Emissions Data Management via High-Level Modeling
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Solution Overview
Problem
Managing large volumes of granular emissions data for numerous entities is resource-intensive and challenging due to storage and processing requirements, and entities may be hesitant to provide detailed data for security reasons, making it difficult to generate accurate and efficient emissions indicators.
Innovation Solution
Collecting high-level data instead of granular data and using modeling assumptions to generate emissions indicators, which reduces data storage and processing needs while maintaining accuracy by simulating low-level data based on high-level information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If granular emissions data is collected for numerous entities, then measurement precision is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent segments the data collection approach by dividing entities into groups and collecting high-level summary data for each group rather than individual granular data for every entity. This segmentation reduces the overall data volume while maintaining sufficient precision for emissions indicators through statistical aggregation and modeling.
Solution Approach 2:
The patent uses modeling assumptions to create synthetic representations (copies) of low-level detailed data based on available high-level data. These modeled data copies enable accurate emissions calculations without requiring actual granular data collection, thereby reducing system complexity and resource requirements.
2Measurement precision
If granular emissions data is collected for numerous entities, then measurement precision is improved, but loss of time and processing efficiency worsen
Solution Approach 1:
The patent performs preliminary aggregation of data into high-level summaries and pre-establishes modeling assumptions before detailed emissions calculations are needed. This preliminary processing reduces the computational burden during actual emissions indicator generation, significantly decreasing processing time while maintaining accuracy.
Solution Approach 2:
By creating modeled copies of detailed data from high-level summaries, the system avoids time-consuming collection and processing of actual granular data for every entity, thereby accelerating the emissions indicator generation process.
3Device complexity
If high-level data is collected instead of granular data, then device complexity is reduced, but measurement precision may worsen
Solution Approach 1:
The patent uses modeling assumptions to generate synthetic detailed data (copies) from high-level summary data. These modeled data copies restore the necessary detail and precision for accurate emissions calculations while maintaining the simplicity of collecting only high-level data, thus resolving the precision concern.
Solution Approach 2:
The patent transforms high-level aggregated data into detailed-level information through parameter-based modeling assumptions. By changing the level of detail through mathematical modeling rather than data collection, the system maintains measurement precision without requiring granular data input.
4Productivity
If high-level data is collected instead of granular data, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system creates modeled data copies from high-level summaries, enabling fast processing while maintaining the precision needed for accurate emissions indicators. The copying approach allows rapid generation of detailed emissions data without actual granular data collection.
Solution Approach 2:
The patent uses parameter-based modeling to transform high-level data into detailed emissions calculations, achieving both high productivity through simplified data collection and high precision through mathematical modeling of the transformation parameters.
Data Source
AI summary
A system includes one or more processors and one or more memory devices storing instructions thereon that, when executed by the one or more processors, cause one or more processors to receive high-level sustainability data associated with an entity; generate a plurality of sustainability indicators for a plurality of categories for the entity based on the high-level sustainability data and one or more modeling assumptions associated with the entity; aggregate the plurality of sustainability indicators into a single sustainability indicator; generate a graphical user interface including the single sustainability indicator; and cause one or more devices to perform an action to improve the single sustainability indicator.