AI-Driven Forest Carbon Accounting: A Scoping Review

Gabriel Osei Forkuo's scoping review maps 200 primary sources from 2015 to 2024, analyzing the intersection of artificial intelligence and forest carbon systems. It focuses on European frameworks and is structured across six thematic pillars, including engineering methodologies and digital twins. The dataset provides a synthesized overview of research trends and deployment gaps in this domain.

Use Cases

• Mapping research trends in AI for forest carbon based on the analysis of 200 primary sources.
• Identifying gaps in the deployment of digital twins for carbon accounting from the six thematic pillars.
• Benchmarking engineering methodologies for aboveground biomass estimation within European frameworks.

Strengths

• Based on a substantial corpus of 200 reviewed primary sources.
• Provides a defined temporal scope covering research from 2015 to 2024.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Column names and row counts are not provided, limiting understanding of the dataset's internal structure.
• The organization of the author is unknown, affecting provenance clarity.
• The dataset's size (5,039,750 bytes) suggests a large file, but the specific content format (XLSX/TXT) and data granularity are unclear.

Provenance

Source

Gabriel Osei Forkuo

Collection Method

Scoping review methodology applied to 200 primary sources.

Time Range

2015–2024

Freshness

Last updated on 2026-03-18.

Geography

Focus on European frameworks.