(237h) Emissai: LLM-Powered Analysis of Carbon Footprint

The urgent need to address rising global CO₂ emissions 36.8 billion metric tons in 2023, driven significantly by industrial activities has intensified demands for scalable solutions to achieve net-zero targets aligned with the Paris Agreement’s 1.5°C threshold. Our work introduces EmissAI, a novel system leveraging large language models (LLMs) to automate carbon footprint analysis and generate actionable reduction strategies. Traditional life cycle assessments often underestimate emissions by up to 29% due to static data and incomplete system boundaries, highlighting a critical gap in dynamic, data-driven decarbonization tools. EmissAI processes unstructured textual data from sources like the Carbon Catalogue, constructing a dynamic knowledge base to identify emission patterns and propose targeted interventions. By training on product descriptions and sustainability literature, the system reduces analysis time to minutes while matching human expert accuracy, addressing a bottleneck that risks stalling global decarbonization efforts. We detail EmissAI’s architecture, including LLM integration with environmental datasets, methodology for extracting insights, and evaluation of industry-specific applications. The results demonstrate its potential to bridge data overload and actionable sustainability, offering a pathway to mitigate the 11-fold increase in humanity’s carbon footprint since 1961. As global ecosystems face irreversible threats, EmissAI emerges as a timely tool to empower industries and policymakers in transitioning toward a sustainable future.

References:

Intergovernmental Panel on Climate Change (IPCC). (2023). Climate change 2023: Synthesis report. Retrieved from https://www.ipcc.ch/report/ar6/syr/

Ritchie, H., & Roser, M. (2020). CO₂ and greenhouse gas emissions. Our World in Data. Retrieved from https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions