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.

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")

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)

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.

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")

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")

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 panels are expected to improve over time. The following module efficiencies (mena, minimum, maximum) are considered for the different types of PV panels, applied as a triangular distribution on the panel surface required to reach the peak power output of the dataset:

module efficiency	micro-Si	single-Si	multi-Si	CIGS	CIS	CdTe	GaAs	perovskite	Source
2010	10.0 (7.5-12.5)	15.0 (11.3-18.9)	14.0 (10.5-17.5)	11.0 (8.3-13.8)	11.0 (8.3-13.8)	10.0 (8.8-12.0)	28.0 (21.0-35.0)	25.0 (19.0-31.0)	[1], [2], [3], [4], [5], [6], [7], [8]
2020	11.9 (9.0-15.0)	17.9 (13.0-22.0)	16.8 (12.0-21.0)	14.0 (10.5-18.0)	14.0 (10.5-18.0)	16.8 (13.0-21.0)	28.0 (21.0-35.0)	25.0 (19.0-31.0)	[1], [2], [3], [4], [5], [6], [7], [8]
2023		22.0 (17.0-24.0)		15.0 (11.3-19.0)		19.0 (15.0-20.0)			[2], [4], [6]
2050	13.0 (9.0-16.0)	27.0 (20.0-34.0)	24.0 (16.0-30.0)	23.0 (17.3-29.0)	23.0 (17.3-29.0)	22.6 (22.0-25.0)	28.0 (25.0-28.0)	25.2 (22.0-31.3)	[1], [2], [3], [4], [5], [6], [7], [8]

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

And the efficiency values are stored in the file premise/data/renewables/efficiency_solar_PV.csv.

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).

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 * (1 / scaling_factor)

For scenario years beyond 2050, 2050 efficiency values are used.

The table below provides such an example where a 450 kWp flat-roof installation sees its current (2020) module efficiency improving from 20% to 26% by 2050. THe are of PV panel (and mounting system) has been multiplied by 1 / (0.26/0.20), 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

1731

m2

photovoltaic panel, single-SI

2500

1881

m2

treatment, single-SI PV module

30000

30000

kg

electricity, low voltage

25

25

kWh

module efficiency

20%

26%

%

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")

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 teh 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")"

The modelling of future improvements in the steel sector is relatively simple at the moment, and does not involve the emergence of new technologies (e.g., hydrogen-based DRI, electro-winning).

Dataset proxies

premise duplicates steel production datasets in ecoinvent for the production of primary and secondary steel (called respectively “steel production, converter” and “steel production, electric”) 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

Regarding primary steel production (using BO-BOF), premise adjusts the inputs of fuels found in:

the pig iron production datasets,
the steel production datasets,

assuming an integrated steel mill unit, by multiplying these fuel inputs by a scaling factor provided by the IAM scenario.

Typical fuel inputs for these process are natural gas, coal, coal-based coke. Emissions of (fossil) CO2 are scaled accordingly.

Regarding the production of secondary steel (using EAF), premise adjusts the input of electricity based on the scaling factor provided by the IAM 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. The datatset used to that effect is from Meunier_ et al., 2020. The dataset described the capture of CO2 from a cement plant, not a steel mill, but it is assumed to be an acceptable approximation since the CO2 concentration in the flue gases should not be significantly different.

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: 3.66 MJ/kg CO2 captured in 2020, and 2.6 MJ/kg in 2050. The first number is from Meunier_ et al., 2020, 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 15% by the recovery of excess heat, as mentioned in the 2018 IEA cement roadmap report, which is assumed to be also valid in the case of a steel mill.

Steel markets

premise create a dataset “market for steel, low-alloyed” 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, converter, low-alloyed

0.66

kilogram

IND

steel production, electric, low-alloyed

0.34

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.

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.

producer amount unit location market for clinker 1.00E+00 kilogram ZA

supplier amount unit location market for clinker 1.00E+00 kilogram SAF

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

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.

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_roadfreight_variables.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("dac")

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")

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).

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).

