premise 2.3.7
Coupling IAM output to ecoinvent LCA database ecoinvent for prospective LCA
Requires Python: >=3.10

# ``premise``

<div style="text-align:center">
<img src="https://github.com/polca/premise/raw/master/docs/large.png" alt="premise logo" 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)


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``premise`` is a Python tool for prospective life cycle assessment. 
It allows users to project the [ecoinvent 3](https://ecoinvent.org) database into the future, 
using scenarios from Integrated Assessment Models (IAMs). It does so by 
modifying the ecoinvent database to reflect projected energy policy trajectories, include emerging
technologies, modify market shares as well as technologies' efficiency.

In practice, ``premise`` updates selected sectors and markets (e.g., energy supply, transport, fuels,
industrial processes) while leaving other parts of the database unchanged unless explicitly mapped.
Results are scenario- and model-specific, and depend on the IAM model, scenario, year, and the
ecoinvent version used.

Among others, it can be used to assess the environmental impacts of future energy systems,
and to compare different energy policies. It includes a set of IAM scenarios
and a set of tools to create custom scenarios.

The tool was designed to be user-friendly and to allow for reproducible results. 
While it is built on the [brightway framework](https://docs.brightway.dev/en/latest/), 
its outputs can naturally be used in [Activity Browser](https://github.com/LCA-ActivityBrowser/activity-browser), 
but also in other LCA software, such as [SimaPro](https://simapro.com/), [OpenLCA](https://www.openlca.org/), or directly in Python.

The tool is described in the following scientific publication: [Sacchi et al, 2022](https://doi.org/10.1016/j.rser.2022.112311).
If this tool helps you in your research, please consider citing this publication.

Also, use the following references to cite the scenarios used with the tool:

- REMIND and REMIND-EU scenarios: Baumstark et al. REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits, Geoscientific Model Development, 2021.
- IMAGE scenarios: Stehfest, Elke, et al. Integrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications. Netherlands Environmental Assessment Agency (PBL), 2014.
- TIAM-UCL scenarios: Pye, S., et al. The TIAM-UCL Model (Version 4.1.1) Documentation, 2020.
- MESSAGEix-GLOBIOM-GAINS scenarios: MESSAGEix-GLOBIOM-GAINS 2.1-M-R12 documentation.
- GCAM scenarios: Calvin, K., et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.

Models
------

The tool currently supports the following IAMs:

| Model             | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| REMIND v.3.5.2    | REMIND (Regionalized Model of Investment and Development) is an integrated assessment model that combines macroeconomic growth, energy system, and climate policy analysis. It is designed to analyze long-term energy transition pathways, accounting for technological, economic, and environmental factors. REMIND simulates how regions invest in different technologies and energy resources to balance economic growth and climate targets, while considering factors like energy efficiency, emissions, and resource availability. The model is particularly strong in its detailed representation of energy markets and macroeconomic interactions across regions, making it valuable for global climate policy assessments.                                                          |
| REMIND-EU v.3.5.2 | REMIND-EU is a regionalized version of the REMIND model that further subdivides the European region into 8 geographies (France, Germany, Portugal-Spain, etc.). It allows for more detailed analysis of energy transition pathways and climate policies within Europe, considering regional differences in energy resources, technologies, and socio-economic conditions. This model is particularly useful for assessing the impacts of European Union policies on energy systems and climate change mitigation.                                                                                                                                                                                                                                                                             |
| IMAGE v3.4        | IMAGE (Integrated Model to Assess the Global Environment) is a comprehensive IAM developed to explore the interactions between human development, energy consumption, and environmental systems over the long term. It focuses on assessing how land use, food systems, energy systems, and climate change interact under different policy scenarios. The model integrates biophysical processes, such as land-use change and greenhouse gas emissions, with socio-economic drivers like population growth and economic development. IMAGE is commonly used for analyzing sustainable development strategies, climate impacts, biodiversity loss, and exploring mitigation and adaptation options.                                                                                            |
| TIAM-UCL v.4.1    | TIAM-UCL (TIMES Integrated Assessment Model by University College London) is a global energy system model based on the TIMES (The Integrated MARKAL-EFOM System) framework, developed to evaluate long-term decarbonization pathways for global energy systems. It provides detailed insights into energy technology options, resource availability, and emission reduction strategies under various climate policy scenarios. The model focuses on the trade-offs and synergies between energy security, economic costs, and environmental outcomes. TIAM-UCL is frequently used to analyze scenarios consistent with the Paris Agreement and examine technological innovation's role in mitigating climate change globally.                                                                 |
| MESSAGEix-GLOBIOM-GAINS 2.1-M-R12 | MESSAGEix-GLOBIOM-GAINS (MESSAGE) is an integrated assessment model that couples the MESSAGEix energy system with the GLOBIOM land-use model and GAINS air-pollution module. It is used to explore long-term energy and land-use transitions and their climate and air-quality implications under different policy scenarios. |
| GCAM v.8.2        | GCAM (Global Change Analysis Model) is an integrated assessment model that simulates the interactions between energy, water, land use, climate, and economic systems on a global scale. It is designed to analyze how different policy scenarios, technological developments, and socio-economic factors influence greenhouse gas emissions, energy production and consumption, land use changes, and climate outcomes. GCAM incorporates detailed representations of energy technologies, agricultural systems, and land-use dynamics, allowing for comprehensive assessments of mitigation strategies and their implications for sustainable development. The model is widely used for exploring pathways to achieve climate targets while considering trade-offs across multiple sectors.  |


