# ``premise``
<div style="text-align:center">
<img src="https://github.com/romainsacchi/premise/raw/master/docs/large.png" height="300"/>
</div>
# **PR**ospective **E**nviron**M**ental **I**mpact As**SE**ssment
## Coupling the ecoinvent database with projections from Integrated Assessment Models (IAM)
<p align="center">
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</p>
Previously named *rmnd-lca*. *rmnd-lca* was designed to work with the IAM model REMIND only.
As it now evolves towards a more IAM-neutral approach, a change of name was considered.
What's new in 0.2.0?
====================
* CODE-BREAKING CHANGES --> New workflow (please check [examples notebook](https://github.com/romainsacchi/premise/blob/master/examples/examples.ipynb)): better suited for creating several scenarios, as the original ecoinvent database and inventories are only loaded once.
* `update_solar_PV()`: adjusts the efficiency of photovoltaic solar panels in ecoinvent according to the year of projection.
* `update_cars()`: creates car inventories in line with the year of projection. Also creates new fleet average car transport
and links it back to transport-consuming activities.
* `update_trucks()`: creates truck inventories in line with the year of projection. Also creates new fleet average truck transport
and links it back to transport-consuming activities.
* `update_steel()`: creates regional steel markets instead of one "Global" one. For each regional market, the share of
primary vs. secondary steel is adjusted/extrapolated based on recent statistics. These regional markets supply steel-consuming
activities within their geographical scope, but also supplies the global steel market.
Documentation
-------------
[https://premise.readthedocs.io/en/latest/](https://premise.readthedocs.io/en/latest/)
Objective
---------
The objective is to produce life cycle inventories under future energy policies, by modifying the inventory database
ecoinvent 3 to reflect projected energy policy trajectories.
Requirements
------------
* **Python 3.9**
* License for [ecoinvent 3][1]
* Some IAM output files come with the library ("REMIND_xxx.csv" for REMIND, "IMAGE_xxxx.csv" for IMAGE)
and are located by default in the subdirectory "/data/iam_output_files". **If you wish to use
those files, you need to request (by [email](mailto:romain.sacchi@psi.ch)) an encryption key from the developers**.
A file path can be specified to fetch IAM output files elsewhere on your computer.
* [brightway2][2] (optional)
How to install this package?
----------------------------
Two options:
A development version with the latest advancements (but with the risks of unseen bugs),
is available from Anaconda Cloud:
conda install -c romainsacchi premise
For a more stable and proven version, from Pypi:
pip install premise
will install the package and the required dependencies.
Introduction
============
**premise** allows to align the life cycle inventories contained in the **ecoinvent 3.5, 3.6 and 3.7 cutoff** databases with
the output results of Integrated Assessment Models (IAM) **[REMIND][3]** and **[IMAGE][4]**, in order to produce life cycle inventories under
future policy scenarios (from business-as-usual to very ambitious climate scenarios) for any year between 2005 and 2100.
Inputs
------
Either:
* ecoinvent v.3.5, 3.6, 3.7 or 3.7.1 as a registered brightway2 database
* ecoinvent v.3.5, 3.6, 3.7 or 3.7.1 as [ecospold2][5] files
Transformations
---------------
More specifically, **premise** will apply a series of transformation functions to ecoinvent.
In the latest version (0.2.0), the following transformation functions are available:
* **update_electricity()**: alignment of regional electricity production mixes as well as efficiencies for a number of
electricity production technologies, including Carbon Capture and Storage technologies.
* **update_cars()**: new passenger car inventories are created based on [carculator][17], fuel markets that supply passenger cars are adjusted
according to the IAM projections, including penetration of bio- and synthetic fuels. Then, given a fleet composition, markets for passenger car transport are created.
Finally, these transport markets link back to transport-consuming activities.
* **update_trucks()**: new truck inventories are created based on [carculator_truck][18], fuel markets that supply trucks are adjusted
according to the IAM projections, including penetration of bio- and synthetic fuels. Then, given a fleet composition, markets for truck transport are created.
