
Contributed to the Vizzuality/tnc-blue-carbon-cost-tool by developing and refining a suite of data-driven features for blue carbon cost modeling and scenario analysis. Leveraged Python, Jupyter Notebooks, and Pandas to automate Excel workflows, enhance data integration, and improve cost and abatement calculations. Focused on stabilizing notebook execution, resolving merge conflicts, and implementing robust data cleaning and validation routines to ensure reliable outputs. Introduced IRR analysis methods and sensitivity testing, while maintaining repository hygiene through code refactoring and documentation updates. The work enabled faster stakeholder decision support, improved data reliability, and established a foundation for scalable, production-grade financial modeling tools.
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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