tailings treatment pathways and regional adoption shares.

• Updates mining waste inventories and links them to regional markets.

Sulfidic tailings

Mine tailings represent one of the most environmentally problematic waste streams generated by mining activities, especially when they originate from the processing of sulfidic ores. If not properly managed, such tailings can lead to acid mine drainage, causing long-term toxic contamination of surrounding ecosystems even decades after mine closure [1].

Globally, most tailings generated through the beneficiation of hard rock metal ores and industrial minerals are stored in dammed impoundments where they are often submerged to minimize dust and reduce sulfide oxidation [2]. However, a range of alternative tailings management strategies is increasingly being adopted.

To better reflect these evolving practices in life cycle modeling, we modified the ecoinvent database by introducing multiple treatment pathways for sulfidic tailings. These include:

• Surface impoundment, which remains the default inventory in ecoinvent.

• Backfilling into underground voids, based on [1], which builds upon operational data

from [3]. The life cycle inventory for this process includes the consumption of materials such as cement binders, slags, and fuel, and accounting for the associated energy demands. Backfilling is assumed to involve cement stabilization of the residues, effectively preventing leaching emissions from the deposited material.

• Flocculation-flotation, based on [4], where the sulfur-rich fraction from the tailings

stream is separated using polyacrylamide and xanthate as flocculants and collector agents to improve pyrite separation. The valorized output can potentially be used downstream in the cement and ceramic tiles industries.

• Roasting and leaching, also based on [4], involves first removing the sulfur content of tailings

through drying and roasting. Copper and zinc are then recovered using a combination of ammoniacal leaching, ion flotation, and chemical precipitation.

In the default ecoinvent system, all sulfidic tailings are treated via impoundment. The table below presents regional estimates for the uptake of the various alternatives, along with the references used to approximate the data points or inform the underlying trends.

These newly introduced treatment pathways represent a more energy- and material-intensive treatment alternative compared to impoundments. However, they also provide a means of significantly reducing, or potentially eliminating, leachate-related emissions, which are critical environmental burden of tailings disposal. The modeled transition thus captures the trade-offs between higher resource consumption and the mitigation of long-term pollution risks. The assumed reduction in impoundments reflects broader trends in the industry toward more sustainable and circular tailings management practices, supported by technological innovation and emerging environmental regulation [5].

Regional estimates for sulfidic tailings treatment uptake

Region	Backfilling 2020	Backfilling 2050	Impoundment 2020	Impoundment 2050	Ref. (BF/Imp)	Floc-Flotation 2020	Floc-Flotation 2050	Roasting & Leaching 2020	Roasting & Leaching 2050	Ref. (Floc/R&L)
North America	15%	30%	80%	60%	[6], [7]	4%	8%	1%	2%	[1], [8], [9], [10]
LATAM	5%	25%	90%	65%	[7], [11]	4%	8%	1%	2%	[1], [8], [9], [10]
Europe	15%	35%	80%	55%	[1], [12]	4%	8%	1%	2%	[1], [8], [9], [10]
APAC	10%	20%	85%	70%	[13], [14]	4%	8%	1%	2%	[1], [8], [9], [10]
Africa	5%	10%	90%	70%	[8], [15], [16]	4%	8%	1%	2%	[1], [8], [9], [10]
Global	10%	25%	85%	65%	[7], [8], [16]	4%	8%	1%	2%	[1], [8], [9], [10]

[1]

https://doi.org/10.1016/j.scitotenv.2023.162038

[2]

https://doi.org/10.1016/j.jclepro.2017.03.129

[3]

http://dx.doi.org/10.1016/j.jclepro.2008.08.014

[4]

https://doi.org/10.1016/j.resconrec.2022.106567

[5]

https://doi.org/10.1016/j.clet.2022.100499

[6]

https://www.hecla.com/wp-content/uploads/Hecla-2021-TailingsMgmt_FactSheet.pdf

[7]

https://globaltailingsreview.org/wp-content/uploads/2020/08/global-industry-standard-on-tailings-management.pdf

[8]

https://globaltailingsreview.org/wpcontent/uploads/2020/09

[9]

https://www.nature.com/articles/s41598-021-84897-0

[10]

http://dx.doi.org/10.1016/j.jclepro.2019.01.312

[11]

https://doi.org/10.3390/su141710901

[12]

https://ec.europa.eu/environment/pdf/waste/mining/MWEI%20BREF.pdf.

[13]

https://doi.org/10.3390/rs13040743

[14]

https://doi.org/10.3389/feart.2021.726336

[15]

https://sancold.org.za/wp-content/uploads/2024/05/SANCOLD-Your-Tailings-Dam-2020.pdf

[16]

https://globaltailingsreview.org/wp-content/uploads/2020/09/Ch-II-Mine-Tailings-Facilities_Overview-and-Industry-Trends.pdf

Inventories

premise provides several inventories regarding updated mining practices:

• `Alternative treatment pathways for sulfidic tailings

<https://github.com/polca/premise/blob/master/premise/data/additional_inventories/lci-sulfidic-tailings.xlsx>`_.

The inventories are provided under premise/data/additional_inventories

Biomass

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("biomass")

Key outputs

• Regionalizes biomass supply and forestry activities by IAM region.

• Updates biomass markets and relinks consuming activities accordingly.

Regional biomass markets

premise creates regional markets for biomass which is meant to be used as fuel in biomass-fired powerplants or heat generators. Originally in ecoinvent, the biomass being supplied to biomass-fired powerplants is “purpose grown” biomass that originate forestry activities (called “market for wood chips” in ecoinvent). While this type of biomass is suitable for such purpose, it is considered a co-product of the forestry activity, and bears a share of the environmental burden of the process it originates from (notably the land footprint, emissions, potential use of chemicals, etc.).

However, not all the biomass projected to be used in IAM scenarios is “purpose grown”. In fact, significant shares are expected to originate from forestry residues. In such cases, the environmental burden of the forestry activity is entirely allocated to the determining product (e.g., timber), not to the residue, which comes “free of burden”.

Hence, premise creates average regional markets for biomass, which represents the average shares of “purpose grown” and “residual” biomass being fed to biomass-fired powerplants.

The following market is created for each IAM region:

market name	location
market for biomass, used as fuel	all IAM regions

inside of which, the shares of “purpose grown” and “residual” biomass is represented by the following activities:

• market for wood chips (for “purpose grown” biomass)

• market for wood chips (for “purpose grown” woody biomass)

• supply of forest residue (for “residual” biomass)

The sum of those shares equal 1. The activity “supply of forest residue” includes the energy, embodied biogenic CO2, transport and associated emissions to chip the residual biomass and transport it to the powerplant, but no other forestry-related burden is included.

Note

You can check the share of residual biomass used for power generation assumed in your scenarios by generating a scenario summary report.

Note

When running premise with the consequential method, the biomass market is only composed of purpose-grown biomass. This is because the residual biomass cannot be considered a marginal supplier for an increase in demand for biomass.

ndb.generate_scenario_report()

Power generation

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx',
    use_absolute_efficiency=False # default
)
ndb.update("electricity")

Key outputs

• Builds regional electricity markets (high/medium/low voltage) and relinks consumers.

• Updates generation technology shares and efficiencies using IAM outputs.

• Applies technology-specific corrections (e.g., hydropower water emissions, PV/wind regionalization).

Efficiency adjustment

The energy conversion efficiency of power plant datasets for specific technologies is adjusted to align with the efficiency changes indicated by the IAM scenario.

Two approaches are possible (use_absolute_efficiency):

• application of a scaling factor to the inputs of the dataset relative to the current efficiency

• application of a scaling factor to the inputs of the dataset to match the absolute efficiency given by the IAM scenario

The first approach (default) preserves the relative share of inputs in the dataset, as reported in ecoinvent, while the second approach adjusts the inputs to match the absolute efficiency given by the IAM scenario.

Combustion-based powerplants

First, premise adjust the efficiency of coal- and lignite-fired power plants on the basis of the excellent work done by Oberschelp et al. (2019), to update some datasets in ecoinvent, which are, for some of them, several decades old. More specifically, the data provides plant-specific efficiency and emissions factors. We average them by country and fuel type to obtain volume-weighted factors. The efficiency of the following datasets is updated:

• electricity production, hard coal

• electricity production, lignite

• heat and power co-generation, hard coal

• heat and power co-generation, lignite

The data from Oberschelp et al. (2019) also allows us to update emissions of SO2, NOx, CH4, and PMs.

Second, premise iterates through coal, lignite, natural gas, biogas, and wood-fired power plant datasets in the LCI database to calculate their current efficiency (i.e., the ratio between the primary fuel energy entering the process and the output energy produced, which is often 1 kWh). If the IAM scenario anticipates a change in efficiency for these processes, the inputs of the datasets are scaled up or down by the scaling factor to effectively reflect a change in fuel input per kWh produced.

The origin of this scaling factor is the IAM scenario selected.