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_variables.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")

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

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-topology.json

gcam-topology.json

tiam-ucl-topology.json

remind-topology.json

image-topology.json

AF

SAS

South Asia

ODA

OAS

RSAS

AG

LAM

Central America and Caribbean

CSA

LAM

N/A

AI

LAM

Central America and Caribbean

CSA

LAM

RCAM

AL

EEU

Europe_Non_EU

WEU

NEU

CEU

AM

FSU

Central Asia

FSU

REF

RUS

AO

AFR

Africa_Southern

AFR

SSA

RSAF

AR

LAM

Argentina

CSA

LAM

RSAM

AS

PAS

Southeast Asia

ODA

OAS

OCE

AT

WEU

EU-15

WEU

EUR

WEU

AU

PAO

Australia_NZ

AUS

CAZ

OCE

AZ

FSU

Central Asia

FSU

REF

RUS

BA

EEU

Europe_Non_EU

EEU

NEU

CEU

BD

SAS

South Asia

ODA

OAS

RSAS

BE

WEU

EU-15

WEU

EUR

WEU

BF

AFR

Africa_Western

AFR

SSA

WAF

BG

EEU

EU-12

EEU

EUR

CEU

BH

MEA

Middle East

MEA

MEA

ME

BI

AFR

Africa_Eastern

AFR

SSA

EAF

BJ

AFR

Africa_Western

AFR

SSA

WAF

BN

PAS

Southeast Asia

MEA

OAS

SEAS

BO

LAM

South America_Southern

CSA

LAM

RSAM

BR

LAM

Brazil

CSA

LAM

BRA

BS

LAM

Central America and Caribbean

CSA

LAM

RCAM

BT

SAS

South Asia

ODA

OAS

RSAS

BW

AFR

Africa_Southern

AFR

SSA

RSAF

BY

FSU

Europe_Eastern

FSU

REF

UKR

BZ

LAM

Central America and Caribbean

CSA

LAM

RCAM

CA

NAM

Canada

CAN

CAZ

CAN

CD

AFR

Africa_Western

AFR

SSA

WAF

CF

AFR

Africa_Western

AFR

SSA

WAF

CG

AFR

Africa_Western

AFR

SSA

WAF

CH

WEU

European Free Trade Association

WEU

NEU

WEU

CI

AFR

Africa_Western

AFR

SSA

WAF

CL

LAM

South America_Southern

CSA

LAM

RSAM

CM

AFR

Africa_Western

AFR

SSA

WAF

CN

CHN

China

CHI

CHA

CHN

CO

LAM

Colombia

CSA

LAM

RSAM

CR

LAM

Central America and Caribbean

CSA

LAM

RCAM

CU

LAM

Central America and Caribbean

CSA

LAM

RCAM

CY

WEU

EU-12

MEA

EUR

N/A

CZ

EEU

EU-12

EEU

EUR

CEU

DE

WEU

EU-15

WEU

EUR

WEU

DJ

AFR

Africa_Eastern

AFR

SSA

EAF

DK

WEU

EU-15

WEU

EUR

WEU

DM

LAM

Central America and Caribbean

CSA

LAM

RCAM

DO

LAM

Central America and Caribbean

CSA

LAM

RCAM

DZ

MEA

Africa_Northern

AFR

MEA

NAF

EC

LAM

South America_Southern

CSA

LAM

RSAM

EE

EEU

EU-12

FSU

EUR

CEU

EG

MEA

Africa_Northern

AFR

MEA

NAF

ER

AFR

Africa_Eastern

AFR

SSA

EAF

ES

WEU

EU-15

WEU

EUR

WEU

ET

AFR

Africa_Eastern

AFR

SSA

EAF

FI

WEU

EU-15

WEU

EUR

WEU

FJ

PAS

Southeast Asia

ODA

OAS

OCE

FR

WEU

EU-15

WEU

EUR

WEU

GA

AFR

Africa_Western

AFR

SSA

WAF

GB

WEU

EU-15

UK

EUR

WEU

GE

FSU

Central Asia

FSU

REF

RUS

GF

LAM

South America_Northern

CSA

LAM

RSAM

GH

AFR

Africa_Western

AFR

SSA

WAF

GI

WEU

EU-15

WEU

EUR

WEU

GL

WEU

EU-15

NEU

NEU

WEU

GM

AFR

Africa_Western

AFR

SSA

WAF

GN

AFR

Africa_Western

AFR

SSA

WAF

GQ

AFR

Africa_Western

AFR

SSA

WAF

GR

WEU

EU-15

WEU

EUR

WEU

GT

LAM

Central America and Caribbean

CSA

LAM

RCAM

GW

AFR

Africa_Western

AFR

SSA

WAF

GY

LAM

South America_Northern

CSA

LAM

RSAM

HK

CHN

China

CHI

CHA

CHN

HN

LAM

Central America and Caribbean

CSA

LAM

