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 .. code-block:: python 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) BoP mass share [%] Battery energy [kWh/kg cell] 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) BoP mass share [%] Battery energy [kWh/kg cell] 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. .. _Degen: https://www.nature.com/articles/s41560-023-01355-z 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) BoP mass share [%] Battery energy [kWh/kg cell] 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) BoP mass share [%] Battery energy [kWh/kg cell] 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. ======================================================== =========== ============================================================================= .. _Schlichenmaier: https://doi.org/10.1016/j.egyr.2022.11.025 `market for battery capacity, stationary (CONT scenario)` supplies any storage capacity needed in high voltage electricity markets. Biomass +++++++ Run .. code-block:: python 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. .. code-block:: python ndb.generate_scenario_report() Power generation ++++++++++++++++ Run .. code-block:: python 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. .. _Oberschelp: https://www.nature.com/articles/s41893-019-0221-6 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: .. code-block:: python 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] ===================== ==================== ==================== =================== ================== ================== ================= ================== ================== ========================================= .. [1] https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf .. [2] https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf .. [3] https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf .. [4] https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf. For future efficiency: own assumption, -+25%. .. [5] Future eff: Fraunhofer ISE Photovoltaics Report 2019; Uncertainty: Own assumption: -+25%. .. [6] https://www.sciencedirect.com/science/article/pii/S0927024823001101 .. [7] https://link.springer.com/article/10.1007/s11367-020-01791-z .. [8] https://pubs.rsc.org/en/content/articlelanding/2022/se/d2se00096b; https://www.csem.ch/en/news/photovoltaic-technology-breakthrough-achieving-31.25-efficiency/ The sources for these efficiencies are also given in the inventory file LCI_PV_: .. _LCI_PV: https://github.com/polca/premise/blob/master/premise/data/additional_inventories/lci-PV.xlsx 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). .. _IAI Beyond 2 Degrees Aluminium Roadmap: https://international-aluminium.org/resource/aluminium-sector-greenhouse-gas-pathways-to-2050-2021/ 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). .. _Ozawa: https://doi.org/10.1016/j.ijhydene.2019.02.230 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. .. code-block:: python 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. .. _Muller: https://doi.org/10.1016/j.jclepro.2024.141884 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 .. code-block:: python 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. .. code-block:: python 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. .. _IEA: https://iea.blob.core.windows.net/assets/cbaa3da1-fd61-4c2a-8719-31538f59b54f/TechnologyRoadmapLowCarbonTransitionintheCementIndustry.pdf 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. .. _Volkart: https://doi.org/10.1016/j.ijggc.2013.03.003 .. _Institute: https://www.globalccsinstitute.com/wp-content/uploads/2022/05/State-of-the-Art-CCS-Technologies-2022.pdf .. note:: You can check the the carbon capture rate for cement production assumed in your scenarios by generating a scenario summary report. .. code-block:: python ndb.generate_scenario_report() Cement markets -------------- Run .. code-block:: python 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 .. code-block:: python 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. .. code-block:: python 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. .. _steel: https://github.com/polca/premise/blob/master/premise/data/battery/scenario.csv 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. ============================================ =========== ================ =========== 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 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 .. code-block:: python 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. .. _VECTO: https://ec.europa.eu/clima/eu-action/transport-emissions/road-transport-reducing-co2-emissions-vehicles/vehicle-energy-consumption-calculation-tool-vecto_en The truck vehicle model is from Sacchi_ et al, 2021. .. _Sacchi: https://pubs.acs.org/doi/abs/10.1021/acs.est.0c07773 .. 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: +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | Transport Type | REMIND | IMAGE | TIAM-UCL | +===========================================================+======================+======================+======================+ | transport, freight, lorry 16-32 metric ton, EURO1 | 26t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, EURO3 | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 16-32 metric ton, EURO5 | 26t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry >32 metric ton, EURO1 | 40t gross weight | 40t gross weight | 40t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, EURO4 | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry, all sizes, EURO1 to market | unspecified, long haul| unspecified, long haul| unspecified, long haul| +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO6 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO1 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry, all sizes, EURO3 to market | unspecified, long haul| unspecified, long haul| unspecified, long haul| +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 16-32 metric ton, EURO6 | 26t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO2 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO3 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO4 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 16-32 metric ton, EURO2 | 26t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry >32 metric ton, EURO6 | 40t gross weight | 40t gross weight | 40t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, EURO2 | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, EURO1 | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry, all sizes, EURO2 to market | unspecified, long haul| unspecified, long haul| unspecified, long haul| +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 16-32 metric ton, unregulated | 26t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry >32 metric ton, unregulated | 40t gross weight | 40t gross weight | 40t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry >32 metric ton, EURO3 | 40t gross weight | 40t gross weight | 40t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, unregulated | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, EURO5 | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 3.5-7.5 metric ton, EURO6 | 7.5t gross weight | 18t gross weight | 7.5t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | | long haul | long haul | long haul | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ | transport, freight, lorry 7.5-16 metric ton, unregulated | 18t gross weight | 18t gross weight | 18t gross weight | | | unspecified powertrain,| unspecified powertrain,| unspecified powertrain,| | +-----------------------------------------------------------+----------------------+----------------------+----------------------+ Direct Air Capture ++++++++++++++++++ Run .. code-block:: python 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. .. _Qiu: https://doi.org/10.1038/s41467-022-31146-1 Fuels +++++ Run .. code-block:: python 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). .. _Wulf: https://www.sciencedirect.com/science/article/pii/S095965261832170X .. _Cerniauskas: https://doi.org/10.1016/j.ijhydene.2020.02.121 .. _Hank: https://pubs.rsc.org/en/content/articlelanding/2020/se/d0se00067a .. _Schori: https://treeze.ch/fileadmin/user_upload/downloads/PublicLCI/Schori_2012_NaturalGas.pdf ========================== ================= ======== ======= ============== =============== ==================== _ _ 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 .. code-block:: python 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 .. image:: map_image.png :width: 500pt :align: center Map of REMIND regions .. image:: map_remind.png :width: 500pt :align: center *premise* uses the following correspondence between ecoinvent locations and IAM regions. This mapping is performed by the constructive_geometries_ implementation in the wurst_ library. .. _constructive_geometries: https://github.com/cmutel/constructive_geometries .. _wurst: https://github.com/polca/wurst =============== ================================= ================================ ======================== =========================== ======================== 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: **Decision Tree for Processing Datasets** The process begins with a dataset that requires processing. .. contents:: :local: Decision: Is the Exchange in Cache? ----------------------------------- - **Yes** - Use :func:`process_cached_exchange`. - Retrieve cached data. - Update ``new_exchanges`` with cached data. - **No** - Use :func:`process_uncached_exchange`. Decision: Number of Possible Datasets ------------------------------------ - **None** - Print a warning and return. - **One** - Use :func:`handle_single_possible_dataset`. - Use the single matched dataset. - Update ``new_exchanges`` with this dataset information. - **Multiple** - Use :func:`handle_multiple_possible_datasets`. Decision: Does Dataset Location Match Possible Dataset Locations? ----------------------------------------------------------------- - **Yes** - Use the matched dataset location. - **No** - Use :func:`process_complex_matching_and_allocation`. Decision: Dataset Location Type -------------------------------- - **IAM Region** - Use :func:`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 :func:`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 :func:`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 .. code-block:: python 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. .. _GAINS-EU: https://gains.iiasa.ac.at/gains/EUN/index.login .. _GAINS-IAM: https://gains.iiasa.ac.at/gains/IAM/index.login 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: .. code-block:: python 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.