Construction of an enterprise-level global supply chain database

Area of Science:

• Economics
• Environmental Science
• Data Science

Background:

• Input-output models are crucial for economic, regulatory, and environmental applications, informing policies like Scope 3 emissions targets.
• Existing national input-output tables lack the resolution needed for detailed supply chain analysis.
• Environmental burden-shifting and interdependency insights are limited by current sector-level data.

Purpose of the Study:

• To construct a novel enterprise-level multi-regional input-output (EMRIO) table.
• To enhance the precision and utility of global supply chain data for various applications.
• To provide a more disaggregated resource for policy and analysis.

Main Methods:

• Merging official national input-output tables with firm-level production and transaction data.
• Creating a globally consistent database of company-level purchases and sales.
• Developing an EMRIO table encompassing 9,466 companies, 86,305 subsegments, and 121 countries.

Main Results:

• Construction of a comprehensive EMRIO table with unprecedented enterprise-level detail.
• A globally consistent account of supply chain transactions across numerous companies and countries.
• Demonstration of the enhanced resolution for supply chain analyses.

Conclusions:

• The EMRIO table provides a significant advancement over traditional input-output models.
• Finer resolution data enables more precise economic and environmental impact assessments.
• This resource supports improved policy development for supply chain transparency and sustainability.