Bob Weihai He

Project Owner

AURORA: Decentralised AI for Carbon Credit Trust

Expert Rating

n/a

Proposal for BGI Nexus 1
Funding Request $50,000 USD
Funding Pools Beneficial AI Solutions
Total 2 Milestones

Overview

Harnessing decentralised AI and blockchain, AURORA redefines industrial decarbonisation by validating scalable, verifiable CCS carbon credits through real‐time insights, predictive analytics and transparent ESG reporting. Inspired by SingularityNET’s open AI marketplace, AURORA integrates secure, interoperable AI services to optimise carbon capture, storage and impact investment mechanisms. This innovative platform builds trust and transparency in climate finance, accelerating the global transition to sustainable, low‐carbon operations.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

Our proposal advances the BGI mission by employing decentralised AI and blockchain to boost transparency, trust, and scalability in carbon credit verification. Real‐time insights and predictive analytics enable close monitoring and optimisation of carbon capture and storage, accelerating decarbonisation. Robust ESG reporting and interoperable AI services further enhance accountability and collaboration, driving a sustainable, low‐carbon future.

Our Team

The AURORA Project is an initiative from Veritas-Kenja Labs (www.veritas-kenja-labs.com)- a joint-venture between Australia and Japan. Our team has a robust network of experts whose specialised knowledge in AI, blockchain, and industrial decarbonisation underpins every facet of the proposal. Our collective expertise ensures the project is developed and implemented with technical precision and strategic insight, advancing the mission to foster transparent and scalable carbon credit solutions.

AI services (New or Existing)

Real-Time Monitoring and Analytics Service

Type

New AI service

Purpose

To continuously monitor CCS operations, detect inefficiencies, and optimise performance in real time.

AI inputs

Real-time sensor data from industrial CCS facilities, including CO₂ emissions, capture efficiency, and operational parameters.

AI outputs

Live performance metrics, actionable insights, and alerts displayed on interactive dashboards.

Predictive Analytics Service

Type

New AI service

Purpose

To forecast future performance of carbon capture and storage systems and identify potential risks or operational issues before they occur

AI inputs

Historical CCS performance data combined with current operational metrics and sensor readings

AI outputs

Predictive reports, risk assessments, and early-warning notifications that support proactive decision-making

ESG Reporting and Risk Assessment Service

Type

New AI service

Purpose

To automate the aggregation and standardisation of ESG data, ensuring transparent and verifiable reporting while assessing operational risks.

AI inputs

Real-time operational and sensor data, historical performance records, and external ESG datasets

AI outputs

Standardised ESG reports, risk assessment scores, and comprehensive audit trails for regulatory compliance and stakeholder assurance.

Company Name (if applicable)

Veritas-Kenja Labs

The core problem we are aiming to solve

Current carbon credit systems are hindered by widespread mistrust, opacity, and inconsistent standards, resulting in unreliable ESG reporting and prevalent greenwashing. This lack of transparency and verifiability obstructs efforts to scale decarbonisation and implement effective carbon capture and storage. Our proposal tackles these issues by deploying decentralised AI and blockchain technologies that deliver real‐time insights, predictive analytics, and robust verification frameworks. By ensuring carbon credit data is fully transparent and verifiable, we aim to restore stakeholder confidence and accelerate the transition to a sustainable, low‐carbon future.

Our specific solution to this problem

Our solution confronts the challenges of opaque carbon credit systems by leveraging the SingularityNET Decentralised AI Platform, its AI Services, and the AI Marketplace. By integrating decentralised AI with blockchain technology, we ensure secure, transparent, and scalable verification of carbon credits. Industrial sensors and IoT devices continuously feed real‐time data into our system, which is then processed by advanced AI algorithms sourced from the SingularityNET ecosystem. These algorithms monitor carbon capture and storage operations, deliver predictive analytics to forecast performance, and identify inefficiencies in the decarbonisation process. The dedicated AI Services facilitate robust risk assessment, automate ESG reporting, and enable continuous improvement through iterative learning, ensuring that every carbon credit is underpinned by verifiable data. Meanwhile, the AI Marketplace offers a dynamic repository of specialised models and services, promoting seamless integration of cutting‐edge innovations as they emerge. This interconnected framework not only rebuilds stakeholder confidence by providing an immutable audit trail but also accelerates industrial decarbonisation by optimising operational efficiency. In doing so, our approach drives a sustainable, low‐carbon future while fostering industry‐wide accountability, collaboration, and long‐term environmental impact.

