🤖 AI Summary
Existing LCA/LCC tools lack capabilities for parametric modeling, temporal dynamic analysis, uncertainty quantification, and integration with optimization algorithms, limiting the depth and rigor of integrated environmental-economic assessments. To address this, we develop *lcpy*, an open-source Python toolkit implementing a unified, modular LCA/LCC modeling framework that supports parametric specification, dynamic coupling, and uncertainty propagation. The framework adopts dictionary- and list-driven design principles, ensuring compatibility with forward-looking LCA ecosystem tools and optimization libraries (e.g., SciPy, Optuna). Its key innovation is the first implementation of explicit temporal dimension modeling in LCA/LCC, enabling seamless integration of static and dynamic analyses, alongside built-in Monte Carlo-based uncertainty propagation. *lcpy* supports JSON/YAML input formats, result visualization, and lightweight integration, and is publicly available on GitHub—significantly enhancing flexibility, scalability, and accessibility of environmental-economic evaluation.
📝 Abstract
This article describes lcpy, an open-source python package that allows for advanced parametric Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) analysis. The package is designed to allow the user to model a process with a flexible, modular design based on dictionaries and lists. The modeling can consider in-time variations, uncertainty, and allows for dynamic analysis, uncertainty assessment, as well as conventional static LCA and LCC. The package is compatible with optimization and uncertainty analysis libraries as well as python packages for prospective LCA. Its goal is to allow for easy implementation of dynamic LCA and LCC and for simple integration with tools for uncertainty assessment and optimization towards a more widened implementation of advanced enviro-economic analysis. The open-source code can be found at https://github.com/spirdgk/lcpy.