Medicine Carbon Footprint Classifier is a scientifically grounded suite of applications designed to bring transparency and standardisation to medicine carbon emissions data. Powered with our AI-driven methodology, MCF Classifier enables organisations to assess, compare, and improve the carbon impact of their medicines.
MCF Classifier includes three key products - MCF Data, MCF Reports, and MCF Formulary.
Comprehensive Carbon Emissions Data for Medicines
Access standardised, cradle-to-gate carbon emissions data for over 30,0000 medicine products. Available as a subscription service or as an extract for selected medicines.
Cradle-to-Gate Product Carbon Footprint Reports
Get third-party assured product carbon footprint reports compliant with GHG protocol, PAS 2050 and ISO 14067. Choose from one-page summaries, detailed reports, product comparisons, or bespoke insight reports.
Explore Medicine Carbon Footprint Ratings – Free
Search and compare per-dose medicine carbon footprint ratings for >4000 medicines in our user-friendly, online formulary. This free educational resource is designed for healthcare, pharma, and supply chain professionals.
WHY MCF CLASSIFIER?
Currently, standardised product-level medicine emissions data is very limited, yet medicines contribute ~25% of healthcare emissions.
We created MCF Classifier to fill this gap.
Using YewMaker's proprietary technology and AI-driven predictive modelling, MCF Classifier provides carbon emissions data for over 30,000 medicines, bringing product-level visibility to medicine emissions. This enables transparent reporting, consistent analyses, and standardised comparisons, empowering healthcare and the medicine supply chain to make more informed decisions on prioritisation and mitigation strategies.
MCF Method: An AI-driven Approach to Medicine Carbon Footprints
At the core of MCF Classifier is the MCF Method, an AI-driven process that uses data science and machine learning to predict the carbon emissions of medicines.
The method is grounded in Process Mass Intensity (PMI), an industry-standard metric that is strongly correlated with global warming potential.
By integrating empirical and modelled data MCF Classifier generates standardised cradle-to-gate carbon footprints for medicines, compliant with GHG Protocol, PAS and ISO standards.
To learn more about our methods, please read our open access paper.
Research Spotlight: Antibiotic Carbon Emissions
We used MCF Classifier to conduct an analysis of carbon emissions across therapeutic categories, which showed that antibiotics accounted for 15% of emissions in our dataset, despite representing only 2% of doses.
This analysis highlights how reducing unnecessary antibiotic prescriptions can support urgent goals on healthcare decarbonisation and antimicrobial stewardship.
The recommended 20% reduction could save 4,200 tonnes of CO2e per month—equivalent to removing 29,000 cars from the road.
The analyses also showed the potential of MCF Classifier data to support research and actions that deliver better healthcare and meet net zero commitments.
“Our research identifies antibiotics as a major contributor to medicine carbon emissions, and an area where interventions can support decarbonisation and antimicrobial stewardship”
Public Perception on Medicine Carbon Footprints: Key Insights
To better understand public sentiment on medicine carbon footprints we conducted quantitative and qualitative research.
Our findings highlight the growing demand for transparency and action on medicine-related emissions
We conducted an online survey, with responses from 1304 individuals across four countries, revealing remarkable consistency in their perspectives.
We next conducted in-depth interviews to better understand personal preferences for receiving information about the carbon footprint of medicines.
“Our research shows most people want more transparency and more action on medicine carbon emissions”