
Alicia Arenzana developed and refined the Blue Carbon Cost Tool in the Vizzuality/tnc-blue-carbon-cost-tool repository, focusing on scenario-based cost modeling and robust data workflows. She engineered Jupyter Notebook-based pipelines using Python and Pandas to automate data ingestion, cleaning, and visualization, integrating Excel automation and API-driven data sources. Her work included refactoring cost and abatement calculations, implementing IRR analysis, and enhancing data reliability through improved validation and error handling. By consolidating code, resolving merge conflicts, and maintaining repository hygiene, Alicia delivered a stable, demo-ready prototype that supports data-driven decision-making and accelerates stakeholder engagement for blue carbon project planning.

June 2025: Delivered a robust prototype for the Blue Carbon Cost Tool with IRR analysis considerations, improved data robustness, and repository hygiene. The work enables data-driven decision support for blue carbon projects and establishes a foundation for production-grade tooling.
June 2025: Delivered a robust prototype for the Blue Carbon Cost Tool with IRR analysis considerations, improved data robustness, and repository hygiene. The work enables data-driven decision support for blue carbon projects and establishes a foundation for production-grade tooling.
May 2025 performance summary for Vizzuality/tnc-blue-carbon-cost-tool: Delivered key features to improve cost modeling accuracy and data reliability, hardened data ingestion/export workflows to prevent data loss, and tightened data integration across project_v2/tab datasets. Refined map visualizations to support data-driven decisions, and established groundwork to ensure credits data quality and downstream impact calculations. This set of changes reduces risk of incorrect credit reporting, increases trust in unit economics like cost_per_tCO2e_NPV, and accelerates stakeholder-facing insights.
May 2025 performance summary for Vizzuality/tnc-blue-carbon-cost-tool: Delivered key features to improve cost modeling accuracy and data reliability, hardened data ingestion/export workflows to prevent data loss, and tightened data integration across project_v2/tab datasets. Refined map visualizations to support data-driven decisions, and established groundwork to ensure credits data quality and downstream impact calculations. This set of changes reduces risk of incorrect credit reporting, increases trust in unit economics like cost_per_tCO2e_NPV, and accelerates stakeholder-facing insights.
In April 2025, the tnc-blue-carbon-cost-tool project progressed a suite of end-to-end enhancements for scenario-based cost modeling and automated data workflows, delivering measurable business value and improved maintainability. Key features were implemented, bugs addressed, and the repository kept in a clean, scalable state to support rapid iteration.
In April 2025, the tnc-blue-carbon-cost-tool project progressed a suite of end-to-end enhancements for scenario-based cost modeling and automated data workflows, delivering measurable business value and improved maintainability. Key features were implemented, bugs addressed, and the repository kept in a clean, scalable state to support rapid iteration.
2025-03 Monthly Summary: Stabilized and cleaned up the Blue Carbon Notebook workflow for the tnc-blue-carbon-cost-tool, delivering a reliable, up-to-date prototype and enabling faster decision support. Key changes focus on two Jupyter notebooks (High_level_overview.ipynb and TNC_BlueCarbonTool_prototype.ipynb) with deduplicated code, resolved merge conflicts, and streamlined data processing to a stable baseline suitable for demos and further development.
2025-03 Monthly Summary: Stabilized and cleaned up the Blue Carbon Notebook workflow for the tnc-blue-carbon-cost-tool, delivering a reliable, up-to-date prototype and enabling faster decision support. Key changes focus on two Jupyter notebooks (High_level_overview.ipynb and TNC_BlueCarbonTool_prototype.ipynb) with deduplicated code, resolved merge conflicts, and streamlined data processing to a stable baseline suitable for demos and further development.
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