Project details

AURORA is an innovative project designed to transform industrial decarbonisation by addressing critical issues in current carbon credit systems. By integrating decentralised AI and blockchain technologies - specifically via the SingularityNET Decentralised AI Platform, its AI Services, and the AI Marketplace - AURORA creates a secure, transparent, and scalable framework for verifying and managing carbon credits.

Background and Problem Statement
Existing carbon credit systems suffer from mistrust, opacity, and inconsistent standards. Unreliable ESG reporting and prevalent greenwashing undermine confidence, while fragmented data sources hinder effective monitoring of carbon capture and storage (CCS) operations. Without verifiable, real‐time insights, industrial operators, investors, and regulators face challenges in assessing the true environmental impact of decarbonisation initiatives. A system is needed that delivers real-time, verifiable data, ensuring that each carbon credit accurately reflects sustainable practices.

The AURORA Solution
AURORA addresses these challenges by combining decentralised AI and blockchain technology. The solution centres on the SingularityNET Decentralised AI Platform, which enables the deployment and management of AI algorithms in a decentralised environment, eliminating single points of failure and ensuring resilience. The platform is enhanced by a suite of AI Services and access to a dynamic AI Marketplace, which together supply advanced analytics, risk assessment, and continuous improvement.

Data Acquisition and Integration: Industrial sensors and IoT devices capture real‐time data from CCS facilities - monitoring CO₂ emissions, capture efficiency, storage conditions, and operational parameters. This data is securely transmitted and immutably recorded on a blockchain ledger, creating a trusted audit trail that guarantees data integrity and traceability.
Decentralised AI Processing and Analytics: The captured data is processed by AI algorithms from the SingularityNET ecosystem. Real-time monitoring and predictive analytics assess CCS performance, detect inefficiencies, and forecast operational issues. These AI Services provide early warnings and actionable insights, allowing operators to optimise processes and improve decarbonisation efficiency.
Robust Verification and ESG Reporting: Blockchain technology underpins a multi-layered verification framework. Every transaction and data point is recorded on an immutable ledger, ensuring that all carbon credit information is tamper-proof. Simultaneously, AI-driven tools aggregate and standardise ESG data, generating reliable reports that validate true environmental impact and reduce greenwashing.
Dynamic AI Marketplace Integration: The SingularityNET AI Marketplace offers a diverse range of specialised models that can be seamlessly integrated into AURORA. As new AI Services are developed, they enhance the system’s verification and analytics capabilities. This ongoing innovation keeps the platform at the forefront of technological advancements, allowing it to adapt to evolving industry standards.
User Interface and Stakeholder Engagement: A secure, user-friendly dashboard provides stakeholders with real-time access to performance data, detailed analytics, and comprehensive ESG reports. Industrial operators can monitor CCS operations and receive actionable insights, while investors and regulators benefit from transparent, verifiable data that supports compliance and informed decision-making. The interface also enables stakeholders to access additional AI Services from the SingularityNET Marketplace.

Benefits and Impact

Enhanced Transparency and Trust: Blockchain and decentralised AI create an immutable, auditable record, rebuilding confidence in carbon credit systems by ensuring that every credit is backed by verifiable data.
Optimised Industrial Operations: Real-time monitoring and predictive analytics enable rapid identification and resolution of inefficiencies, leading to improved CCS performance and lower emissions.
Standardised ESG Reporting: Automated reporting tools ensure reliable and consistent ESG data, crucial for regulatory compliance and investor assurance.
Scalability and Continuous Innovation: The modular nature of the SingularityNET platform and its dynamic AI Marketplace allow the system to scale effortlessly and integrate new technologies as they emerge.
Risk Mitigation: Advanced risk assessments and early-warning analytics reduce the likelihood of fraudulent or substandard carbon credits, protecting stakeholder investments.

