About Gabriel Penedo
Gabriel Penedo is an environmental data scientist based in Pittsburgh, Pennsylvania, working at the intersection of climate finance, environmental justice, geospatial analysis, and sustainability economics. His research and professional work focus on applying quantitative methods to environmental challenges including carbon footprint reduction, green bond market analysis, energy transition financing, and climate resilience planning.
Climate Finance and Green Bond Research
Gabriel built the Global Green Bond Tracker, an interactive mapping tool that visualizes green bond issuance across 114 countries alongside the carbon intensity of national banking systems. The project integrates data from the World Bank, International Monetary Fund, London Stock Exchange Group, and Climate Bonds Initiative to identify gaps in climate finance deployment. His analysis covers sovereign green bonds, ESG bond issuances, carbon footprint of bank loans, certified climate bonds, and the relationship between financial system carbon exposure and green bond activity in both emerging and advanced economies.
Environmental Justice and Spatial Analysis
His spatial analysis work examines the distribution of green building infrastructure across environmental justice communities. Using GIS tools including ArcGIS Pro, the sf package in R, and ArcGIS REST APIs, Gabriel has mapped LEED-certified buildings against Justice 40 designated neighborhoods in Pittsburgh, revealing that only 19 percent of LEED buildings are located within environmental justice areas. His analysis of the Mon Valley communities of Braddock and East Pittsburgh demonstrates significant geographic isolation from green building investment, contributing to ongoing conversations about environmental equity, equitable development, and just transition in post-industrial regions.
Drone-Based Environmental Monitoring
As an Applied Data Scientist at Frontline Gig, Gabriel leads drone-based environmental data collection projects in partnership with the Pennsylvania Department of Transportation. His work includes developing machine learning algorithms for environmental inspection site detection, managing aerial data collection infrastructure using OpenDroneMap, and applying DeepForest tree detection models for urban canopy analysis. He holds a FAA Part 107 Remote Pilot certification for unmanned aircraft operations.
Sustainability Credentials and Recognition
Gabriel is a LEED Green Associate certified by the U.S. Green Building Council, demonstrating expertise in green building practices, sustainable site development, water efficiency, energy and atmosphere, materials and resources, and indoor environmental quality. He was recognized as a 2026 University of Pittsburgh Sustainability Champion for his contributions to sustainability across campus and community, including his work in environmental data science, his founding of EnviroEcon.com, and his professional contributions to neighborhood greening and environmental resilience in Pittsburgh.
Environmental Economics and Policy
Through EnviroEcon.com, Gabriel publishes weekly analysis on environmental economics topics including carbon pricing mechanisms, renewable energy policy, cap-and-trade systems, environmental regulation, clean energy tax incentives, sustainable infrastructure investment, circular economy principles, natural capital accounting, ecosystem services valuation, biodiversity economics, water resource economics, climate adaptation finance, and the economics of environmental compliance. His publication serves as a resource for professionals and policymakers working on the intersection of economic analysis and environmental decision-making.
Technical Expertise
Gabriel's technical toolkit spans environmental data science workflows including geospatial analysis with ArcGIS Pro, ArcGIS Online, ModelBuilder, StoryMaps, GeoPandas, and the sf package; statistical programming in Python and R; machine learning with scikit-learn; data visualization with Tableau, plotly, ggplot2, and R Shiny dashboards; cloud computing on AWS and Azure; and large language model integration for environmental data processing. His quantitative economics training at the University of Pittsburgh provides a foundation in econometrics, causal inference, time series analysis, and applied microeconomics relevant to environmental and resource economics research.