Changelog
---------
Release notes are maintained in [`CHANGELOG.md`](CHANGELOG.md).


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 3.10, 3.11 or 3.12**
* License for [ecoinvent 3][1]. Please note that the ecoinvent database is not included in this package. Also, read ecoinvent's [GDPR & EULA](https://ecoinvent.org/gdpr-eula/).
* Scenarios available through Premise can be consulted [here](https://premisedash-6f5a0259c487.herokuapp.com/).
* If you want to use the results in the Brightway 2 framework, you need `bw2data <4.0.0`. To produce Brightway 2.5-compatible databases, you need `bw2data >=4.0.0`. See the installation instructions below for more details.

> [!NOTE]
> Please note that the ecoinvent database is not included in this package. Also, read ecoinvent's [GDPR & EULA](https://ecoinvent.org/gdpr-eula/).

> [!WARNING]
> If you wish to use standard IAM scenarios, you need to request (by [email](mailto:romain.sacchi@psi.ch)) an encryption key from the developers.

Reproducibility note
--------------------

Outputs are reproducible when using the same ``premise`` version, IAM scenario files, and ecoinvent
release. Changing any of these inputs will change the resulting database.

How to install this package?
----------------------------

Two options:

From Pypi:

    pip install premise

will install the package and the required dependencies.

``premise`` comes with the latest version of ``brightway``, which is Brightway 2.5.
This means that ``premise`` will output databases that are compatible with Brightway 2.5.


> [!WARNING]
> If you want to use the results in the Brightway 2 framework, 
> you need to specify it in the installation command:

>    pip install "premise[bw2]"


A development version with the latest advancements (but with the risks of unseen bugs),
is available from Anaconda Cloud. Similarly, you should specify that you want to use Brightway 2.5:

    conda install -c conda-forge premise-bw25

Or rather use Brightway2:

    conda install -c conda-forge premise-bw2


How to use it?
--------------

The best way is to follow [the examples from the Jupyter Notebook](https://github.com/polca/premise/blob/master/examples/examples.ipynb). 

## Disclaimer on the Use of IAM-Based Scenarios in Premise

It is essential to recognize the nature and 
limitations of the underlying IAM scenarios to responsibly interpret 
and apply ``premise`` outputs.

This disclaimer is informed by the critical insights and recommendations 
presented in the article:

> *de Bortoli, A., Chanel, A., Chabas, C., Greffe, T., & Louineau, E. (2025). More rationality and inclusivity are imperative in reference transition scenarios based on IAMs and shared socioeconomic pathways—recommendations for prospective LCA. Renewable and Sustainable Energy Reviews, 222, 115924. https://doi.org/10.1016/j.rser.2025.115924*

### Nature of IAM-Based Scenarios

IAMs, such as REMIND, IMAGE, MESSAGE, TIAM-UCL, and GCAM, simulate socio-technical 
transitions by combining models of the economy, energy systems, land use, 
and climate. They are used to create scenarios aligned with the Shared 
Socioeconomic Pathways (SSPs) and climate trajectories, such as the Representative 
Concentration Pathways (RCPs) framework developed by the IPCC. These models and 
pathways serve as standardized tools to explore climate mitigation strategies under 
various socioeconomic futures.

