Emissions Data Management System Using Modeling Assumptions
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Solution Overview
Problem
Managing large volumes of emissions data for numerous entities is challenging due to storage and processing requirements, and collecting granular data raises security concerns, necessitating a more efficient approach to track and reduce emissions effectively.
Innovation Solution
A data management system that collects high-level data, uses modeling assumptions to generate emissions indicators, sorts them into categories, and displays them in a user-friendly interface, allowing for aggregation and drill-down capabilities, while reducing the need for granular data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If granular emissions data is collected for numerous entities, then measurement precision is improved, but data storage and processing requirements increase significantly
Solution Approach 1:
The patent extracts only the essential high-level data elements needed for emissions calculation while eliminating unnecessary granular details. Instead of collecting complete transactional data for every entity, the system identifies and collects only the critical parameters (entity type, activity level, emission factors) required to generate accurate emissions indicators, thereby reducing data volume while maintaining measurement precision.
Solution Approach 2:
The patent inverts the traditional approach by starting with high-level aggregate data and using modeling assumptions to derive emissions indicators, rather than collecting all granular data and processing it upward. This inversion allows the system to work with smaller data sets while still achieving accurate emissions measurements through statistical modeling and representative sampling.
2Measurement precision
If granular data collection is implemented, then emissions tracking accuracy is improved, but security concerns increase
Solution Approach 1:
The patent extracts only the minimum necessary data elements required for emissions calculation, eliminating sensitive granular information that would pose security risks. By collecting high-level data such as entity categories, activity levels, and emission factors rather than detailed operational data, the system maintains tracking accuracy while reducing security vulnerabilities associated with data collection.
3Productivity
If high level data is used with modeling assumptions, then data processing efficiency is improved, but data management complexity increases
Solution Approach 1:
The patent segments the emissions calculation process into distinct modules: data collection, modeling assumption application, indicator generation, and validation. By dividing the complex process into manageable segments with clear interfaces, the system improves processing efficiency while making the complexity more controllable and maintainable through modular architecture.
Solution Approach 2:
The patent manages complexity by parameterizing modeling assumptions, allowing the system to adjust the level of detail and complexity based on specific needs. Rather than hardcoding complex models, the system uses configurable parameters to control the sophistication of assumptions, enabling efficient processing for simple cases while accommodating complex scenarios when necessary.
4Loss of information
If emissions indicators are generated for multiple categories and entities, then comprehensive emissions tracking is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-categorizing entities and pre-establishing modeling assumptions before emissions calculation. Entities are classified into categories with associated emission factors and calculation methods in advance, so that when emissions indicators need to be generated, the system can quickly apply pre-configured models rather than determining categories and assumptions in real-time, significantly reducing processing time while maintaining comprehensive coverage.
Data Source
AI summary
A data management system that operates to receive high level data of categories for a corpus of entities, the high level data indicating characteristics of the corpus of entities that indicate emissions production of each entity of the corpus of entities. The data management system operates to select one or more modeling assumptions for the categories, the one or more modeling assumptions modeling low level consumption data based on the high level data and generate emissions indicators for each of the categories for each entity of the corpus of entities for a plurality of points in time based on the high level data and the one or more modeling assumptions. The data management system operates to sort the emissions indicators into a plurality of buckets based on the categories and generate data causing a computing device to display the emissions indicators sorted into the plurality of buckets.