Trust Framework for AI-Based Carbon Reduction Activity Certification

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Demonstrates a novel integration of AI and blockchain for carbon certification, opening avenues for future empirical studies on trust in environmental claims.

🏢実務担当者:Provides a blueprint for building verifiable systems for individual carbon offsets, enhancing credibility of sustainability reports and carbon credit portfolios.

🏛政策担当者:Highlights how technology can strengthen carbon credit certification, potentially informing regulatory standards for digital verification in carbon markets.

The accelerating carbon emissions crisis demands verifiable, sustainable mechanisms for individual carbon reduction. Existing digital platforms often rely on manual or self-reported verification, which limits credibility, scalability, and resistance to falsification. This study presents an AI-based, participatory framework for carbon-reduction activity certification that integrates automated image analysis with blockchain-based data anchoring. The proposed system employs artificial intelligence to validate user-submitted evidence of eco-friendly actions and stores verification results on the PureChain blockchain as immutable cryptographic hashes, ensuring transparency, traceability, and tamper resistance. By eliminating operator dependency, the framework enhances the trustworthiness and accountability of environmental data certification. Experimental validation confirms its feasibility under real blockchain conditions, achieving privacy preservation and efficient verification. The results establish a secure and scalable foundation for reliable AI-driven environmental certification, advancing digital trust frameworks for sustainable carbon-reduction ecosystems.

• semanticscholar https://doi.org/10.1109/icaiic68212.2026.11454334first seen 2026-06-23 06:13:04