To calculate the old and new efficiency of the dataset, it is necessary to know the net calorific content of the fuel. The table below shows the Lower Heating Value for the different fuels used in combustion-based power plants.

name of fuel	LHV [MJ/kg, as received]
hard coal	26.7
lignite	11.2
petroleum coke	31.3
wood pellet	16.2
wood chips	18.9
natural gas	45
gas, natural, in ground	45
refinery gas	50.3
propane	46.46
heavy fuel oil	38.5
oil, crude, in ground	38.5
light fuel oil	42.6
biogas	22.73
biomethane	47.5
waste	14
methane, fossil	47.5
methane, biogenic	47.5
methane, synthetic	47.5
diesel	43
gasoline	42.6
petrol, 5% ethanol	41.7
petrol, synthetic, hydrogen	42.6
petrol, synthetic, coal	42.6
diesel, synthetic, hydrogen	43
diesel, synthetic, coal	43
diesel, synthetic, wood	43
diesel, synthetic, wood, with CCS	43
diesel, synthetic, grass	43
diesel, synthetic, grass, with CCS	43
hydrogen, petroleum	120
hydrogen, electrolysis	120
hydrogen, biomass	120
hydrogen, biomass, with CCS	120
hydrogen, coal	120
hydrogen, from natural gas	120
hydrogen, from natural gas, with CCS	120
hydrogen, biogas	120
hydrogen, biogas, with CCS	120
hydrogen	120
biodiesel, oil	38
biodiesel, oil, with CCS	38
bioethanol, wood	26.5
bioethanol, wood, with CCS	26.5
bioethanol, grass	26.5
bioethanol, grass, with CCS	26.5
bioethanol, grain	26.5
bioethanol, grain, with CCS	26.5
bioethanol, sugar	26.5
bioethanol, sugar, with CCS	26.5
ethanol	26.5
methanol, wood	19.9
methanol, grass	19.9
methanol, wood, with CCS	19.9
methanol, grass, with CCS	19.9
liquified petroleum gas, natural	45.5
liquified petroleum gas, synthetic	45.5
uranium, enriched 3.8%, in fuel element for light water reactor	4199040
nuclear fuel element, for boiling water reactor, uo2 3.8%	4147200
nuclear fuel element, for boiling water reactor, uo2 4.0%	4147200
nuclear fuel element, for pressure water reactor, uo2 3.8%	4579200
nuclear fuel element, for pressure water reactor, uo2 4.0%	4579200
nuclear fuel element, for pressure water reactor, uo2 4.2%	4579200
uranium hexafluoride	709166
enriched uranium, 4.2%	4579200
mox fuel element	4579200
heat, from hard coal	1
heat, from lignite	1
heat, from petroleum coke	1
heat, from wood pellet	1
heat, from natural gas, high pressure	1
heat, from natural gas, low pressure	1
heat, from heavy fuel oil	1
heat, from light fuel oil	1
heat, from biogas	1
heat, from waste	1
heat, from methane, fossil	1
heat, from methane, biogenic	1
heat, from diesel	1
heat, from gasoline	1
heat, from bioethanol	1
heat, from biodiesel	1
heat, from liquified petroleum gas, natural	1
heat, from liquified petroleum gas, synthetic	1
bagasse, from sugarcane	15.4
bagasse, from sweet sorghum	13.8
sweet sorghum stem	4.45
cottonseed	21.97
flax husks	21.5
coconut husk	20
sugar beet pulp	5.11
cleft timber	14.46
rape meal	31.1
molasse, from sugar beet	16.65
sugar beet	4.1
barkey grain	19.49
rye grain	12
sugarcane	5.3
palm date	10.8
whey	1.28
straw	15.5
grass	17
manure, liquid	0.875
manure, solid	3.6
kerosene, from petroleum	43
kerosene, synthetic, from electrolysis, energy allocation	43
kerosene, synthetic, from electrolysis, economic allocation	43
kerosene, synthetic, from coal, energy allocation	43
kerosene, synthetic, from coal, economic allocation	43
kerosene, synthetic, from natural gas, energy allocation	43
kerosene, synthetic, from natural gas, economic allocation	43
kerosene, synthetic, from biomethane, energy allocation	43
kerosene, synthetic, from biomethane, economic allocation	43
kerosene, synthetic, from biomass, energy allocation	43
kerosene, synthetic, from biomass, economic allocation	43

Additionally, the biogenic and fossil CO2 emissions of the datasets are also scaled up or down by the same factor, as they are proportional to the amount of fuel used.

Below is an example of a natural gas power plant with a current (2020) conversion efficiency of 77%. If the IAM scenario indicates a scaling factor of 1.03 in 2030, this suggests that the efficiency increases by 3% relative to the current level. As shown in the table below, this would result in a new efficiency of 79%, where all inputs, as well as CO2 emissions outputs, are re-scaled by 1/1.03 (=0.97).

While non-CO2 emissions (e.g., CO) are reduced because of the reduction in fuel consumption, the emission factor per energy unit remains the same (i.e., gCO/MJ natural gas)). It can be re-scaled using the .update(“emissions”) function, which updates emission factors according to GAINS projections.

electricity production, natura gas, conventional	before	after	unit
electricity production	1	1	kWh
natural gas	0.1040	0.1010	m3
water	0.0200	0.0194	m3
powerplant construction	1.00E-08	9.71E-09	unit
CO2, fossil	0.0059	0.0057	kg
CO, fossil	5.87E-06	5.42E-03	kg
fuel-to-electricity efficiency	77%	79%	%

premise has a couple of rules regarding projected scaling factors:

• scaling factors inferior to 1 beyond 2020 are not accepted and are treated as 1.

• scaling factors superior to 1 before 2020 are not accepted and are treated as 1.

• efficiency can only improve over time.

This is to prevent degrading the performance of a technology in the future, or improving its performance in the past, relative to today.

Note

You can check the efficiencies assumed in your scenarios by generating a scenario summary report, or a report of changes. They are automatically generated after each database export, but you can also generate them manually:

ndb.generate_scenario_report()
ndb.generate_change_report()

Photovoltaics panels

Photovoltaic installation and construction datasets are updated from the module-efficiency trajectories stored in premise/data/renewables/efficiency_solar_PV.csv. The latest revision of that file refreshes the PV assumptions with recent record-based and roadmap-based anchors and adds descriptive metadata for traceability.

premise currently reads only the columns technology, year, mean, min and max when building the efficiency time series. The additional columns source, metric_level, maturity, basis, use_for_projection and review_notes are retained in the CSV for documentation and scenario curation, but are not yet consumed directly by the transformation code.

The current anchor years in the CSV are 2010, 2020, 2023, 2025, 2027, 2030, 2035 and 2050. The latest update notably adds:

Selected additions in the current PV efficiency file

Technology family	Anchor years	Notes
single-Si	2025, 2035	25.4% module record in 2025 (NREL Champion Module Efficiencies, revision 2024-12-18) and a 25.23% mainstream silicon projection in 2035 (NREL Spring 2025 Solar Industry Update / ITRPV).
CIGS and CdTe	2025	2025 record-module anchors of 19.2% and 19.9%, respectively.
GaAs	2010, 2020, 2025	Historical anchors were revised to keep a monotonic progression toward the 25.1% 2025 record module value.
perovskite	2025	21.1% single-junction module record in 2025; kept separate from tandem modules.
perovskite-Si tandem	2027, 2030, 2035	New dedicated tandem trajectory for advanced scenarios: 27.0%, 30.0% and 30.5%.
multi-Si	2025	20.4% 2025 record retained as a legacy sensitivity case rather than a mainstream future pathway.
micro-Si and CIS	existing 2010, 2020, 2050 anchors	Retained as niche or legacy trajectories.

The CSV sources now combine the historical Fraunhofer ISE / literature values already used in premise with newer NREL champion-module records and tandem roadmap projections (ITRPV and Oxford PV) for near- and medium-term updates. The main references currently cited in the CSV are:

• Historical thin-film and silicon anchors: IEA PV roadmap (https://iea.blob.core.windows.net/assets/3a99654f-ffff-469f-b83c-bf0386ed8537/pv_roadmap.pdf), Treeze / IEA PVPS Task 12 (https://treeze.ch/fileadmin/user_upload/downloads/Publications/Case_Studies/Energy/Future-PV-LCA-IEA-PVPS-Task-12-March-2015.pdf) and Fraunhofer ISE Photovoltaics Report (https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf).

• CdTe anchors: Solar Energy Materials and Solar Cells article (https://www.sciencedirect.com/science/article/pii/S0927024823001101).

• GaAs long-term anchor: The International Journal of Life Cycle Assessment article (https://link.springer.com/article/10.1007/s11367-020-01791-z).

• Perovskite long-term anchor: RSC Energy & Environmental Science article (https://pubs.rsc.org/en/content/articlelanding/2022/se/d2se00096b) and CSEM note on a 31.25% cell result (https://www.csem.ch/en/news/photovoltaic-technology-breakthrough-achieving-31.25-efficiency/).

• 2025 record-module anchors for single-Si, multi-Si, CIGS, CdTe, GaAs and perovskite: NREL Champion Module Efficiencies, revision 2024-12-18 (https://www.nrel.gov/docs/libraries/pv/champion-module-efficiencies.pdf).

• 2035 mainstream silicon and tandem projections: NREL Spring 2025 Solar Industry Update / ITRPV (https://docs.nrel.gov/docs/fy25osti/95135.pdf).

• 2027 tandem commercialization anchor: ITRPV 15th edition 2024 (https://www.qualenergia.it/wp-content/uploads/2024/06/ITRPV-15th-Edition-2024-2.pdf).

• 2030 tandem midpoint: Oxford PV roadmap page (https://www.oxfordpv.com/mainstream).

For full row-level attribution, refer directly to premise/data/renewables/efficiency_solar_PV.csv.

Overview of the photovoltaic module-efficiency trajectories currently encoded in premise/data/renewables/efficiency_solar_PV.csv, including source labels and min-max uncertainty bands.

The sources for these efficiencies are also given in the inventory file LCI_PV:

Given a scenario year, premise iterates through the different PV panel installation datasets to update their efficiency accordingly. To do so, the required surface of panel (in m2) per kW of capacity is adjusted down (or up, if the efficiency is lower than current). Dataset names are matched against technology aliases in premise/electricity.py. With the latest update, datasets containing perovskite-on-silicon tandem are mapped to the dedicated perovskite-Si tandem trajectory when it is available in the CSV; when older CSV files are used they fall back to the generic perovskite trajectory.

