premise

PRospective EnvironMental Impact AsSEssment

Coupling the ecoinvent database with projections from Integrated Assessment Models (IAM)

premise is a Python tool for prospective life cycle assessment.
It allows users to project the ecoinvent 3 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,
its outputs can naturally be used in Activity Browser,
but also in other LCA software, such as SimaPro, OpenLCA, or directly in Python.

The tool is described in the following scientific publication: Sacchi et al, 2022.
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: Daniel Huppmann, Matthew Gidden, Oliver Fricko, Peter Kolp, Clara Orthofer, Michael Pimmer, Nikolay Kushin, Adriano Vinca, Alessio Mastrucci, Keywan Riahi, Volker Krey, The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development, Environmental Modelling & Software, 2019, https://doi.org/10.1016/j.envsoft.2018.11.012.
• 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:

Changelog

Release notes are maintained in CHANGELOG.md.

Documentation

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. Please note that the ecoinvent database is not included in this package. Also, read ecoinvent's GDPR & EULA.
• Scenarios available through Premise can be consulted here.
• 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.

[!WARNING]
If you wish to use standard IAM scenarios, you need to request (by email) 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.

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.

Current and past Contributors

• Romain Sacchi
• Alois Dirnaichner
• Tom Mike Terlouw
• Laurent Vandepaer
• Chris Mutel
• Alvaro Hahn Menacho
• David Bantje

Code Maintainers

• Romain Sacchi
• Alvaro Hahn Menacho

IAM Mapping Collaborators

premise benefits from input from researchers in the IAM teams below who have
helped align model outputs and mappings.

REMIND and REMIND-EU

• David Bantje, Doctoral Researcher,
  Energy Transition Lab, Research Department Transformation Pathways, Potsdam
  Institute for Climate Impact Research (PIK)
• Alois Dirnaichner, former researcher at
  PIK (PIK alum, 2018-2022; current
  institution: freelancer; expertise: transport and life-cycle impact
  assessment)
• Gunnar Luderer, Deputy Head,
  Research Department Transformation Pathways, and Head of the Energy
  Transition Lab, PIK

IMAGE

• Vassilis Daioglou, Department of Global
  Sustainability, PBL Netherlands Environmental Assessment Agency; IMAGE team
  coordination on energy

MESSAGE

• Florian Maczek, Integrated Assessment and
  Climate Change Research Group, Energy, Climate, and Environment Program,
  International Institute for Applied Systems Analysis (IIASA)
• Volker Krey, Research Group Leader and Principal
  Research Scholar, Integrated Assessment and Climate Change Research Group,
  Energy, Climate, and Environment Program, IIASA

TIAM-UCL

• Joris Šimaitis, contributor to recent TIAM-UCL and premise integration
  work; current public affiliation in 2025 publications: University of Bath.
• Isabela Butnar, Principal Research Fellow, UCL
  Institute for Sustainable Resources, The Bartlett School of Environment,
  Energy and Resources

GCAM

• Tapajyoti Ghosh, Researcher IV,
  Environmental Engineering, Strategic Energy Analysis Center, National
  Renewable Energy Laboratory (NREL)
• Brian Gentry, Strategic Energy Analysis Center, NREL.
• Patrick Lamers, Researcher V, Decision
  Support Analysis, and Senior Topic and Team Lead, Strategic Energy Analysis
  Center, NREL

Contributing

See contributing.

References

License

BSD-3-Clause.
Copyright 2026 Potsdam Institute for Climate Impact Research, Paul Scherrer Institut.