While IAMs offer a structured and policy-relevant way to explore 
decarbonization pathways, their scenarios are not predictions. 
They are "what-if" simulations based on a set of assumptions and modeling 
choices that are inherently subjective, value-laden, and reflective of 
specific worldviews.

### Key Limitations

1. **Optimistic Technological Assumptions**  
   Many mainstream IAMs assume aggressive deployment of technologies (e.g., 
   carbon dioxide removal or advanced renewables) that may not be feasible 
   due to technical, economic, or social constraints.

2. **Neglect of Demand-Side and Social Dynamics**  
   IAMs often emphasize technological solutions and underrepresent lifestyle 
   changes, equity, institutional barriers, and behavioral feedbacks.

3. **Blind Spots in Biophysical and Geopolitical Constraints**  
   Constraints on resource availability (e.g., critical minerals, land, 
   water) are often simplified or, for some IAMs, ignored.

4. **Embedded Economic Paradigm and Value Judgments**  
   The SSPs assume perpetual GDP growth and reflect neoclassical economic 
   thinking, potentially sidelining alternative visions of sustainability 
   and well-being.

5. **Underrepresentation of Justice and Global South Perspectives**  
   IAM scenarios may perpetuate global inequalities by assuming continued 
   economic and energy dominance of the Global North.

6. **Opaque Ethical Assumptions**  
   Key ethical parameters such as discount rates or equity considerations 
   are rarely made explicit, despite their large impact on outcomes.

### Recommendations for Responsible Use

- **Transparency**: Clearly state the IAM, SSP, and climate trajectory used. Communicate 
  their assumptions, scope, and limitations to end users.

- **Scenario Screening**: Where possible, favor scenarios with credible 
  assumptions regarding technology deployment, demand-side measures, 
  and material feasibility. Use extreme scenarios (e.g., RCP 1.9, RCP 6.0) 
  cautiously, as they may not reflect realistic futures.

- **Reflect Variability**: Consider using multiple scenarios or ensembles 
  to capture the uncertainty inherent in IAM projections.

- **Ethical Reflexivity**: Recognize and reflect on the value-laden choices 
  embedded in IAMs. Prioritize transparency and intergenerational equity.

### Final Note

``premise`` provides a robust, transparent foundation for scenario-based 
prospective LCA. However, the reliability and legitimacy of resulting 
assessments depend heavily on the careful selection and interpretation 
of input scenarios. Practitioners are encouraged to engage critically 
with the underlying assumptions of IAM-based projections and to use 
``premise`` in conjunction with a broader reflection on possible, 
plausible, and desirable futures.


## Support

Do not hesitate to contact [romain.sacchi@psi.ch](mailto:romain.sacchi@psi.ch).

## Contributors

* [Romain Sacchi](https://github.com/romainsacchi)
* [Alois Dirnaichner](https://github.com/Loisel)
* [Tom Mike Terlouw](https://github.com/tomterlouw)
* [Laurent Vandepaer](https://github.com/lvandepaer)
* [Chris Mutel](https://github.com/cmutel/)
* [Alvaro Hahn Menacho](https://github.com/alvarojhahn)
* [David Bantje](https://github.com/dbantje)


## Maintainers

* [Romain Sacchi](https://github.com/romainsacchi)
* [Alvaro Hahn Menacho](https://github.com/alvarojhahn)

## Contributing

See [contributing](https://github.com/polca/premise/blob/master/CONTRIBUTING.md).

## References

[1]:https://www.ecoinvent.org/
[2]:https://brightway.dev/

## License

[BSD-3-Clause](https://github.com/polca/premise/blob/master/LICENSE).
Copyright 2025 Potsdam Institute for Climate Impact Research, Paul Scherrer Institut.