To calculate the current efficiency of a PV installation, premise assumes a solar irradiation of 1000 W/m2. Hence, the current efficiency is calculated as:

current_eff [%] = installation_power [W]  / (panel_surface [m2] * 1000 [W/m2])

The scaling factor is calculated as:

scaling_factor = current_eff / new_eff

The required surface of PV panel in the dataset is then adjusted like so:

new_surface = current_surface * scaling_factor

The mean, minimum and maximum module efficiencies are propagated as a triangular uncertainty on the panel surface exchange. For years between anchor points, premise interpolates the efficiency values linearly. For years outside the CSV range, it extrapolates them linearly and clips the resulting efficiencies to the 10-30% interval. The update is applied only when the projected mean efficiency is higher than the efficiency inferred from the existing dataset.

The table below provides such an example where a 450 kWp flat-roof installation sees its current (2020) module efficiency improving from 20% to 27% by 2050. The area of PV panel (and mounting system) and the end-of-life treatment flow are multiplied by 0.20 / 0.27 = 0.74, all other inputs remaining unchanged.

450kWp flat roof installation	before	after	unit
photovoltaic flat-roof installation, 450 kWp, single-SI, on roof	1	1	unit
inverter production, 500 kW	1.5	1.5	unit
photovoltaic mounting system, …	2300	1704	m2
photovoltaic panel, single-SI	2500	1852	m2
treatment, single-SI PV module	30000	22222	kg
electricity, low voltage	25	25	kWh
module efficiency	20%	27%	%

Markets

premise creates additional datasets that represent the average supply and production pathway for a given commodity for a given scenario, year and region.

Such datasets are called regional markets. Hence, a regional market for high voltage electricity contains the different technologies that supply electricity at high voltage in a given IAM region, in proportion to their respective production volumes.

Regional electricity markets

High voltage regional markets

premise creates high, medium and low-voltage electricity markets for each IAM region. It starts by creating high-voltage markets and define the share of each supplying technology by their respective production volumes in respect to the total volume produced.

High voltage supplying technologies are all technologies besides:

• residential (<=3kWp) photovoltaic power (low voltage)

• waste incineration co-generating powerplants (medium voltage)

Several datasets can qualify for a given technology, in a given IAM region. To define to which extent a given dataset should be supplying in the market, premise uses the current production volume of the dataset.

For example, if coal-fired powerplants are to supply 25% of the high voltage electricity in the IAM region “Europe”, premise fetches the production volumes of all coal-fired powerplants which ecoinvent location is included in the IAM region “Europe” (e.g., DE, PL, LT, etc.), and allocates to each of those a supply share based on their respective production volume in respect to the total production volume of coal-fired powerplants.

For example, the table below shows the contribution of biomass-fired CHP powerplants in the regional high voltage electricity market for IMAGE’s “WEU” region (Western Europe). The biomass CHP technology represents 2.46% of the supply mix. Biomass CHP datasets included in the region “WEU” are given a supply share corresponding to their respective current production volumes.

energy type	Supplier name	Supplier location	Contribution within energy type	Final contribution
Biomass CHP	heat and power co-generation, wood chips	FR	3.80%	0.09%
Biomass CHP	heat and power co-generation, wood chips	AT	2.87%	0.07%
Biomass CHP	heat and power co-generation, wood chips	NO	0.06%	0.00%
Biomass CHP	heat and power co-generation, wood chips	FI	7.65%	0.19%
Biomass CHP	heat and power co-generation, wood chips	SE	9.04%	0.22%
Biomass CHP	heat and power co-generation, wood chips	IT	8.27%	0.20%
Biomass CHP	heat and power co-generation, wood chips	BE	4.59%	0.11%
Biomass CHP	heat and power co-generation, wood chips	DE	12.53%	0.31%
Biomass CHP	heat and power co-generation, wood chips	LU	0.05%	0.00%
Biomass CHP	heat and power co-generation, wood chips	DK	6.60%	0.16%
Biomass CHP	heat and power co-generation, wood chips	GR	0.01%	0.00%
Biomass CHP	heat and power co-generation, wood chips	CH	1.81%	0.04%
Biomass CHP	heat and power co-generation, wood chips	ES	5.10%	0.13%
Biomass CHP	heat and power co-generation, wood chips	PT	1.34%	0.03%
Biomass CHP	heat and power co-generation, wood chips	IE	0.77%	0.02%
Biomass CHP	heat and power co-generation, wood chips	NL	2.32%	0.06%
Biomass CHP	heat and power co-generation, wood chips	GB	33.18%	0.81%
_	_	Sum	100.00%	2.46%

Transformation losses are added to the high-voltage market datasets. Transformation losses are the result of weighting country-specific high voltage losses (provided by ecoinvent) of countries included in the IAM region with their respective current production volumes (also provided by ecoinvent). This is not ideal as it supposes that future country-specific production volumes will remain the same in respect to one another.

High voltage regional markets for aluminium smelters

Aluminium production is a significant consumer of electricity. In the ecoinvent database, aluminium smelters are represented by specific electricity markets. Conversely, Integrated Assessment Models (IAM) scenarios aggregate the electricity consumption of aluminium smelters with that of other electricity consumers.

To improve accuracy, it is necessary to align the electricity markets of aluminium producers with regional electricity markets. However, certain aluminium electricity markets have already achieved substantial decarbonization, primarily due to the use of hydroelectric power in some smelters.

Therefore, premise integrates aluminium smelters into regional electricity markets only for those regions that have not yet undergone significant decarbonization. The regions affected are:

• Rest of World (RoW)

• IAI Area, Africa

• China (CN)

• IAI Area, South America

• United Nations Oceania (UN-OCEANIA)

• IAI Area, Asia excluding China and Gulf Cooperation Council (GCC)

• IAI Area, Gulf Cooperation Council (GCC)

Meanwhile, premise maintains the current decarbonized electricity markets for aluminium smelters in the following regions:

• IAI Area, Russia & Rest of Europe excluding EU27 & EFTA

• Canada (CA)

• IAI Area, EU27 & EFTA

Although the future development of aluminium-specific electricity markets remains uncertain, it is reasonable to hypothesize that these markets will follow the decarbonization trends of their respective regions. Consequently, aligning the carbon-intensive electricity markets of aluminium smelters with regional electricity markets is likely more accurate than retaining the current setup.

In fact, such approach has been used by the International Aluminium Industry association itself, in their Aluminium Sector Greenhouse Gas Pathways to 2050 Roadmap_, where they connected the electricity consumption of aluminium smelters to future regional mixes defined by the International Energy Agency (IEA).

Storage

If the IAM scenario requires the use of storage, premise adds a storage dataset to the high voltage market. premise can add two types of storage:

• storage via a large-scale flow battery (electricity supply, high voltage, from vanadium-redox flow battery system)

• storage via the conversion of electricity to hydrogen and subsequent use in a gas turbine (electricity production, from hydrogen-fired one gigawatt gas turbine)

The electricity storage via battery incurs a 33% loss. It is operated by a 8.3 MWh vanadium redox-based flow battery, with a lifetime of 20 years or 8176 cycle-lifes (i.e., 49,000 MWh).

The storage of electricity via hydrogen is done in two steps: first, the electricity is converted to hydrogen via a 1MW PEM electrolyser, with an efficiency of 62%. The hydrogen is then stored in a geological cavity and used in a gas turbine, with an efficiency of 51%. Accounting for leakages and losses, the overall efficiency of the process is about 37% (i.e., 2.7 kWh necessary to deliver 1 kWh to the grid).

The efficiency of the H2-fed gas turbine is based on the parameters of Ozawa et al. (2019).

Medium voltage regional markets

The workflow is not too different from that of high voltage markets. There are however only two possible providers of electricity in medium voltage markets: the high voltage market, as well as waste incineration powerplants.

High-to-medium transformation losses are added as an input of the medium voltage market to itself. Distribution losses are modelled the same way as for high voltage markets and are added to the input from high voltage market.

Low voltage regional markets

Low voltage regional markets receive an input from the medium voltage market, as well as from residential photovoltaic power.

Medium-to-low transformation losses are added as an input from the low voltage market to itself. Distribution losses are modelled the same way as for high and medium voltage markets, and are added to the input from the medium voltage market.

The table below shows the example of a low voltage market for the IAM IMAGE regional “WEU”.

supplier	amount	unit	location	description
market group for electricity, medium voltage	1.023880481	kilowatt hour	WEU	input from medium voltage + distribution losses
market group for electricity, low voltage	0.025538286	kilowatt hour	WEU	transformation losses (2.55%)
electricity production, photovoltaic, residential	0.00035691	kilowatt hour	DE
electricity production, photovoltaic, residential	0.000143875	kilowatt hour	IT
electricity production, photovoltaic, residential	9.38E-05	kilowatt hour	ES
electricity production, photovoltaic, residential	9.03E-05	kilowatt hour	GB
electricity production, photovoltaic, residential	7.82E-05	kilowatt hour	FR
electricity production, photovoltaic, residential	6.80E-05	kilowatt hour	NL
electricity production, photovoltaic, residential	3.76E-05	kilowatt hour	BE
electricity production, photovoltaic, residential	2.16E-05	kilowatt hour	GR
electricity production, photovoltaic, residential	2.08E-05	kilowatt hour	CH
electricity production, photovoltaic, residential	1.48E-05	kilowatt hour	AT
electricity production, photovoltaic, residential	9.44E-06	kilowatt hour	SE
electricity production, photovoltaic, residential	8.66E-06	kilowatt hour	DK
electricity production, photovoltaic, residential	6.83E-06	kilowatt hour	PT
electricity production, photovoltaic, residential	2.60E-06	kilowatt hour	FI
electricity production, photovoltaic, residential	1.30E-06	kilowatt hour	LU
electricity production, photovoltaic, residential	1.01E-06	kilowatt hour	NO
electricity production, photovoltaic, residential	2.40E-07	kilowatt hour	IE
distribution network construction, electricity, low voltage	8.74E-08	kilometer	RoW
market for sulfur hexafluoride, liquid	2.99E-09	kilogram	RoW
sulfur hexafluoride	2.99E-09	kilogram		transformer emissions

Note

You can check the electricity supply mixes assumed in your scenarios by generating a scenario summary report.

ndb.generate_scenario_report()

Long-term regional electricity markets

Long-term (i.e., 20, 40 and 60 years) regional markets are created for modelling the lifetime-weighted burden associated to electricity supply for systems that have a long lifetime (e.g., battery electric vehicles, buildings).

