Database development in Python and SQL for the Voluntary Registry Offsets Database
Barbara Haya, Research Fellow
Public Policy
Applications for Spring 2026 are closed for this project.
The Berkeley Carbon Trading Project's Voluntary Registry Offsets Database is an important source of information and transparency in the carbon offset market and has been widely used by researchers, offset credit raters, offset buyers, and others. We welcome help from advanced undergraduate students in updating the database code in Python, building an SQL database, and other coding/data tasks.
Role: Five years ago a UC Berkeley senior developed the database in Excel. Since then UC Berkeley students (graduate and undergraduate) have fully automated the database processing in Python. This semester we will create an SQL database to support an on line dashboard in addition to the downloadable Excel. We can also update the machine learning system for predicting project types if a student has experience in machine learning.
You'll have a chance to be on the development team of this widely used database, and to get experience in a range of database development tasks, including coding, documentation, debugging, data finding on developments in the offset registries, data analysis for the dashboard we'll be developing, and other tasks.
Qualifications: Experience in Python needed.
Interest in carbon offsets and climate mitigation a plus.
Experience in SQL a plus.
Experience in machine learning a plus.
Day-to-day supervisor for this project: Pamela Quartson, masters student in Information Management and Systems
Hours: to be negotiated
Off-Campus Research Site: This work can be done remotely.
Related website: https://gspp.berkeley.edu/berkeley-carbon-trading-project/offsets-database
Environmental Issues Social Sciences