Finally, these transport markets link back to lorry transport-consuming activities.
* **update_cement()**: adjustment of technologies for cement production (dry, semi-dry, wet, with pre-heater or not),
fuel efficiency of kilns, fuel mix of kilns (including biomass and waste fuels) and clinker-to-cement ratio.
* **update_steel()**: creation of regional low-alloy steel markets and correction/projection of primary vs. secondary steel supply.
* **update_solar_PV()**: adjustment of solar PV modules efficiency, to reflect current (18-20%) and future (25%) efficiencies.
However, whether or not these transformation functions can be applied will depend on the existence of the necessary variables in
the IAM file you use as input.
|Function |Implemented?|Description |REMIND|IMAGE|Other IAM|Comment |
|--------------------------------|------------|-----------------------------------------------------------------------|------|-----|---------|--------------------------------------|
|update_electricity()| Yes | Aligns electricity markets and power plants efficiencies | Yes | Yes | No | |
|update_cars() | Yes | Creates fleet average passenger cars as projected by the IAM | Yes | Yes | No | Uses default projection if the IAM does not provide a fleet projection. |
|update_trucks() | Yes | Creates fleet average lorries as projected by the IAM | Yes | Yes | No | Uses default projection if the IAM does not provide a fleet projection. |
|update_cement() | Yes | Aligns clinker and cement production and supply | Yes | Yes | Yes | Uses external data sources ([WBCSD][6] and [IEA][8])|
|update_steel() | Yes | Aligns primary and secondary steel production and supply| Yes | No | No | Uses external data source ([BIR][19])|
|update_metal_markets() | Not yet | Aligns share of metal extraction vs. recycling and and supply with IAM | No | No | No | |
|update_solar_PV() | Yes | Aligns solar PV modules efficiency | Yes | Yes | Yes | Uses external data source ([PSI][7]) |
The following REMIND IAM files come with the library:
* SSP2
1. **Base:** counter-factual scenario with no climate policy implemented
2. **NPi** (*N*ational *P*olicies *i*mplemented): scenario describes energy, climate and economic projections for the period until 2030, and equivale
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PyPI 官网下载 | premise-0.4.5.tar.gz (138个子文件)
setup.cfg 38B
GAINS emission factors.csv 15.78MB
remind_SSP2-PkBudg1100.csv 3.1MB
remind_SSP2-PkBudg1300.csv 3.1MB
remind_SSP2-PkBudg900.csv 3.09MB
remind_SSP2-PkBudg1100_Elec.csv 3.08MB
remind_SSP2-PkBudg1300_Elec.csv 3.07MB
remind_SSP2-PkBudg900_Elec.csv 3.06MB
Budg1100_H2Push_vintcomp.csv 2.95MB
Budg1100_H2Push_vintcomp.csv 2.95MB
Budg1100_H2Push_LowD_vintcomp.csv 2.93MB
Budg1100_H2Push_LowD_vintcomp.csv 2.93MB
Budg1100_ElecPush_vintcomp.csv 2.92MB
Budg1100_ElecPush_vintcomp.csv 2.92MB
Budg1100_ElecPush_LowD_vintcomp.csv 2.91MB
Budg1100_ElecPush_LowD_vintcomp.csv 2.91MB
remind_SSP2-Npi.csv 2.87MB
remind_SSP2-Base.csv 2.87MB
fleet_file.csv 2.86MB
Budg1100_Conv_vintcomp.csv 2.86MB