These long-term markets contain a period-weighted electricity supply mix. For example, if the scenario year is 2030 and the period considered is 20 years, the supply mix represents the supply mixes between 2030 and 2050, with an equal weight given to each year.

The rest of the modelling is similar to that of regular regional electricity markets described above.

Original market datasets

Market datasets originally present in the ecoinvent LCI database are cleared from any inputs. Instead, an input from the newly created regional market is added, depending on the location of the dataset.

The table below shows the example of the low voltage electricity market for Great Britain, which now only includes an input from the “WEU” regional market, which “includes” it in terms of geography.

Output	_	_	_
producer	amount	unit	location
market for electricity, low voltage	1.00E+00	kilowatt hour	GB
Input	_	_	_
supplier	amount	unit	location
market group for electricity, low voltage	1.00E+00	kilowatt hour	WEU

Relinking

Once the new markets are created, premise re-links all electricity-consuming activities to the new regional markets. The regional market it re-links to depends on the location of the consumer.

Cement production

The modelling of future improvements in the cement sector is dependent on the IAM model chosen.

When choosing IMAGE, scenarios include the emergence of a new, more efficient kiln, as well as kilns fitted with three types of carbon capture technologies:

• using monoethanolamine (MEA) as a solvent,

• using oxyfuel combustion,

• using Direct Separation (Leilac process).

The implementation of the corresponding datasets for these new kiln technologies are based on the work of Muller et al., 2024.

We differ slightly from the implementation of Muller et al., 2024, in that:

• the heat necessary for the regeneration of the MEA solvent is assumed to be provided by a natural gas boiler (instead of a fuel mix resembling that of the kiln itself), with up to 30% coming from recovered heat from the kiln by 2050,

• the amount of heat needed for the regeneration of the MEA solvent goes from 3.76 GJ/ton CO2 in 2020, to 2.6 GJ/ton CO2 in 2050,

• the provision of oxygen for the Direct Separation option comes from an existing air separation dataset from ecoinvent,

• the fuel mix for the kiln is that of ecoinvent, further scaled down by the change of efficiency of the kiln (in Müller et al., 2024, they use directly the fuel mix provided by the IMAGE scenario, which we do not find representative, as it also includes the fuel used by other activities in the non-metallic minerals, notably a large share of natural gas).

In a nutshell, premise:

• makes copies of the clinker production dataset,

• adjusts the fuel consumption and related CO2 emissions,

• adjusts specific hot pollutant emissions removed by the carbon capture process (Mercury, NOx, SOx),

• adds an input from the carbon capture process, based on a capture efficiency share,

• and removes a corresponding amount from the outgoing CO2 emissions.

The Direct Separation process only captures calcination emissions, while the other two technologies capture both combustion and calcination emissions.

When choosing another IAM (e.g., REMIND, TIAM-UCL), the current implementation is relatively simpler at the moment, and does not involve the emergence of new technologies. In these scenarios, the production volumes of kilns equipped with CCS is not given. Instead, the share of CO2 emissions that is sequestered is given. We use the ratio of the CO2 emissions sequestered over the total CO2 emissions to determine the share of the CO2 emissions that is sequestered in the clinker production dataset

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("cement")

Key outputs

• Duplicates clinker production routes per IAM region and rebuilds markets.

• Scales fuel inputs and process efficiencies using IAM projections.

• Integrates CCS options where modeled and adjusts captured CO2 flows.

Dataset proxies

premise duplicates clinker production datasets in ecoinvent (called “clinker production”) so as to create a proxy dataset for each IAM region. The location of the proxy datasets used for a given IAM region is a location included in the IAM region. If no valid dataset is found, premise resorts to using a rest-of-the-world (RoW) dataset to represent the IAM region.

premise changes the location of these duplicated datasets and fill in different fields, such as that of production volume.

Efficiency adjustment

premise then adjusts the thermal efficiency of the process.

It first calculates the energy input in the current (original) dataset, by looking up the fuel inputs and their respective lower heating values.

Once the energy required per ton clinker today (2020) is known, it is multiplied by a scaling factor that represents a change in efficiency between today and the scenario year.

Note

You can check the efficiency gains assumed relative to 2020 in your scenarios by generating a scenario summary report.

ndb.generate_scenario_report()

Note

premise enforces a lower limit on the fuel consumption per ton of clinker. This limit is set to 3.1 GJ/t clinker and is close to the minimum theoretical fuel consumption with an moisture content of the raw materials, as considered in the 2018 IEA cement roadmap report (i.e., 2.8 GJ/t clinker). Hence, regardless of the scaling factor, the fuel consumption per ton of clinker will never be less than 3.1 GJ/t.

Once the new energy input is determined, premise scales down the fuel, and the fossil and biogenic CO2 emissions accordingly, based on the Lower Heating Value and CO2 emission factors for these fuels.

Note that the change in CO2 emissions only concerns the share that originates from the combustion of fuels. It does not concern the calcination emissions due to the production of calcium oxide (CaO) from calcium carbonate (CaCO3), which is set at a fix emission rate of 525 kg CO2/t clinker.

Carbon Capture and Storage

If the IAM scenario indicates that a share of the CO2 emissions for the cement sector in a given region and year is sequestered and stored, premise adds CCS to the corresponding clinker production dataset.

The CCS dataset used to that effect is from Muller et al., 2024. The dataset described the capture of CO2 from a cement plant, using a monoethanolamine-based sorbent. To that dataset, premise adds another dataset that models the storage of the CO2 underground, from Volkart et al, 2013.

Besides electricity, the CCS process requires heat, water and others inputs to regenerate the amine-based sorbent. We use two data points to approximate the heat requirement: 3.76 MJ/kg CO2 captured in 2020 (minus 30% coming from the kiln as recovered heat), and 2.6 MJ/kg in 2050. The first number is from Muller et al., 2024, while the second number is described as the best-performing pilot project today, according to the 2022 review of pilot projects by the Global CCS Institute. It is further assumed that the heat requirement is fulfilled to an extent of 30% by the recovery of excess heat, as found in numerous studies.

Note

You can check the the carbon capture rate for cement production assumed in your scenarios by generating a scenario summary report.

ndb.generate_scenario_report()

Cement markets

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("cement")

When clinker production datasets are created for each IAM region, premise duplicates cement production datasets for each IAM region as well. These cement production datasets link the newly created clinker production dataset, corresponding to their IAM region.

Clinker-to-cement ratio

premise used to modify the composition of cement markets to reflect a lower clinker content over time, based on external projections. This is no longer performed, as it is not an assumption stemming from the IAM model, but rather a projection of the cement industry.

Original market datasets

Market datasets originally present in the ecoinvent LCI database are cleared from any inputs. Instead, an input from the newly created regional market is added, depending on the location of the dataset.

The table below shows the example of the clinker market for South Africa, which now only includes an input from the “SAF” regional market, which “includes” it in terms of geography.

Output	_	_	_
producer	amount	unit	location
market for clinker	1.00E+00	kilogram	ZA
Input	_	_	_
supplier	amount	unit	location
market for clinker	1.00E+00	kilogram	*SAF

Relinking

Once cement production and market datasets are created, premise re-links cement-consuming activities to the new regional markets for cement. The regional market it re-links to depends on the location of the consumer.

Steel production

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("steel")"

Key outputs

• Creates region-specific proxy datasets for multiple steel production routes.

• Scales energy inputs and direct CO2 emissions based on IAM efficiency trends.

• Adds CCS-linked variants where IAM indicates sequestration.

The modelling of future improvements in the steel sector is now based on multiple explicit production routes for both primary and secondary steel, rather than only modifying the two generic ecoinvent datasets. This allows ``premise` to represent different process configurations (e.g., blast furnace–basic oxygen furnace, direct reduced iron with natural gas, electric arc furnace with varying scrap shares) and to adapt their energy requirements and emissions according to scenario projections.

Dataset proxies

premise creates proxy datasets for each IAM region by duplicating all available steel production routes defined in its internal library (see lci-steel.xlsx). These include, for example:

Primary routes: BF–BOF with different coke/natural gas shares, NG-DRI + EAF, etc.

Secondary routes: Scrap-based EAF with different efficiencies.

These inventories are based on the work from Harpprechet et al., 2025.

For each IAM region, premise changes the location of these duplicated datasets to match the IAM region (falling back to RoW if no valid location exists), and updates key metadata such as production volume.

Efficiency adjustment

For each steel production route, premise applies efficiency improvements projected by the IAM scenario:

For primary steel routes, energy and fuel inputs (coal, coke, natural gas, electricity) are scaled by the scenario-specific factor. Direct CO₂ emissions are adjusted accordingly.

For secondary steel routes (EAF), the electricity input is scaled with the scenario factor, representing improved efficiency of scrap-based production.

These adjustments are route-specific: each proxy dataset retains the structure of its underlying process (e.g., DRI vs. BF–BOF), but its energy intensity evolves in line with the scenario.

Note

You can check the efficiency gains assumed relative to 2020 for steel production in your scenarios by generating a scenario summary report.

ndb.generate_scenario_report()

Warning

If your system of interest relies heavily on the provision of steel, you should probably consider modelling steel production based on primary data. ecoinvent datasets for steel production rely on a few data points, which are then further process transformed by premise. Therefore, there is a large modelling uncertainty.

