WS3: An open-source Python framework for integrated simulation and optimization of forest landscape and wood supply systems

Gregory Paradis 

Transparent, reproducible decision support for forest landscape planning increasingly requires integrated treatment of harvest scheduling, spatial allocation, and greenhouse gas (GHG) accounting. We present WS3, an open-source Python framework for long-horizon wood supply modeling that combines modular simulation with solver-backed optimization and hybrid spatial/aspatial workflows. WS3 represents stands, actions, and transitions through a clear data model, supports scenario-based analyses, and interoperates with geospatial rasters for spatial allocation of aspatial schedules. A key contribution is a built-in linkage to the open-source Canadian Forest Service Carbon Budget Model (libCBM), enabling seamless carbon stock and flux estimation alongside traditional harvest and area outcomes. We demonstrate WS3 through reproducible use-case demonstrations that couple optimization-driven harvest scheduling with libCBM carbon accounting and a raster allocation step to visualize spatial implications. These demonstrations highlight how alternative policy constraints influence harvest flows and carbon dynamics, illustrating the framework’s utility for climate change mitigation planning. The full code, documentation, and notebooks are publicly available via GitHub and PyPI, with a versioned archive on Zenodo, and a complete reproduction package accompanying this article. WS3 lowers barriers to rigorous, extensible analysis, supporting researchers and practitioners seeking open, auditable workflows for forest sector planning under evolving climate and policy objectives.