CO2 Emission Estimation Using Proxy Data and Industry Models
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Solution Overview
Problem
Incomplete or non-existent CO2 emission data poses challenges in rating companies, as existing technologies lack effective methods to estimate and provide valuable ratings in such circumstances.
Innovation Solution
A method and system utilizing multiple algorithms and modules to estimate CO2 emissions, combining qualitative and quantitative data, and providing estimated values through a user interface, even when complete data is not available, by applying industry and sector-specific models and correcting for errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete CO2 emission data is required for rating companies, then rating accuracy is improved, but many companies cannot be rated due to data unavailability
Solution Approach 1:
The patent introduces an intermediary estimation system that acts as a mediator between companies with incomplete data and the rating system. The system uses proxy variables (energy consumption, production volume, industry benchmarks) as intermediaries to infer CO2 emissions when direct data is unavailable, enabling rating continuity without sacrificing accuracy entirely.
Solution Approach 2:
The system performs preliminary actions by pre-establishing estimation models and benchmark data before actual rating occurs. Industry-specific emission factors and historical data are prepared in advance, allowing rapid estimation when company data is missing, thus preventing rating gaps without requiring complete real-time data.
2Adaptability or versatility
If estimation algorithms are used to fill missing data, then rating coverage is improved, but estimation accuracy may be compromised
Solution Approach 1:
The patent applies local quality by tailoring estimation methods to specific industries and company types. Different algorithms are selected based on the company's sector (e.g., manufacturing vs. services), and industry-specific emission factors are applied locally rather than using a one-size-fits-all approach, improving estimation accuracy for each local context while maintaining broad coverage.
Solution Approach 2:
The system dynamically changes estimation parameters based on available data characteristics. When certain data points are missing, the system adjusts which proxy variables to use and which estimation models to apply, adapting the calculation parameters to the specific data gaps and company profile to maintain accuracy despite incomplete information.
3Reliability
If multiple estimation models are applied, then estimation reliability is improved, but system complexity increases
Solution Approach 1:
The patent segments the estimation system into distinct, manageable modules: data collection module, model selection module, calculation module, and validation module. Each segment handles a specific aspect of estimation, making the overall complex system more manageable and maintainable while improving reliability through specialized processing at each stage.
Solution Approach 2:
The system applies partial action by using multiple estimation models selectively rather than universally. Not all models are applied to every company; the system chooses the appropriate subset of models based on data availability and company characteristics, reducing unnecessary computational complexity while maintaining high reliability through model diversity where needed.
Data Source
AI summary
Estimations of carbon dioxide (“CO2”) emission of an entity upon the condition of incomplete or missing data uses one or more algorithms implemented in a machine having a processor and a memory and data concerning the entity. The data is applied to an algorithm implemented as code executable in the processor. The algorithm produces a result that comprises an estimate of the CO2 emission of the entity. The CO2 emission estimate can be output to a user, and the underlying formula and data can inspected and optionally modified by users with suitable permissions. The CO2 emission estimate can be applied as a factor in a formula to compute a rating for the entity which can be output from the machine. Error estimates associated with the data used by the algorithm can be generated to provide improved estimates.