Direct Solutions
Last Updated: 2025-10-06
Direct solutions avoid future emissions by replacing or taking market share from incumbents responsible for higher emissions. These solutions achieve emissions reduction through their direct integration into a lower emissions value chain. Most solutions in the Koi database are direct solutions, as they represent the most straightforward and measurable path to emissions reduction.
Solution Types
Direct solutions include both complete products and critical components:
- Products: Complete solutions that can be purchased and used to directly achieve GHG impact (e.g., EVs, heat pumps, sustainably produced food)
- Components: Critical parts of an overall solution that contribute significantly to its GHG impact (e.g., EV batteries, efficient motors, recycled materials)
Modeling Approach
Direct solutions are generally the most reliable for automated modeling because they have clear, measurable emissions reductions that can be directly attributed to the solution's function. The Koi Engine can typically find appropriate baselines for direct solutions by matching them to conventional systems they replace or displace.

Example solution value chain that includes the solution (a heat pump) directly
Unlike facilitating solutions, direct solutions require fewer assumptions about downstream effects and behavioral changes, making them more suitable for automated modeling with minimal human intervention.
Data Lake Matching for Direct Solutions
Direct solutions can be effectively modeled with the Koi Engine by identifying the relevant value chains and markets to align with records from the Data Lake.
Baseline GHG Intensity
Top match, click dropdown to see others
Baseline Market Size
Top match, click dropdown to see others
Solution GHG Intensity
Top match, click dropdown to see others
Market Capture
Based on IEA Net Zero goals for heat pump deployment
Allocation Considerations
How to appropriately allocate avoided emissions for direct solutions that are components is an unsettled science. Koi does not pre-segment any analysis data for allocation and instead provides the entire system savings as a model. Users can apply an adjustment factor using their allocation method of choice within a Collection.