Brightcon 2026, hackathon and courses

Highlights

• AI-assisted workflow for extracting life cycle inventory data from
  secondary resources
• Use of open-source LLMs and fine-tuned
  classification models for document screening and information
  extraction
• Harmonization of heterogeneous inventories for emerging
  technologies with low technology readiness levels
• Transparent and reproducible workflow...

Highlights
1. AI integration in LCA introduces both new uncertainties and undetectable errors that differ fundamentally from traditional LCA uncertainty
2. A clear distinction is drawn between AI-induced uncertainty (stochastic, plausible outputs) and AI-induced error (hallucinated, factually wrong outputs)
3. A stage-based framework is proposed mapping AI-induced uncertainty and errors...

Large Language Models (LLMs) are increasingly used in professional services, including sustainability assessment. However, their reliability in Life Cycle Assessment (LCA) remains largely unexplored, particularly in the interpretation of results to provide mitigation advice and the treatment of circular value chains.

This short study benchmarks at least 3 LLMs (e.g., ChatGPT and Llama) as...

Highlights / Discussion Points

• A global physical uncertainty-aware oil Supply-Use Table (SUT) compiled through an AI-assisted scientific workflow.
• Human input was limited to prompting, review, and modelling judgement; AI generated the code.
• Demonstrates R/targets as a reproducible backbone for agentic data engineering.
• Discusses strengths, failure modes, memory management,...

Artificial intelligence (AI) is increasingly embedded in life cycle assessment (LCA) workflows, supporting inventory completion, literature synthesis, scenario analysis, and interpretation. Yet while AI is often framed as an efficiency gain for sustainability assessment, the environmental impacts of AI workflows themselves remain largely unexamined. Current AI-enabled LCA approaches tend to...

The LCA community has invested heavily in domain-specific AI: curated ontologies, hand-crafted matching algorithms, and specialised models trained on ecoinvent. We believe this approach, while well-intentioned, is following a familiar trajectory. Across field after field, the same pattern has emerged: domain-specific AI, carefully engineered with expert knowledge, is eventually overtaken by...