fleet_file.csv 2.86MB
Budg1100_Conv_vintcomp.csv 2.86MB
Budg1100_ConvSyn_vintcomp.csv 2.86MB
Budg1100_ConvSyn_vintcomp.csv 2.86MB
Budg1100_ConvSyn_LowD_vintcomp.csv 2.84MB
Budg1100_ConvSyn_LowD_vintcomp.csv 2.84MB
Budg1100_Conv_LowD_vintcomp.csv 2.83MB
Budg1100_Conv_LowD_vintcomp.csv 2.83MB
remind_SSP2-NDC.csv 2.68MB
image_SSP2-RCP19.csv 2.67MB
image_SSP2-RCP26.csv 2.51MB
image_SSP2-Base.csv 2.23MB
simapro_categories.csv 1.79MB
fleet_file.csv 573KB
fleet_file.csv 573KB
flows_biosphere.csv 430KB
flows_biosphere_37.csv 377KB
additional_data_GNR.csv 174KB
electricity_production_volumes_per_tech.csv 143KB
references.csv 63KB
simapro_classification.csv 19KB
migration_map.csv 13KB
losses_per_country.csv 8KB
regionmappingH12.csv 5KB
clinker_ratio_ecoinvent_36.csv 5KB
clinker_ratio_ecoinvent_35.csv 4KB
secondary_steel_ratios.csv 4KB
clinker_ratios.csv 4KB
steel_recycling_shares.csv 3KB
electricity_markets.csv 3KB
GAINStoREMINDtechmap.csv 2KB
electricity_efficiencies.csv 2KB
electricity_production_volumes_per_country.csv 1KB
electricity_emissions.csv 1KB
fix_names.csv 272B
efficiency_solar_PV.csv 188B
ecoinvent_to_gains_emission_mappping.csv 148B
MANIFEST.in 889B
simapro-biosphere.json 55KB
LICENSE 2KB
README.md 12KB
image_pass_cars_inventory_data_ei_37_2025.pickle 2.98MB
image_pass_cars_inventory_data_ei_37_2020.pickle 2.95MB
image_pass_cars_inventory_data_ei_37_2050.pickle 2.94MB
image_pass_cars_inventory_data_ei_37_2045.pickle 2.93MB
image_pass_cars_inventory_data_ei_37_2040.pickle 2.93MB
image_pass_cars_inventory_data_ei_37_2035.pickle 2.92MB
image_pass_cars_inventory_data_ei_37_2030.pickle 2.87MB
image_trucks_inventory_data_ei_37_2045.pickle 2.53MB
image_trucks_inventory_data_ei_37_2050.pickle 2.5MB
image_trucks_inventory_data_ei_37_2040.pickle 2.5MB
image_trucks_inventory_data_ei_37_2035.pickle 2.48MB
image_trucks_inventory_data_ei_37_2030.pickle 2.41MB
image_trucks_inventory_data_ei_37_2025.pickle 2.28MB
image_trucks_inventory_data_ei_37_2020.pickle 2.11MB
inventory_data_ei_35.pickle 1.67MB
inventory_data_ei_37.pickle 1.67MB
inventory_data_ei_36.pickle 1.67MB
remind_pass_cars_inventory_data_ei_37_2025.pickle 1.57MB
remind_pass_cars_inventory_data_ei_37_2020.pickle 1.55MB
remind_pass_cars_inventory_data_ei_37_2050.pickle 1.5MB
remind_pass_cars_inventory_data_ei_37_2045.pickle 1.5MB
remind_pass_cars_inventory_data_ei_37_2040.pickle 1.5MB
remind_pass_cars_inventory_data_ei_37_2035.pickle 1.49MB
remind_pass_cars_inventory_data_ei_37_2030.pickle 1.47MB
remind_trucks_inventory_data_ei_37_2045.pickle 1.27MB
remind_trucks_inventory_data_ei_37_2050.pickle 1.27MB
remind_trucks_inventory_data_ei_37_2040.pickle 1.26MB
remind_trucks_inventory_data_ei_37_2035.pickle 1.25MB
remind_trucks_inventory_data_ei_37_2030.pickle 1.21MB
remind_trucks_inventory_data_ei_37_2025.pickle 1.13MB
remind_trucks_inventory_data_ei_37_2020.pickle 1.04MB
PKG-INFO 3KB
PKG-INFO 3KB
inventory_imports.py 86KB
steel.py 66KB
cement.py 65KB
electricity.py 61KB
metals.py 60KB
export.py 37KB
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