RCAM

HR

EEU

Europe_Non_EU

EEU

EUR

CEU

HT

LAM

Central America and Caribbean

CSA

LAM

RCAM

HU

EEU

EU-12

EEU

EUR

CEU

ID

PAS

Indonesia

ODA

OAS

INDO

IE

WEU

EU-15

WEU

EUR

WEU

IL

MEA

Middle East

MEA

MEA

ME

IN

SAS

India

IND

IND

INDIA

IQ

MEA

Middle East

MEA

MEA

ME

IR

MEA

Middle East

MEA

MEA

ME

IS

WEU

European Free Trade Association

WEU

NEU

WEU

IT

WEU

EU-15

WEU

EUR

WEU

JM

LAM

Central America and Caribbean

CSA

LAM

RCAM

JO

MEA

Middle East

MEA

MEA

ME

JP

PAO

Japan

JPN

JPN

JAP

KE

AFR

Africa_Eastern

AFR

SSA

EAF

KG

FSU

Central Asia

FSU

REF

STAN

KH

RCPA

Southeast Asia

ODA

OAS

SEAS

KI

PAS

Southeast Asia

ODA

OAS

OCE

KM

AFR

Africa_Eastern

AFR

SSA

EAF

KN

LAM

Central America and Caribbean

CSA

LAM

RCAM

KP

RCPA

Southeast Asia

ODA

OAS

KOR

KR

PAS

South Korea

SKO

OAS

KOR

KW

MEA

Middle East

MEA

MEA

ME

KY

LAM

Central America and Caribbean

CSA

LAM

RCAM

KZ

FSU

Central Asia

FSU

REF

STAN

LA

RCPA

Southeast Asia

ODA

OAS

SEAS

LB

MEA

Middle East

MEA

MEA

ME

LC

LAM

Central America and Caribbean

CSA

LAM

RCAM

LI

WEU

EU-15

WEU

NEU

WEU

LK

SAS

South Asia

ODA

OAS

RSAS

LR

AFR

Africa_Western

AFR

SSA

WAF

LS

AFR

Africa_Southern

AFR

SSA

RSAF

LT

EEU

EU-12

FSU

EUR

CEU

LU

WEU

EU-15

WEU

EUR

WEU

LV

EEU

EU-12

FSU

EUR

CEU

LY

MEA

Africa_Northern

AFR

MEA

NAF

MA

MEA

Africa_Northern

AFR

MEA

NAF

MC

WEU

EU-15

WEU

NEU

WEU

MD

FSU

Europe_Eastern

FSU

REF

UKR

ME

EEU

Europe_Non_EU

EEU

NEU

CEU

MG

AFR

Africa_Eastern

AFR

SSA

RSAF

MK

EEU

Europe_Non_EU

EEU

NEU

CEU

ML

AFR

Africa_Western

AFR

SSA

WAF

MM

PAS

Southeast Asia

ODA

OAS

SEAS

MN

RCPA

Central Asia

ODA

OAS

CHN

MO

CHN

China

CHI

CHA

CHN

MR

AFR

Africa_Western

AFR

SSA

WAF

MS

LAM

Central America and Caribbean

CSA

LAM

RCAM

MT

WEU

EU-12

WEU

EUR

WEU

MU

AFR

Africa_Eastern

ODA

SSA

EAF

MW

AFR

Africa_Southern

AFR

SSA

RSAF

MX

LAM

Mexico

MEX

MEX

MEX

MY

PAS

Southeast Asia

ODA

OAS

SEAS

MZ

AFR

Africa_Southern

AFR

SSA

RSAF

NA

AFR

Africa_Southern

AFR

SSA

RSAF

NE

AFR

Africa_Western

AFR

SSA

WAF

NG

AFR

Africa_Western

AFR

SSA

WAF

NI

LAM

Central America and Caribbean

CSA

LAM

RCAM

NL

WEU

EU-15

WEU

EUR

WEU

NO

WEU

European Free Trade Association

WEU

NEU

WEU

NP

SAS

South Asia

ODA

OAS

RSAS

NR

PAS

Southeast Asia

ODA

OAS

OCE

NU

PAS

Southeast Asia

ODA

OAS

OCE

NZ

PAO

Australia_NZ

AUS

CAZ

OCE

OM

MEA

Middle East

MEA

MEA

ME

PA

LAM

Central America and Caribbean

CSA

LAM

RCAM

PE

LAM

South America_Southern

CSA

LAM

RSAM

PF

PAS

Southeast Asia

ODA

OAS

OCE

PG

PAS

Southeast Asia

ODA

OAS

INDO

PH

PAS

Southeast Asia

ODA

OAS

SEAS

PK

SAS

Pakistan

ODA

OAS

RSAS

PL

EEU

EU-12

EEU

EUR

CEU

PT

WEU

EU-15

WEU

EUR

WEU

PY

LAM

South America_Southern

CSA

LAM

RSAM

QA

MEA

Middle East

MEA

MEA

ME

RE

AFR

Africa_Eastern

AFR

SSA

EAF

RO

EEU

EU-12

EEU

EUR

CEU

RS

EEU

Europe_Non_EU

EEU

NEU

CEU

RW

AFR

Africa_Eastern

AFR

SSA

EAF

SA

MEA

Middle East

MEA

MEA

ME

SB

PAS

Southeast Asia

ODA

OAS

OCE