Carbon Capture and Storage

If the IAM scenario indicates that a share of the CO2 emissions from the steel sector in a given region and year is sequestered and stored, premise adds a corresponding input from a CCS dataset.

To that dataset, premise adds another dataset that models the storage of the CO2 underground, from Volkart et al, 2013.

The material and energy requirements of the CCS process are from the work of Harpprechet et al., 2025.

Steel markets

premise create a dataset “market for steel, low-alloyed” and “market for steel, unalloyed” for each IAM region. Within each dataset, the supply shares of primary and secondary steel are adjusted to reflect the projections from the IAM scenario, for a given region and year, based on the variables described in the steel mapping file.

The table below shows an example of the market for India, where 66% of the steel comes from an oxygen converter process (primary steel), while 34% comes from an electric arc furnace process (secondary steel).

Output	_	_	_
producer	amount	unit	location
market for steel, low-alloyed	1	kilogram	IND
Input
supplier	amount	unit	location
market group for transport, freight, inland waterways, barge	0.5	ton kilometer	GLO
market group for transport, freight train	0.35	ton kilometer	GLO
market for transport, freight, sea, bulk carrier for dry goods	0.38	ton kilometer	GLO
transport, freight, lorry, unspecified, regional delivery	0.12	ton kilometer	IND
steel production, blast furnace-basic oxygen furnace, unalloyed	0.66	kilogram	IND
steel production, blast furnace-basic oxygen furnace, with carbon capture and storage, unalloyed	0.05	kilogram	IND
steel production, natural gas-based direct reduction iron-electric arc furnace, unalloyed	0.04	kilogram	IND
steel production, natural gas-based direct reduction iron-electric arc furnace, with carbon capture and storage, unalloyed	0.05	kilogram	IND
steel production, blast furnace-basic oxygen furnace, with top gas recycling, unalloyed	0.08	kilogram	IND
steel production, blast furnace-basic oxygen furnace, with top gas recycling, with carbon capture and storage, unalloyed	0.02	kilogram	IND
steel production, electric, low-alloyed	0.10	kilogram	IND

Original market datasets

Market datasets originally present in the ecoinvent LCI database are cleared from any inputs. Instead, an input from the newly created regional market is added, depending on the location of the dataset.

Relinking

Once steel production and market datasets are created, premise re-links steel-consuming activities to the new regional markets for steel. The regional market it re-links to depends on the location of the consumer.

Transport

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("two_wheelers")
ndb.update("cars")
ndb.update("trucks")
ndb.update("buses")
ndb.update("trains")

premise imports inventories for transport activity operated by:

• two-wheelers

• passenger cars

• medium and heavy duty trucks

• buses

• trains

Key outputs

• Adds mode- and powertrain-specific transport inventories.

• Scales vehicle energy use and efficiencies based on IAM projections.

• Builds regional fleet-average transport datasets where IAM shares are available.

Inventories are available for current vehicles. Future vehicle inventories are obtained by scaling down the current inventories based on the vehicle efficiency improvements projected by the IAM scenario.

Trucks

The following size classes of medium and heavy duty trucks are imported:

• 3.5t

• 7.5t

• 18t

• 26t

• 40t

These weights refer to the vehicle gross mass (the maximum weight the vehicle is allowed to reach, fully loaded).

Each truck is available for a variety of powertrain types:

• fuel cell electric

• battery electric

• diesel hybrid

• plugin diesel hybrid

• diesel

• compressed gas

but also for different driving cycles, to which a range autonomy of the vehicle is associated:

• urban delivery (required range autonomy of 150 km)

• regional delivery (required range autonomy of 400 km)

• long haul (required range autonomy of 800 km)

Those are driving cycles developed for the software VECTO, which have become standard in measuring the CO2 emissions of trucks.

The truck vehicle model is from Sacchi et al, 2021.

Key outputs

• Adds size- and powertrain-specific truck inventories by driving cycle.

• Builds IAM-region fleet-average truck datasets by size class and haul type.

• Relinks freight transport activities to the fleet-average markets.

Note

Not all powertrain types are available for regional and long haul driving cycles. This is specifically the case for battery electric trucks, for which the mass and size prevent them from completing the cycle, or surpasses the vehicle gross weight.

Fleet average trucks

REMIND, IMAGE and TIAM-UCL provide fleet composition data, per scenario, region and year.

The fleet data is expressed in “ton-kilometers” performed by each type of vehicle for passenger transport, in a given region and year.

premise uses the fleet data to produce fleet average trucks for each IAM region, and more specifically:

• a fleet average truck, all powertrains and size classes considered

• a fleet average truck, all powertrains considered, for a given size class

They appear in the LCI database as the following:

truck transport dataset name	description
transport, freight, lorry, 3.5t gross weight, unspecified powertrain, long haul	fleet average, for 3.5t size class, long haul
transport, freight, lorry, 7.5t gross weight, unspecified powertrain, long haul	fleet average, for 7.5t size class, long haul
transport, freight, lorry, 18t gross weight, unspecified powertrain, long haul	fleet average, for 18t size class, long haul
transport, freight, lorry, 26t gross weight, unspecified powertrain, long haul	fleet average, for 26t size class, long haul
transport, freight, lorry, 40t gross weight, unspecified powertrain, long haul	fleet average, for 26t size class, long haul
transport, freight, lorry, unspecified, long haul	fleet average, all powertrain types, all size classes

The mapping file linking IAM variables to the truck datasets is available here: https://github.com/polca/premise/blob/master/premise/iam_variables_mapping/transport_road_freight.yaml

Relinking

Regarding trucks, premise re-links truck transport-consuming activities to the newly created fleet average truck datasets.

The following table shows the correspondence between the original truck transport datasets and the new ones replacing them:

Direct Air Capture

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("cdr")

Key outputs

• Creates region-specific DAC datasets from literature inventories.

• Scales electricity and heat inputs based on IAM efficiency signals.

premise creates different region-specific Direct Air Capture (DAC) datasets, based on the inventories from Qiu et al., 2022.

If provided by the IAM scenario, premise scales the inputs of electricity and heat of the DAC datasets to reflect changes in efficiency.

Fuels

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("fuels")

Key outputs

• Builds regional fuel supply chains and secondary energy markets.

• Adjusts biofuel conversion efficiencies and land-use-related burdens where IAM provides data.

• Harmonizes market compositions to fuel LHV and relinks consuming activities.

premise create different region-specific fuel supply chains and fuel markets, based on data from the IAM scenario.

Efficiency adjustment

Biofuels

The biomass-to-fuel efficiency ratio of bioethanol and biodiesel production datasets is adjusted according to the IAM scenario projections.

Inputs to the biofuel production datasets are multiplied by a scaling factor that represents the change in efficiency relative to today (2020).

Feedstock regionalization

For bioethanol and oil-based biodiesel, premise selects region-appropriate feedstocks before building fuel markets (e.g., “market for petrol” and “market for diesel”). This prevents regions from drawing on feedstocks that are not representative of their dominant crop choices (for example, sugarbeet ethanol in Latin America).

The selection is driven by explicit IAM-region mappings:

• oil-based biodiesel feedstocks: premise/iam_variables_mapping/iam_region_to_biodiesel_feedstock.yaml

• bioethanol feedstocks (sugar, grass, wood, grain): premise/iam_variables_mapping/iam_region_to_bioethanol_feedstock.yaml

When a region has no suppliers in its allowed locations for a given feedstock, premise falls back to any available supplier of that same feedstock to avoid empty markets.

Land use and land use change

When building a database using IMAGE, land use and land use change emissions are available. Upon the import of crops farming datasets, premise adjusts the land occupation as well as CO2 emissions associated to land use and land use change, respectively.

Output	_	_	_
producer	amount	unit	location
Farming and supply of corn	1	kilogram	CEU
Input
supplier	amount	unit	location
market for diesel, burned in agricultural machinery	0.142	megajoule	GLO
petrol, unleaded, burned in machinery	0.042	megajoule	GLO
market for natural gas, burned in gas motor, for storage	0.091	megajoule	GLO
market group for electricity, low voltage	0.004	kilowatt hour	CEU
Energy, gross calorific value, in biomass	15.910	megajoule	_
Occupation, annual crop	1.584	square meter-year	_
Carbon dioxide, in air	1.476	kilogram	_
Carbon dioxide, from soil or biomass stock	1.140	kilogram	_

The land use value is given from the IAM scenario in Ha/GJ of primary crop energy. Hence, the land occupation per kg of crop farmed is calculated as:

land_use = land_use [Ha/GJ] * 10000 [m2/Ha] / 1000 [MJ/GJ] * LHV [MJ/kg]

Regarding land use change CO2 emissions, the principle is similar. The variable is expressed in kg CO2/GJ of primary crop energy. Hence, the land use change CO2 emissions per kg of crop farmed are calculated as:

land_use_co2 = land_use_co2 [kg CO2/GJ] / 1000 [MJ/GJ] * LHV [MJ/kg]

Regional supply chains

premise builds several supply chains for synthetic fuels, for each IAM region. THe reason for this is that synthetic fuels can be produced from a variety of hydrogen and CO2 sources. Additionally, hydrogen can be supplied by different means of transport, and in different states.

Hydrogen

Several pathways for hydrogen production are modeled in premise (see Hydrogen section under EXTRACT>Import of additional inventories).

The efficiency of hydrogen production pathways is adjusted according to the IAM scenario projections, if available. A scaling factor is calculated for each pathway, which is the ratio between the IAM variable value for the year in question and the current efficiency value (i.e., in 2020). premise uses this scaling factor to adjust the amount of feedstock input to produce 1 kg of hydrogen (e.g., m3 of natural gas per kg hydrogen).

If not available, external projection data is used to adjust future efficiencies (see Hydrogen section under EXTRACT>Import of additional inventories).

