Product Coverage, Data Philosophy, and Practical Use
A literature-based database designed to capture all available emission factors and inventory datapoints across agriculture and industrial systems
The DEISO Agri & Industrial LCI Database™ provides structured coverage across a defined set of products derived from scientific literature, including emission factors and full inventory datapoints. The database currently includes:
Agriculture & Crops
- Rice, Corn, Sugarcane, Sugar beet
- Tomato, Cucumber
- Cacao, Coffee, Tea
- Palm oil
Livestock & Animal Products
- Beef, Pork, Chicken, Lamb
- Milk, Cheese, Meat products
Food & Beverage Processing
- Beer production
- Chocolate
- Processed and canned food systems
Industrial & Materials
- Aluminum, Nickel, Copper, Gold
- Lithium, Iron ore, Chromium, Cobalt
Why multiple emission factors exist for the same product
A defining feature of this database is that it includes multiple emission factors for the same product, rather than a single averaged value. For example, for a product such as tomato, emission factors for the same pollutant (e.g., CO₂ emissions) may be reported across numerous academic studies — often resulting in 10 or more values from different papers.
This variation is expected and reflects real-world differences in:
- Geographic location (e.g., country or region)
- Production systems (e.g., greenhouse vs. open-field)
- System boundaries (e.g., cradle-to-gate vs. cradle-to-grave)
- Methodological choices in each study
- Technological conditions and time period
The core concept: capturing all literature evidence
The objective of this database is not to reduce variability, but to:
- Extract all emission factors reported in the literature
- Preserve each value with its original context
- Provide a complete and transparent view of available data
This ensures that variability is not hidden, but instead becomes a valuable analytical input.
User responsibility and analytical flexibility
The database provides structured, literature-based values, but does not prescribe a single “correct” emission factor. Users are responsible for selecting the most appropriate value based on:
- Goal and scope of the study
- Geographic relevance
- System boundaries
- Methodological consistency
This supports advanced use cases such as sensitivity analysis, scenario comparison, and transparent reporting.
Sector structure and organization
The database is organized into five main sectors:
- Agriculture & Crops
- Livestock & Animal Products
- Food & Beverage Processing
- Metals & Mining
- Other
This structure enables efficient filtering, targeted analysis, and sector-specific modeling.
How the database supports PCF, GHG, and Scope 3
Product Carbon Footprint (PCF)
- Identify emission factors for products and materials
- Support cradle-to-gate and cradle-to-grave modeling
- Enable comparison across multiple literature values
GHG Accounting
- Provide emission factors for carbon calculations
- Support estimation when primary data are unavailable
- Improve transparency in reporting
Scope 3 Emissions
- Fill data gaps across supply chains
- Support purchased goods and upstream activities
- Provide literature-based fallback values
A practical gap-filling solution
In real-world sustainability work, existing databases often do not provide complete coverage. This database acts as a literature-based gap-filling layer, transforming scattered academic data into a structured, usable dataset — enabling faster analysis, stronger justification, and more transparent decision-making.
