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.

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.

The tool currently supports the following IAMs:

• Update and addition of REMIND and IMAGE scenarios to the latest versions (REMIND v.3.5, IMAGE v.3.4).
• Addition of REMIND-EU scenarios: these scenarios are based on the REMIND model and further subdivide the European region into 8 geographies (France, Germany, Portugal-Spain, etc.).
• Additional sectoral updates:
  • transport (shipping, rail, road)
    • shipping: new inventories for different powertrain types (marine oil, ammonia, methanol, hydrogen),
    • rail: new inventories for different rail technologies (diesel, electric, etc.),
    • road: new inventories for different road transport technologies (ICEV, BEV, FCEV, etc.),
  • carbon dioxide removal mixes are introduced (direct air capture, enhanced rock weather, ocean liming, etc.),
  • metals: metal intensity factors (Pt, Cu, Co, Mn, etc.) in energy technologies (wind turbines, PVs, BEV) are updated to reflect current and projected trends,
  • mining waste: impoundment of sulfidic tailings is gradually replaced by back-filling,
  • heat: residential and industrial heat mixes are introduced, with different technologies (heat pumps, district heating, etc.) and energy sources (biomass, electricity, etc.),
  • and battery: different battery technology mixes are introduced (Lithium, post-Lithium and Mix) and battery energy densities are adjusted over time.
• Additional technology representation for primary steel production (direct reduced iron, hydrogen-based steelmaking, etc.).
• Regionalization of biomass-producing forestry activities.
• PathwaysDataPackage: a new class that allows exporting data packages to pathways.

https://premise.readthedocs.io/en/latest/

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.

• 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.
• Some IAM output files come with the library and are located by default in the subdirectory "/data/iam_output_files". A file path can be specified to fetch IAM output files elsewhere on your computer.
• Brightway (optional). If you want to use the results in the Brightway framework (and Activity Browser), 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.

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.

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

The best way is to follow the examples from the Jupyter Notebook.

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

IAMs, such as REMIND, IMAGE, and TIAM-UCL, 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.

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.

• 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.

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.

Do not hesitate to contact romain.sacchi@psi.ch.

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

• Romain Sacchi
• Alvaro Hahn Menacho

See contributing.

BSD-3-Clause. Copyright 2025 Potsdam Institute for Climate Impact Research, Paul Scherrer Institut.