Key outputs

• Scales hydrogen production inventories to IAM or external efficiency trends.

• Builds supply chains that vary by transport mode, distance, and state.

• Applies transport and storage losses to supply routes.

Hydrogen supply chains

premise starts by building different supply chains for hydrogen by varying:

• the transport mode: truck, hydrogen pipeline, re-assigned CNG pipeline, ship,

• the distance: 500 km, 2000 km

• the state of the hydrogen: gaseous, liquid, liquid organic compound,

• the hydrogen production route: electrolysis, SMR, biomass gasifier (coal, woody biomass)

Hence, for each IAM region, the following supply chains for hydrogen are built:

• hydrogen supply, from electrolysis, by ship, as liquid, over 2000 km

• hydrogen supply, from gasification of biomass by heatpipe reformer, by H2 pipeline, as gaseous, over 500 km

• hydrogen supply, from ATR of from natural gas, by truck, as gaseous, over 500 km

• hydrogen supply, from gasification of biomass by heatpipe reformer, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from SMR of from natural gas, with CCS, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from SMR of from natural gas, with CCS, by ship, as liquid, over 2000 km

• hydrogen supply, from coal gasification, by CNG pipeline, as gaseous, over 500 km

• hydrogen supply, from SMR of from natural gas, by ship, as liquid, over 2000 km

• hydrogen supply, from coal gasification, by truck, as liquid, over 500 km

• hydrogen supply, from gasification of biomass by heatpipe reformer, by truck, as liquid, over 500 km

• hydrogen supply, from ATR of from natural gas, with CCS, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from SMR of from natural gas, with CCS, by truck, as liquid, over 500 km

• hydrogen supply, from electrolysis, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from gasification of biomass, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from SMR of from natural gas, with CCS, by truck, as gaseous, over 500 km

• hydrogen supply, from SMR of biogas, with CCS, by CNG pipeline, as gaseous, over 500 km

• hydrogen supply, from SMR of from natural gas, by truck, as gaseous, over 500 km

• hydrogen supply, from SMR of from natural gas, by H2 pipeline, as gaseous, over 500 km

• hydrogen supply, from gasification of biomass, with CCS, by truck, as liquid organic compound, over 500 km

• hydrogen supply, from gasification of biomass, by ship, as liquid, over 2000 km

Each supply route is associated with specific losses. Losses for the transport of H2 by truck and hydrogen pipelines, and losses at the regional storage storage (salt cavern) are from Wulf et al, 2018. Boil-off loss values during shipping are from Hank et al, 2020. Losses when transporting H2 via re-assigned CNG pipelines are from Cerniauskas et al, 2020. Losses along the pipeline are from Schori et al, 2012., but to be considered conservative, as those are initially for natural gas (and hydrogen has a higher potential for leaking).

_	_	truck	ship	H2 pipeline	CNG pipeline	reference flow
gaseous	compression	0.5%		0.5%	0.5%	per kg H2
_	storage buffer			2.3%	2.3%	per kg H2
_	storage leak			1.0%	1.0%	per kg H2
_	pipeline leak			0.004%	0.004%	per kg H2, per km
_	purification				7.0%	per kg H2
liquid	liquefaction	1.3%	1.3%			per kg H2
_	vaporization	2.0%	2.0%			per kg H2
_	boil-off	0.2%	0.2%			per kg H2, per day
liquid organic compound	hydrogenation	0.5%				per kg H2

Losses are cumulative along the supply chain and range anywhere between 5 and 20%. The table below shows the example of 1 kg of hydrogen transport via re-assigned CNG pipelines, as a gas, over 500 km. A total of 0.13 kg of hydrogen is lost along the supply chain (13% loss):

Output	_	_	_
producer	amount	unit	location
hydrogen supply, from electrolysis, by CNG pipeline, as gaseous, over 500 km	1	kilogram	OCE
Input
supplier	amount	unit	location
hydrogen production, gaseous, 25 bar, from electrolysis	1.133	kilogram	OCE
market group for electricity, low voltage	3.091	kilowatt hour	OCE
market group for electricity, low voltage	0.516	kilowatt hour	OCE
hydrogen embrittlement inhibition	1	kilogram	OCE
geological hydrogen storage	1	kilogram	OCE
Hydrogen refuelling station	1.14E-07	unit	OCE
distribution pipeline for hydrogen, reassigned CNG pipeline	1.56E-08	kilometer	RER
transmission pipeline for hydrogen, reassigned CNG pipeline	1.56E-08	kilometer	RER

• 7% during the purification of hydrogen: when using CNG pipelines, the hydrogen has to be mixed with another gas to prevent the embrittlement of the pipelines. The separation process at the other end leads to significant losses

• 2% lost along the 500 km of pipeline

• 3% at the regional storage (salt cavern)

Also, in this same case, electricity is used:

• 1.9 kWh to compress the H2 from 25 bar to 100 bar to inject it into the pipeline

• 1.2 kWh to recompress the H2 along the pipeline every 250 km

• 0.34 kWh for injecting and pumping H2 into a salt cavern

• 2.46 kWh to blend the H2 with oxygen on one end, and purify on the other

• 0.5 kWh to pre-cool the H2 at the fuelling station (necessary if used in fuel cells, for example)

Fuel markets

premise builds markets for the following fuels:

• market for petrol, unleaded

• market for petrol, low-sulfur

• market for diesel, low-sulfur

• market for diesel

• market for natural gas, high pressure

• market for hydrogen, gaseous

• market for kerosene

• market for liquefied petroleum gas

The market shares are based on the IAM scenario data regarding the composition of liquid and gaseous secondary energy carriers. The ampping between the IAM scenario data and the fuel markets is described under: https://github.com/polca/premise/tree/master/premise/iam_variables_mapping/fuels.yaml

Warning

Some fuel types are not properly represented in the LCI database. Available inventories for biomass-based methanol production do not differentiate between wood and grass as the feedstock.

Note

Modelling choice: premise builds several potential supply chains for hydrogen. Because the logistics to supply hydrogen in the future is not known or indicated by the IAM, the choice is made to supply it by truck over 500 km, in a gaseous state.

Influence of differing LHV on fuel market composition

Because not all competing fuels of a same type have similar calorific values, some adjustments are made. The table below shows the example of the market for gasoline, for the IMAGE region of Western Europe in 2050. The sum of fuel inputs is superior to 1 (i.e., 1.4 kg). This is because the market dataset as “1 kg” as reference unit, and methanol and bioethanol have low calorific values comparatively to petrol (i.e., 19.9 and 26.5 MJ/kg respectively, vs. 42.6 MJ/kg for gasoline). Hence, their inputs are scaled up to reach an average calorific value of 42.6 MJ/kg of fuel supplied by the market.

This is necessary as gasoline-consuming activities in the lCI database are modelled with the calorific value of conventional gasoline.

Output	_	_	_
producer	amount	unit	location
market for petrol, low-sulfur	1	kilogram	WEU
Input
supplier	amount	unit	location
petrol production, low-sulfur	0.550	kilogram	CH
market for methanol, from biomass	0.169	kilogram	CH
market for methanol, from biomass	0.148	kilogram	CH
market for methanol, from biomass	0.122	kilogram	CH
market for methanol, from biomass	0.122	kilogram	CH
Ethanol production, via fermentation, from switchgrass	0.060	kilogram	WEU
Ethanol production, via fermentation, from switchgrass, with CCS	0.053	kilogram	WEU
Ethanol production, via fermentation, from sugarbeet	0.051	kilogram	WEU
Ethanol production, via fermentation, from sugarbeet, with CCS	0.051	kilogram	WEU
Ethanol production, via fermentation, from poplar, with CCS	0.041	kilogram	WEU
Ethanol production, via fermentation, from poplar	0.041	kilogram	WEU

Heat

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("heat")

Key outputs

• Regionalizes heat and steam production datasets by IAM region.

• Relinks heat producers to regional fuel and biomass markets where available.

• Splits CO2 emissions into fossil and non-fossil shares based on fuel mixes.

Datasets that supply heat and steam via the combustion of natural gas and diesel are regionalized (made available for each region of the IAM model) and relinked to regional fuel markets. If the fuel market contains a share of non-fossil fuels, the CO2 emissions of the heat and steam production are split between fossil and non-fossil emissions. Once regionalized, the heat and steam production datasets relink to activities that require heat within the same region.

Here is a list of the heat and steam production datasets that are regionalized:

• diesel, burned in …

• steam production, as energy carrier, in chemical industry

• heat production, natural gas, …

• heat and power co-generation, natural gas, …

• heat production, light fuel oil, …

• heat production, softwood chips from forest, …

• heat production, hardwood chips from forest, …

These datasets are relinked to the corresponding regionalized fuel market only if .update(“fuels”) has been run. Also, heat production datasets that use biomass as fuel input (e.g., softwood and hardwood chips) relink to the dataset market for biomass, used as fuel if update(“biomass”) has been run previously.

CO2 emissions update

premise iterates through activities that consume any of the newly created fuel markets to update the way CO2 emissions are modelled. Based on the fuel market composition, CO2 emissions within the fuel-consuming activity are split between fossil and non-fossil emissions.

The table below shows the example where the CO2 emissions of a 3.5t truck have been split into biogenic and fossil fractions after re-link to the new diesel market of the REMIND region for India.

