DLT Carbon Credit Tokenization with AI Verification
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Solution Overview
Problem
Existing systems face challenges in accurately tracking and validating carbon credits, often relying on estimated emissions and lacking real-time data, which complicates the monetization and trading of carbon credits.
Innovation Solution
The integration of smart meter technology with distributed ledger systems (DLTs) for real-time energy production and usage data, utilizing AI and ML to verify and validate carbon credits, and tokenizing this data for secure and transparent trading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional emission estimation methods are used, then the system complexity is low, but the measurement precision and reliability of carbon credit data are insufficient
Solution Approach 1:
The system segments the carbon credit verification process into multiple independent components: smart meters for energy measurement, blockchain for immutable recording, AI algorithms for validation, and third-party data sources for cross-verification. Each component handles a specific aspect of verification, improving overall precision while distributing complexity across specialized modules rather than requiring a single complex system.
Solution Approach 2:
The patent introduces multiple intermediary layers between energy production and carbon credit validation: smart meters as intermediaries for accurate energy measurement, blockchain as an intermediary for transparent and immutable data recording, and AI validation algorithms as intermediaries that cross-check data against expected values and third-party sources. These intermediaries improve measurement precision without requiring direct complex monitoring of all emission parameters.
2Productivity
If real-time smart meter data is collected and validated, then the productivity and reliability of carbon credit tracking improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary validation actions by pre-establishing expected energy production values based on historical data, equipment specifications, and third-party forecasts. AI algorithms continuously compare real-time smart meter data against these pre-calculated expectations, enabling rapid validation decisions without complex real-time analysis of all possible parameters. This preliminary preparation significantly improves tracking productivity while reducing real-time processing complexity.
Solution Approach 2:
The patent implements feedback mechanisms where AI validation algorithms continuously compare real-time energy data against expected values and adjust validation criteria accordingly. Third-party data sources provide ongoing feedback that refines the validation models. This feedback loop enables the system to maintain high productivity by automatically adapting to changing conditions without requiring complex manual intervention or reconfiguration of the validation process.
3Reliability
If multiple validation layers and third-party data sources are integrated, then the reliability of carbon credit verification improves, but the ease of operation and system implementation becomes more difficult
Solution Approach 1:
The system performs self-service validation by automatically comparing real-time smart meter data against expected values calculated from equipment specifications and historical patterns. AI algorithms autonomously validate carbon credit claims without requiring manual verification or complex user intervention. The blockchain automatically records and immutably stores validated transactions, eliminating the need for manual auditing. This self-service approach significantly improves reliability through consistent, objective validation while dramatically simplifying operation for users who only need to initiate transactions rather than manage complex validation processes.
Data Source
AI summary
Disclosed embodiments include methods and computer-implemented distributed ledger technology (“DLT”) systems based at least in part upon energy usage and savings. Disclosed embodiments include instructions to cause at least one server device and related data processing and storage apparatus to operate over a peer-to-peer network to provide a system having a carbon tracker module that records a transaction comprising an amount of energy incoming from a power grid or an amount of energy savings from energy savings equipment, along with the environmental and other attributes of such energy, wherein the transaction includes identifying data and the carbon tracker module sends such data to a DLT network after verification and validation utilizing AI and/or ML, and wherein the DLT network comprises a plurality of nodes that execute a software verification algorithm that includes a cryptographic hash value based at least in part upon transaction identifying data. Disclosed embodiments also include a predictive analytics module to compare the incoming energy savings or usage against the amount of energy savings or usage from the energy equipment, a timer module to monitor the carbon tracker module through a defined term, and an invoice module for trading carbon credits through the defined term.