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Time-Explicit Life Cycle Optimization with optimex

optimex is an open-source Python package for time-explicit Life Cycle Optimization (LCO). It finds optimal technology transition pathways while accounting for when emissions occur and how product systems evolve over time.

Standard LCA evaluates predefined product systems. But transition planning asks a different question: which technologies should be deployed, when, and at what scale? That is the domain of Life Cycle Optimization. In fast-changing energy and industrial systems, static formulations can be misleading because construction, operation, and end-of-life happen at different times, while background supply chains and technology performance evolve in parallel.

optimex closes this gap by combining temporalized LCA with optimization in one workflow, so pathways can be assessed against both time-specific and cumulative environmental constraints.

Get Started View on GitHub

Why Go Time-Explicit?

  • Distribution of Flows

    Map life cycle exchanges across their actual timeframes via convolution, capturing time lags between construction, operation, and end-of-life.

  • Technology Evolution

    Track vintage-dependent foreground improvements and links to prospective background databases reflecting supply chain decarbonization.

  • Flexible Operation

    Separate capacity installation from operational dispatch, enabling vintage-specific merit order where cleaner cohorts are utilized first.

  • Dynamic Impact Assessment

    Retain emission timing for dynamic impact assessment (e.g., Radiative Forcing, dynamic GWP), capturing how impacts accumulate over time.

What This Enables

Time-explicit LCO reveals transition strategies that are invisible to static approaches:

  • Vintage-specific dispatch — When multiple cohorts of the same technology coexist, the optimizer preferentially utilizes cleaner vintages, creating an emissions-aware merit order
  • Resource bottleneck navigation — Time-specific constraints on water use, critical minerals, or other resources force technology diversification, revealing realistic pathways through transient scarcity
  • Strategic overcapacity — Early investment in clean technologies can offset stranded fossil assets when the net emission savings outweigh the embodied impacts of idle infrastructure
  • Cumulative budget compliance — By tracking exact emission timing alongside dynamic characterization, pathways can be verified against carbon budgets and other absolute limits

Built on Brightway

optimex is deeply integrated with Brightway. You can model foreground systems with familiar Brightway workflows, add temporal metadata, and convert those models directly into optimization problems.

Re-use Temporalized System Models

If you already have a temporalized product system model, e.g., because you made a time-explicit LCA with bw_timex before, you can directly re-use it with optimex.

optimex is free and open source software, published under the BSD 3-Clause License.

Use Cases

optimex is broadly applicable across sectors where temporal dynamics are decisive for sustainability:

  • Evolving supply chains — Systems depending on electricity, steel, or hydrogen that undergo rapid decarbonization
  • Early-stage technologies — Processes with significant vintage-dependent performance improvements (e.g., electrolyzers, DAC)
  • Circular economy planning — Long material residence times create temporal mismatches between primary demand and secondary supply
  • Time-resolved carbon accounting — Biogenic feedstocks, temporary carbon storage, or CO2 removal with varying temporal profiles
  • Multi-regional supply chains — Sourcing decisions across regions with divergent decarbonization trajectories

Citation

If you use optimex in your research, please consider citing our preprint:

Diepers, T., Tautorus, J., Hartmann, J. M., & von der Assen, N. (2026). A Framework for Time-Explicit Life Cycle Optimization. Research Square. https://doi.org/10.21203/rs.3.rs-9630408/v1

Support

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