Conclusion
Project AURORA leverages the SingularityNET Decentralised AI Platform, AI Services, and the AI Marketplace to revolutionise carbon credit verification and industrial decarbonisation. By integrating real-time data acquisition, advanced AI analytics, and blockchain-based verification into a cohesive system, AURORA delivers a transparent, verifiable, and scalable solution that rebuilds stakeholder confidence and drives sustainable environmental impact. This integrated approach accelerates the transition to a low‐carbon future and sets a new standard for accountability and efficiency in the fight against climate change.

Existing resources

Yes, we will be leveraging a range of existing technologies (e.g. IoT, digital twins) and resources that do not require additional funding. We already have robust support from established CCS projects, which provide valuable data and operational insights for our platform. Additionally, we are collaborating with leading universities that contribute research expertise and innovative technologies. Importantly, we benefit from government support from both Australia and Japan, which helps facilitate access to cutting-edge infrastructure and resources necessary to implement and scale our solution.

Links and references

https://drive.google.com/file/d/1P3qRwh0Jb1P-MuSAs-4QXJ2C6zMpbhIf/view?usp=sharing

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Activity Summary

Milestones

2

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

1

Total People

Total Milestones
2
Total Budget
$50,000_USD
Last Updated
23 Feb 2025

Milestone 1 - Architecture and Design

Description

Define a flexible, secure, and scalable system architecture for AURORA. This phase considers simulated CCS sensor data with the SingularityNET Decentralised AI Platform and blockchain verification to enable transparent ESG reporting. It involves designing a modular framework with three layers: data acquisition (from CCS sensors/IoT devices), AI processing (for real-time monitoring and predictive analytics via the SingularityNET ecosystem), and a blockchain layer for immutable data logging.

Deliverables

A high-level architectural design document that includes: (1) Detailed blueprints and data flow diagrams outlining integration between CCS sensors, the AI processing module, and the blockchain ledger. (2) A proof-of-concept diagram that visually represents the end-to-end system architecture and modular design.

Budget

$20,000 USD

Success Criterion

(1) Clear demonstration of secure data capture, efficient AI processing, and reliable blockchain verification in the design. (2) Positive external reviews confirming that the architecture meets technical, security, and regulatory requirements.

Milestone 2 - Demo and Proof of Concept (PoC)

Description

Develop a prototype to validate AURORA’s core functionalities. This PoC will integrate real-time data acquisition from CCS sensors with AI services from the SingularityNET platform for live monitoring and predictive analytics, and it will use blockchain to securely log all data for transparent ESG reporting.

Deliverables

A demo comprising: (1) A live dashboard that visualises real-time CCS data such as CO₂ emissions, capture efficiency, and storage conditions. (2) AI modules that process data for real-time monitoring, predictive analytics, and risk assessment using services from the SingularityNET ecosystem.

Budget

$30,000 USD

Success Criterion

(1) The prototype accurately captures and displays live sensor data and provides timely alerts on operational anomalies. (2) Positive feedback from pilot tests and stakeholder reviews demonstrating that the system meets performance, security, and scalability requirements while remaining adaptable to future needs.

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.

Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).

Feasibility (secondary)

This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

Viability (secondary)

This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary)

Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?

Example:

Creating a translation service from, say Spanish to English might be possible, but it's questionable if such a service would be able to get a significant share of the market

Usefulness (secondary)

This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”

For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

Viability (secondary)

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary)

Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary)

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Bob Weihai He

Project Owner

Manages project activities and reporting while coordinating internal tech teams and external collaborators, ensuring strategic alignment, technical execution, and effective stakeholder communication.

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