Product-Centric Lifecycle Assessment Model for Emissions Certification
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Solution Overview
Problem
Conventional lifecycle assessment protocols fail to effectively differentiate and certify the environmental performance of specific animal, crop, energy, and material products, limiting their practical market application and consumer ability to choose products with lower emissions.
Innovation Solution
Development of model optimization techniques using machine learning algorithms that incorporate producer-specific data, such as management practices, energy production data, and genetic information, to quantify emissions from production systems, enabling the creation of product-centric models that estimate emissions across the entire lifecycle of products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lifecycle assessment protocols are used, then emissions can be quantified at aggregate levels, but the ability to differentiate and certify specific individual products is lost
Solution Approach 1:
The patent segments the aggregate emissions data into individual product-level emissions by creating separate assessment profiles for each product. This is achieved by dividing the overall lifecycle assessment into discrete product-specific modules that can be independently evaluated and certified, thereby enabling precise measurement of individual product emissions without requiring a complete overhaul of the assessment system.
Solution Approach 2:
The patent introduces a new dimension of product identification and tracking by assigning unique identifiers and creating detailed product profiles. This additional dimensional layer allows the system to differentiate between individual products while maintaining the existing aggregate assessment framework, effectively adding granularity without fundamentally changing the core assessment methodology.
2Loss of information
If product-centric models with detailed emissions data are implemented, then consumers can make informed choices, but data collection and processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing emissions data in standardized product profiles before market release. This allows emissions information to be prepared and validated in advance during the product development and registration phase, reducing the real-time data processing burden when consumers query the information. The system performs data preparation work beforehand, making consumer-facing queries simpler and faster.
Solution Approach 2:
The patent introduces an intermediary layer of standardized emission factors and calculation protocols that mediate between complex primary data sources and simplified consumer information displays. This intermediary layer processes and standardizes raw emissions data into uniform, easily comparable metrics, reducing the complexity of data handling while ensuring comprehensive emissions information is captured and presented.
Data Source
AI summary
Approaches provide for machine learning or training algorithms that apply modifications to models based on a type of data obtained, including, for example, including, for example, producer-specific management practice data, performance data, energy production data, among other such data, to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The emissions in certain embodiments can further enable the certification, label, or other transaction associated with emissions for individual animals, specifically identifiable crop products, specifically identifiable energy products, specifically identifiable materials, or other identifiable products.