Session 3: Data Infrastructure & Big Data

Background: Exiobase

Exiobase is a multi-regional, environmentally-extended input-output (MRIO) database tracking economic activity and environmental impacts across global supply chains. Unlike OWID's aggregated national totals, Exiobase provides sectoral resolution: emissions from electricity generation, transport, manufacturing, agriculture, and hundreds of other economic activities across 49 regions.

Key characteristics:

• Structure: Input-output tables linking inter-industry flows with environmental extensions
• Coverage: CO₂, CH₄, land use, water consumption, and more
• Applications: Consumption-based emissions accounting, supply chain analysis, trade embodied emissions, sectoral decarbonization pathways

Methodological note: MRIO models allocate emissions to final consumers rather than production locations. A smartphone manufactured in China but consumed in the US would have emissions attributed differently in Exiobase (consumption-based) versus OWID (territorial).

Suggested Readings

• Exiobase Community on Zenodo — Original data repository and documentation
• Exiobase Official Website — Project overview and methodology
• Exiobase-3 on Source Cooperative — Cloud-optimized GeoParquet format we'll use
• Stadler et al. (2022) — "EXIOBASE 3rx: A flexible multi-regional input-output database" in Scientific Data
• Carbon Brief: CO2 Importers & Exporters — Accessible overview of consumption vs territorial accounting

What You'll Explore

The session notebook (in your module template repository) guides you through:

• Access friction: Attempt to load Exiobase from its original Zenodo archive—experience the barriers to automated workflows
• Cloud-optimized formats: Load the same data from GeoParquet—observe the difference in agent performance
• Schema exploration: Navigate the complex structure (163 industries × 49 regions × multiple environmental pressures)
• Sectoral analysis: Identify top emission-intensive industries globally and by country
• Cross-dataset validation: Compare Exiobase totals with OWID—investigate methodological discrepancies
• Synthesis analysis: Independent investigation integrating multiple data sources

Learning Objectives

1. Evaluate how data format and access patterns constrain automated workflows
2. Compare schema complexity across datasets with different design goals
3. Integrate multi-source data requiring methodological reconciliation
4. Assess when coding agents can operate autonomously versus when domain expertise must guide analysis

Key Insight