Output	before	after	_	_
producer	amount	amount	unit	location
transport, freight, lorry, diesel, 3.5t	1	1	ton-kilometer	IND
Input
supplier	amount	amount	unit	location
treatment of tyre wear emissions, lorry	-0.0009	-0.0009	kilogram	RER
market for road maintenance	0.0049	0.0049	meter-year	RER
market for road	0.0041	0.0041	meter-year	GLO
treatment of road wear emissions, lorry	-0.0008	-0.0008	kilogram	RER
market for refrigerant R134a	2.84E-05	2.84E-05	kilogram	GLO
treatment of brake wear emissions, lorry	-0.0005	-0.0005	kilogram	RER
Light duty truck, diesel, 3.5t	1.39E-05	1.39E-05	unit	RER
market for diesel, low-sulfur	0.1854	0.1854	kilogram	IND
Carbon dioxide, fossil	0.5840	0.5667	kilogram	_
Carbon dioxide, non-fossil	0.0000	0.0173	kilogram	_
Nitrogen oxides	0.0008	0.0008	kilogram	_
Nitrogen oxides	0.0003	0.0003	kilogram	_

Geographical mapping

IAM models have slightly different geographical resolutions and definitions.

Map of IMAGE regions

Map of REMIND regions

Map of REMIND-EU regions

Map of TIAM-UCL regions

Map of GCAM regions

Map of MESSAGE regions

premise uses the following correspondence between ecoinvent locations and IAM regions. This mapping is performed by the constructive_geometries implementation in the wurst library.

Country Code	message	gcam	tiam-ucl	remind	remind-eu	image
AE	MEA	Middle East	MEA	MEA	MEA	ME
AF	SAS	South Asia	ODA	OAS	OAS	RSAS
AG	LAM	Central America and Caribbean	CSA	LAM	LAM	N/A
AI	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
AL	EEU	Europe_Non_EU	WEU	NEU	NES	CEU
AM	FSU	Central Asia	FSU	REF	REF	RUS
AO	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
AQ	MEA	Middle East	MEA	MEA	MEA	ME
AR	LAM	Argentina	CSA	LAM	LAM	RSAM
AS	PAS	Southeast Asia	ODA	OAS	OAS	OCE
AT	WEU	EU-15	WEU	EUR	EWN	WEU
AU	PAO	Australia_NZ	AUS	CAZ	CAZ	OCE
AZ	FSU	Central Asia	FSU	REF	REF	RUS
BA	EEU	Europe_Non_EU	EEU	NEU	NES	CEU
BD	SAS	South Asia	ODA	OAS	OAS	RSAS
BE	WEU	EU-15	WEU	EUR	EWN	WEU
BF	AFR	Africa_Western	AFR	SSA	SSA	WAF
BG	EEU	EU-12	EEU	EUR	ECS	CEU
BH	MEA	Middle East	MEA	MEA	MEA	ME
BI	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
BJ	AFR	Africa_Western	AFR	SSA	SSA	WAF
BN	PAS	Southeast Asia	MEA	OAS	OAS	SEAS
BO	LAM	South America_Southern	CSA	LAM	LAM	RSAM
BR	LAM	Brazil	CSA	LAM	LAM	BRA
BS	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
BT	SAS	South Asia	ODA	OAS	OAS	RSAS
BW	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
BY	FSU	Europe_Eastern	FSU	REF	REF	UKR
BZ	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
CA	NAM	Canada	CAN	CAZ	CAZ	CAN
CD	AFR	Africa_Western	AFR	SSA	SSA	WAF
CF	AFR	Africa_Western	AFR	SSA	SSA	WAF
CG	AFR	Africa_Western	AFR	SSA	SSA	WAF
CH	WEU	European Free Trade Association	WEU	NEU	NEN	WEU
CI	AFR	Africa_Western	AFR	SSA	SSA	WAF
CL	LAM	South America_Southern	CSA	LAM	LAM	RSAM
CM	AFR	Africa_Western	AFR	SSA	SSA	WAF
CN	CHN	China	CHI	CHA	CHA	CHN
CO	LAM	Colombia	CSA	LAM	LAM	RSAM
CR	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
CU	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
CY	WEU	EU-12	MEA	EUR	ESC	N/A
CZ	EEU	EU-12	EEU	EUR	ECE	CEU
DE	WEU	EU-15	WEU	EUR	DEU	WEU
DJ	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
DK	WEU	EU-15	WEU	EUR	ENC	WEU
DM	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
DO	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
DZ	MEA	Africa_Northern	AFR	MEA	MEA	NAF
EC	LAM	South America_Southern	CSA	LAM	LAM	RSAM
EE	EEU	EU-12	FSU	EUR	ECE	CEU
EG	MEA	Africa_Northern	AFR	MEA	MEA	NAF
EH	AFR	Africa_Northern	AFR	SSA	SSA	NAF
ER	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
ES	WEU	EU-15	WEU	EUR	ESW	WEU
ET	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
FI	WEU	EU-15	WEU	EUR	ENC	WEU
FJ	PAS	Southeast Asia	ODA	OAS	OAS	OCE
FK	LAM	South America_Southern	CSA	LAM	LAM	RSAM
FR	WEU	EU-15	WEU	EUR	FRA	WEU
GA	AFR	Africa_Western	AFR	SSA	SSA	WAF
GB	WEU	EU-15	UK	EUR	UKI	WEU
GE	FSU	Central Asia	FSU	REF	REF	RUS
GF	LAM	South America_Northern	CSA	LAM	LAM	RSAM
GH	AFR	Africa_Western	AFR	SSA	SSA	WAF
GI	WEU	EU-15	WEU	EUR	UKI	WEU
GL	WEU	EU-15	NEU	NEU	NEN	WEU
GM	AFR	Africa_Western	AFR	SSA	SSA	WAF
GN	AFR	Africa_Western	AFR	SSA	SSA	WAF
GQ	AFR	Africa_Western	AFR	SSA	SSA	WAF
GR	WEU	EU-15	WEU	EUR	ESC	WEU
GT	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
GW	AFR	Africa_Western	AFR	SSA	SSA	WAF
GY	LAM	South America_Northern	CSA	LAM	LAM	RSAM
HK	CHN	China	CHI	CHA	CHA	CHN
HN	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
HR	EEU	Europe_Non_EU	EEU	EUR	ECS	CEU
HT	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
HU	EEU	EU-12	EEU	EUR	ECS	CEU
ID	PAS	Indonesia	ODA	OAS	OAS	INDO
IE	WEU	EU-15	WEU	EUR	UKI	WEU
IL	MEA	Middle East	MEA	MEA	MEA	ME
IN	SAS	India	IND	IND	IND	INDIA
IQ	MEA	Middle East	MEA	MEA	MEA	ME
IR	MEA	Middle East	MEA	MEA	MEA	ME
IS	WEU	European Free Trade Association	WEU	NEU	NEN	WEU
IT	WEU	EU-15	WEU	EUR	ESC	WEU
JM	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
JO	MEA	Middle East	MEA	MEA	MEA	ME
JP	PAO	Japan	JPN	JPN	JPN	JAP
KE	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
KG	FSU	Central Asia	FSU	REF	REF	STAN
KH	RCPA	Southeast Asia	ODA	OAS	OAS	SEAS
KI	PAS	Southeast Asia	ODA	OAS	OAS	OCE
KM	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
KN	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
KP	RCPA	Southeast Asia	ODA	OAS	OAS	KOR
KR	PAS	South Korea	SKO	OAS	OAS	KOR
KW	MEA	Middle East	MEA	MEA	MEA	ME
KY	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
KZ	FSU	Central Asia	FSU	REF	REF	STAN
LA	RCPA	Southeast Asia	ODA	OAS	OAS	SEAS
LB	MEA	Middle East	MEA	MEA	MEA	ME
LC	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
LI	WEU	EU-15	WEU	NEU	NEN	WEU
LK	SAS	South Asia	ODA	OAS	OAS	RSAS
LR	AFR	Africa_Western	AFR	SSA	SSA	WAF
LS	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
LT	EEU	EU-12	FSU	EUR	ECE	CEU
LU	WEU	EU-15	WEU	EUR	EWN	WEU
LV	EEU	EU-12	FSU	EUR	ECE	CEU
LY	MEA	Africa_Northern	AFR	MEA	MEA	NAF
MA	MEA	Africa_Northern	AFR	MEA	MEA	NAF
MC	WEU	EU-15	WEU	NEU	NES	WEU
MD	FSU	Europe_Eastern	FSU	REF	REF	UKR
ME	EEU	Europe_Non_EU	EEU	NEU	NES	CEU
MG	AFR	Africa_Eastern	AFR	SSA	SSA	RSAF
MK	EEU	Europe_Non_EU	EEU	NEU	NES	CEU
ML	AFR	Africa_Western	AFR	SSA	SSA	WAF
MM	PAS	Southeast Asia	ODA	OAS	OAS	SEAS
MN	RCPA	Central Asia	ODA	OAS	OAS	CHN
MO	CHN	China	CHI	CHA	CHA	CHN
MR	AFR	Africa_Western	AFR	SSA	SSA	WAF
MS	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
MT	WEU	EU-12	WEU	EUR	ESC	WEU
MU	AFR	Africa_Eastern	ODA	SSA	SSA	EAF
MW	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
MX	LAM	Mexico	MEX	MEX	LAM	MEX
MY	PAS	Southeast Asia	ODA	OAS	OAS	SEAS
MZ	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
NA	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
NC	PAS	Southeast Asia	ODA	OAS	OAS	OCE
NE	AFR	Africa_Western	AFR	SSA	SSA	WAF
NG	AFR	Africa_Western	AFR	SSA	SSA	WAF
NI	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
NL	WEU	EU-15	WEU	EUR	EWN	WEU
NO	WEU	European Free Trade Association	WEU	NEU	NEN	WEU
NP	SAS	South Asia	ODA	OAS	OAS	RSAS
NR	PAS	Southeast Asia	ODA	OAS	OAS	OCE
NU	PAS	Southeast Asia	ODA	OAS	OAS	OCE
NZ	PAO	Australia_NZ	AUS	CAZ	CAZ	OCE
OM	MEA	Middle East	MEA	MEA	MEA	ME
PA	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
PE	LAM	South America_Southern	CSA	LAM	LAM	RSAM
PF	PAS	Southeast Asia	ODA	OAS	OAS	OCE
PG	PAS	Southeast Asia	ODA	OAS	OAS	INDO
PH	PAS	Southeast Asia	ODA	OAS	OAS	SEAS
PK	SAS	Pakistan	ODA	OAS	OAS	RSAS
PL	EEU	EU-12	EEU	EUR	ECE	CEU
PR	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
PS	MEA	Middle East	MEA	MEA	MEA	ME
PT	WEU	EU-15	WEU	EUR	ESW	WEU
PY	LAM	South America_Southern	CSA	LAM	LAM	RSAM
QA	MEA	Middle East	MEA	MEA	MEA	ME
RE	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
RO	EEU	EU-12	EEU	EUR	ECS	CEU
RS	EEU	Europe_Non_EU	EEU	NEU	NES	CEU
RU	FSU	Europe_Eastern	FSU	REF	REF	RUS
RW	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
SA	MEA	Middle East	MEA	MEA	MEA	ME
SB	PAS	Southeast Asia	ODA	OAS	OAS	OCE
SC	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
SD	MEA	Africa_Eastern	AFR	MEA	MEA	EAF
SE	WEU	EU-15	WEU	EUR	ENC	WEU
SG	PAS	Southeast Asia	ODA	OAS	OAS	SEAS
SH	AFR	Africa_Western	AFR	SSA	SSA	WAF
SI	EEU	EU-12	EEU	EUR	ECS	CEU
SK	EEU	EU-12	EEU	EUR	ECE	CEU
SL	AFR	Africa_Western	AFR	SSA	SSA	WAF
SM	WEU	EU-15	WEU	NEU	NES	WEU
SN	AFR	Africa_Western	AFR	SSA	SSA	WAF
SO	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
SR	LAM	South America_Northern	CSA	LAM	LAM	RSAM
SS	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
ST	AFR	Africa_Western	AFR	SSA	SSA	WAF
SV	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
SY	MEA	Middle East	MEA	MEA	MEA	ME
SZ	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
TC	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
TD	AFR	Africa_Western	AFR	SSA	SSA	WAF
TF	MEA	Africa_Northern	AFR	MEA	MEA	NAF
TG	AFR	Africa_Western	AFR	SSA	SSA	WAF
TH	PAS	Southeast Asia	ODA	OAS	OAS	SEAS
TJ	FSU	Central Asia	FSU	REF	REF	STAN
TL	PAS	Southeast Asia	ODA	OAS	OAS	INDO
TM	FSU	Central Asia	FSU	REF	REF	STAN
TN	MEA	Africa_Northern	AFR	MEA	MEA	NAF
TO	PAS	Southeast Asia	ODA	OAS	OAS	OCE
TR	WEU	EU-15	MEA	MEA	NES	TUR
TT	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
TV	PAS	Southeast Asia	ODA	OAS	OAS	OCE
TW	PAS	Southeast Asia	ODA	OAS	OAS	OCE
TZ	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
UA	FSU	Europe_Eastern	FSU	REF	REF	UKR
UG	AFR	Africa_Eastern	AFR	SSA	SSA	EAF
US	NAM	USA	USA	USA	USA	USA
UY	LAM	South America_Southern	CSA	LAM	LAM	RSAM
UZ	FSU	Central Asia	FSU	REF	REF	STAN
VC	LAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
VE	LAM	South America_Northern	CSA	LAM	LAM	RSAM
VG	N/A	N/A	—	LAM	LAM	RCAM
VI	NAM	Central America and Caribbean	CSA	LAM	LAM	RCAM
VN	RCPA	Southeast Asia	ODA	OAS	OAS	SEAS
VU	PAS	Southeast Asia	ODA	OAS	OAS	OCE
XK	EU	Europe_Non_EU	EEU	NEU	NES	CEU
YE	MEA	Middle East	MEA	MEA	MEA	ME
ZA	AFR	South Africa	AFR	SSA	SSA	SAF
ZM	AFR	Africa_Southern	AFR	SSA	SSA	RSAF
ZW	AFR	Africa_Southern	AFR	SSA	SSA	RSAF

