Brightcon 2026, hackathon and courses

Energy System Models (ESMs) are powerful tools for designing and assessing energy transition scenarios, but their environmental scope is typically restricted to direct carbon emissions during the operation phase. This narrow framing overlooks the full life-cycle burden of energy infrastructure — from manufacturing to end-of-life — as well as a broad range of impact categories beyond climate change, including human toxicity, water use, land use, and mineral resource depletion. Failing to account for these dimensions risks shifting environmental burdens rather than eliminating them, and misguides decision-making in low-carbon transitions.
mescal is an open-source Python package (MIT license) built as a specialized component of the Brightway Software Framework. It automates the coupling between any bottom-up ESM and LCA, relying on bw2calc and wurst to perform systematic LCI database transformations and LCIA computations. Starting from a user-defined mapping between ESM technologies/resources and ecoinvent (or premise-generated) datasets, mescal performs foreground regionalization, double-counting removal, and parameter harmonization (efficiency, lifetime, capacity factor, physical units) before pre-computing normalized LCIA scores ready for integration in the ESM. After the ESM run, mescal propagates energy system results back into the LCI database, enabling iterative feedback loops between the two models.
A key design principle is model-agnosticism: mescal provides data formats compatible with any bottom-up ESM (TIMES, MESSAGEix, Calliope, EnergyScope, etc.), and the ESM-LCA coupling equations it proposes can support both single-objective and multi-objective optimization. An illustrative case study shows that ESMs restricted to direct CO₂ emissions capture only a small fraction of total environmental impacts in low-carbon scenarios (e.g., 2-10% in our case study), underscoring the necessity of a full life-cycle perspective.
mescal is designed for energy modelers who are not LCA experts, as well as for LCA practitioners wishing to integrate ESM projections into prospective assessments. Its methodology is documented in a peer-reviewed article (Souttre et al., 2026, Journal of Industrial Ecology), and the package is available on PyPI, GitHub, and Zenodo with full documentation and example notebooks.