SC

AFR

Africa_Eastern

AFR

SSA

EAF

SD

MEA

Africa_Eastern

AFR

MEA

EAF

SE

WEU

EU-15

WEU

EUR

WEU

SG

PAS

Southeast Asia

ODA

OAS

SEAS

SH

AFR

Africa_Western

AFR

SSA

WAF

SI

EEU

EU-12

EEU

EUR

CEU

SK

EEU

EU-12

EEU

EUR

CEU

SL

AFR

Africa_Western

AFR

SSA

WAF

SM

WEU

EU-15

WEU

NEU

WEU

SN

AFR

Africa_Western

AFR

SSA

WAF

SO

AFR

Africa_Eastern

AFR

SSA

EAF

SR

LAM

South America_Northern

CSA

LAM

RSAM

SS

AFR

Africa_Eastern

AFR

SSA

EAF

ST

AFR

Africa_Western

AFR

SSA

WAF

SV

LAM

Central America and Caribbean

CSA

LAM

RCAM

SY

MEA

Middle East

MEA

MEA

ME

SZ

AFR

Africa_Southern

AFR

SSA

RSAF

TC

LAM

Central America and Caribbean

CSA

LAM

RCAM

TD

AFR

Africa_Western

AFR

SSA

WAF

TG

AFR

Africa_Western

AFR

SSA

WAF

TH

PAS

Southeast Asia

ODA

OAS

SEAS

TJ

FSU

Central Asia

FSU

REF

STAN

TL

PAS

Southeast Asia

ODA

OAS

INDO

TM

FSU

Central Asia

FSU

REF

STAN

TN

MEA

Africa_Northern

AFR

MEA

NAF

TO

PAS

Southeast Asia

ODA

OAS

OCE

TR

WEU

EU-15

MEA

MEA

TUR

TT

LAM

Central America and Caribbean

CSA

LAM

RCAM

TV

PAS

Southeast Asia

ODA

OAS

OCE

TZ

AFR

Africa_Southern

AFR

SSA

RSAF

UA

FSU

Europe_Eastern

FSU

REF

UKR

UG

AFR

Africa_Eastern

AFR

SSA

EAF

US

NAM

USA

USA

USA

USA

UY

LAM

South America_Southern

CSA

LAM

RSAM

UZ

FSU

Central Asia

FSU

REF

STAN

VC

LAM

Central America and Caribbean

CSA

LAM

RCAM

VE

LAM

South America_Northern

CSA

LAM

RSAM

VG

N/A

N/A

N/A

LAM

RCAM

VI

NAM

Central America and Caribbean

CSA

LAM

RCAM

VN

RCPA

Southeast Asia

ODA

OAS

SEAS

VU

PAS

Southeast Asia

ODA

OAS

OCE

YE

MEA

Middle East

MEA

MEA

ME

ZA

AFR

South Africa

AFR

SSA

SAF

ZM

AFR

Africa_Southern

AFR

SSA

RSAF

ZW

AFR

Africa_Southern

AFR

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.

Decision: Is the Exchange in Cache?

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.

Final Steps 

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.

TRANSFORM

Mobile batteries

Stationary batteries

Biomass

Regional biomass markets

Power generation

Efficiency adjustment

Combustion-based powerplants

Photovoltaics panels

Markets

Regional electricity markets

High voltage regional markets

High voltage regional markets for aluminium smelters

Storage

Medium voltage regional markets

Low voltage regional markets

Long-term regional electricity markets

Original market datasets

Relinking

Cement production

Dataset proxies

Efficiency adjustment

Carbon Capture and Storage

Cement markets

Clinker-to-cement ratio

Original market datasets

Relinking

Steel production

Dataset proxies

Efficiency adjustment

Carbon Capture and Storage

Steel markets

Original market datasets

Relinking

Transport

Trucks

Fleet average trucks

Relinking

Direct Air Capture

Fuels

Efficiency adjustment

Biofuels

Land use and land use change

Regional supply chains

Hydrogen

Hydrogen supply chains

Fuel markets

Influence of differing LHV on fuel market composition

Heat

CO2 emissions update

Geographical mapping

Regionalization

Decision: Is the Exchange in Cache?

Final Steps

GAINS emission factors

Logs

Final Steps 