remindmodel/remind
Dec 2024 – Mar 2026
3 Months active
Languages Used
GAMSR
Technical Skills
Climate Change EconomicsEconomic ModelingSoftware MaintenanceClimate ModelingCode RefactoringData Modeling
piontek
PROFILE
Piontek
Contributed to the remindmodel/remind repository by integrating the COACCH damage realization model into the main damages module, aligning parameter and set naming conventions for consistency and maintainability. Addressed reliability issues in iterative climate modeling by ensuring the MAGICC model executed correctly across all iterations, updating R scripts and data pipelines to eliminate reporting gaps. Enhanced the stability of social cost of carbon calculations by introducing division-by-zero protection within GAMS modules. Work focused on climate modeling, economic modeling, and software maintenance, demonstrating depth in code refactoring, data modeling, and module management while improving the accuracy and reliability of climate damage assessments.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
1
Lines of code
526
Activity Months3
Your Network
73 peopleWork History
March 2026
1 Commits
Mar 1, 2026March 2026: Delivered reliability improvements for iterative MAGICC model invocation in remind. Resolved issue where the MAGICC model was not invoked after the first iteration due to missing reporting variables. Updated data input and post-solve scripts to ensure proper execution and reporting across iterations, boosting accuracy of climate damage assessments and overall model integrity.
1 Commits
Mar 1, 2026March 2026: Delivered reliability improvements for iterative MAGICC model invocation in remind. Resolved issue where the MAGICC model was not invoked after the first iteration due to missing reporting variables. Updated data input and post-solve scripts to ensure proper execution and reporting across iterations, boosting accuracy of climate damage assessments and overall model integrity.
March 2026
September 2025
4 Commits • 1 Features
Sep 1, 2025Month: 2025-09. This month delivered the COACCH damage model integration into REMIND, embedding the damage realization model into the main damages module and its iterative internalization counterpart. Naming conventions for parameters and sets within COACCH were aligned with REMIND standards. Performed code cleanup and refactoring in the damage modules to improve readability and maintainability. Standardized set names to include module numbers, ensuring consistent references across the integration. The changes enable more accurate damage assessment, easier scenario comparison, and a solid foundation for future enhancements.
September 2025
4 Commits • 1 Features
Sep 1, 2025Month: 2025-09. This month delivered the COACCH damage model integration into REMIND, embedding the damage realization model into the main damages module and its iterative internalization counterpart. Naming conventions for parameters and sets within COACCH were aligned with REMIND standards. Performed code cleanup and refactoring in the damage modules to improve readability and maintainability. Standardized set names to include module numbers, ensuring consistent references across the integration. The changes enable more accurate damage assessment, easier scenario comparison, and a solid foundation for future enhancements.
December 2024
1 Commits
Dec 1, 2024December 2024 monthly summary: Implemented a robust bug fix for SCC calculations in remind model, introducing a small epsilon to prevent division by zero in SCC calculations within the internalizeDamages module. Changes applied across multiple GMS files, improving stability and accuracy of preference parameter calculations. This reduces the risk of NaN results and enhances reliability for production workloads.
1 Commits
Dec 1, 2024December 2024 monthly summary: Implemented a robust bug fix for SCC calculations in remind model, introducing a small epsilon to prevent division by zero in SCC calculations within the internalizeDamages module. Changes applied across multiple GMS files, improving stability and accuracy of preference parameter calculations. This reduces the risk of NaN results and enhances reliability for production workloads.
December 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance76.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
GAMSR
Technical Skills
Climate Change EconomicsClimate ModelingCode RefactoringData ModelingEconomic ModelingModel ConfigurationModule ManagementR programmingSoftware DevelopmentSoftware Maintenanceclimate modelingdata analysis
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