The mapping between ecoinvent locations and IAM regions is available under the following directory: https://github.com/polca/premise/blob/master/premise/iam_variables_mapping/topologies

Regionalization

Several of the integration steps described above involve the regionalization of datasets. It is the case, for example, when introducing datasets representing a process for each of the IAM regions. In such case, the datasets are regionalized by selecting the most representative suppliers of inputs for each region. If a dataset in a specific IAM region requires tap water, for example, the regionalization process will select the most representative water suppliers in that region.

If more than one supplier is available, the regionalization process will allocated a supply share to each candidate supplier based on their respective production volume. If no adequate supplier is found for a given region, the regionalization process will select all the existing suppliers and allocate a supply share to each supplier based on their respective production volume.

Here is the decision tree followed:

Decision Tree for Processing Datasets

The process begins with a dataset that requires processing.

• Yes

  • Use process_cached_exchange().

    • Retrieve cached data.

    • Update new_exchanges with cached data.

• No

  • Use process_uncached_exchange().

    • None

      • Print a warning and return.

    • One

      • Use handle_single_possible_dataset().

        • Use the single matched dataset.

        • Update new_exchanges with this dataset information.

    • Multiple

      • Use handle_multiple_possible_datasets().

        • Yes

          • Use the matched dataset location.

        • No

          • Use process_complex_matching_and_allocation().

            • IAM Region

              • Use handle_iam_region().

                • Match IAM region to ecoinvent locations.

                • Update new_exchanges with IAM region-specific data.

                • Cache the new entry.

            • Global (‘GLO’, ‘RoW’, ‘World’)

              • Use handle_global_and_row_scenarios().

                • Allocate inputs for global datasets.

                • Update new_exchanges with global data.

                • Cache the new entry.

            • Others

              • Perform GIS matching.

                • Determine intersecting locations with GIS.

                • Allocate inputs based on GIS matches.

                • Update new_exchanges with GIS-specific data.

                • Cache the new entry.

• If no match is found, use handle_default_option().

  • Integrate new exchanges into the dataset.

GAINS emission factors

Emissions factors from the air pollution model GAINS are used to scale non-CO2 emissions in various datasets. The emission factors are available under:

premise/data/GAINS_emission_factors

Run

from premise import *
import brightway2 as bw

bw.projects.set_current("my_project")

ndb = NewDatabase(
    scenarios=[
            {"model":"remind", "pathway":"SSP2-Base", "year":2028}
        ],
    source_db="ecoinvent 3.7 cutoff",
    source_version="3.7.1",
    key='xxxxxxxxxxxxxxxxxxxxxxxxx'
)
ndb.update("emissions")

When using update(“emissions”), emission factors from the GAINS-EU and GAINS-IAM models are used to scale non-CO2 emissions in various datasets.

The emission factors are available under https://github.com/polca/premise/tree/master/premise/data/GAINS_emission_factors

Emission factors from GAINS-EU are applied to activities in European countries. Emission factors from GAINS-IAM are applied to activities in non-European countries, or to European activities if an emission facor from GAINS-EU has not been applied first.

Emission factors are specific to:

• an activity type,

• a year,

• a country (for GAINS-EU, otherwise a region),

• a fuel type,

• a technology type,

• and a scenario.

The mapping between GAINS and ecoinvent activities is available under the following file: https://github.com/polca/premise/blob/master/premise/data/GAINS_emission_factors/gains_ecoinvent_sectoral_mapping.yaml

The table below shows the mapping between ecoinvent and GAINS emission flows.

ecoinvent species	GAINS species
Sulfur dioxide	SO2
Sulfur oxides	SO2
Carbon monoxide, fossil	CO
Carbon monoxide, non-fossil	CO
Carbon monoxide, from soil or biomass stock	CO
Nitrogen oxides	NOx
Ammonia	NH3
NMVOC, non-methane volatile organic compounds, unspecified origin	VOC
VOC, volatile organic compounds, unspecified origin	VOC
Methane	CH4
Methane, fossil	CH4
Methane, non-fossil	CH4
Methane, from soil or biomass stock	CH4
Dinitrogen monoxide	N2O
Particulates, > 10 um	PM10
Particulates, > 2.5 um, and < 10um	PM25
Particulates, < 2.5 um	PM1

We consider emission factors in ecoinvent as representative of the current situation. Hence, we calculate a scaling factor from the GAINS emission factors for the year of the scenario relative to the year 2020. note that premise prevents scaling factors to be inferior to 1 if the year is inferior to 2020. Inversely, scaling factors cannot be superior to 1 if the year is superior to 2020.

Two GAINS-IAM scenarios are available:

• CLE: **C**urrent **LE**gislation scenario

• MFR: **M**aximum **F**easible **R**eduction scenario

By default, the CLE scenario is used. To use the MFR scenario:

ndb = NewDatabase(
    ...
    gains_scenario="MFR",
)

Finally, unlike GAINS-EU, GAINS-IAM uses IAM-like regions, not countries. The mapping between IAM regions and GAINS-IAM regions is available under the following file:

https://github.com/polca/premise/blob/master/premise/iam_variables_mapping/gains_regions_mapping.yaml

For questions related to GAINS modelling, please contact the respective GAINS team:

• GAINS-EU: https://gains.iiasa.ac.at/gains/EUN/index.login

• GAINS-IAM: https://gains.iiasa.ac.at/gains/IAM/index.login

Logs

premise generates a spreadsheet report detailing changes made to the database for each scenario. The report is saved in the current working directory and is automatically generated after database export.

The report lists the datasets added, updated and emptied. It also gives a number of indicators relating to efficiency, emissions, etc. for each scenario.

Finally, it also contains a “Validation” tab that lists datasets which potentially present erroneous values. These datasets are to be checked by the user.

This report can also be generated manually using the generate_change_report() method.