The Model of Resource Efficiency
Our resource efficiency database dates back to 2005, and covers over 2500 companies globally. We believe it is one of the most extensive repositories of environmental data in the world.
In the absence of consistent environmental reporting standards, Osmosis has pioneered a proprietary approach to the standardisation of unstructured corporate environmental data – enabling the creation of a Resource Efficiency factor.
What is Resource Efficiency?
Resource Efficiency is the effective use of scarce resources to generate greater economic value with decreased environmental impact. Osmosis defines it quantitatively as the amount of carbon emitted, water withdrawn, and waste generated relative to the economic value a company creates.
Why carbon, water and waste?
Our research demonstrates that a comprehensive approach to addressing the corporate environmental balance sheet – considering carbon, water and waste – will leave investors less exposed to a broader range of both current and future externality risks. A three factor approach also delivers a more reliable signal.
Why don’t we use third party data?
Third-party ESG data may offer convenience but falls short in delivering precision and integrity. It is therefore not suited for integration in an alpha seeking portfolio. Osmosis only uses raw, publically disclosed environmental data, standardised in-house using proprietary Osmosis sectors.
The Resource Efficient Nation
At Osmosis, we spend our time identifying companies that do more with less – those that generate more economic output per unit of resource consumed.
For the 2026 FIFA World Cup, we asked a different question: Which football nations are the most resource efficient?
Using the same systematic thinking that underpins our investment process, we analysed all 48 World Cup teams across five key factors – then ran 10,000 tournament simulations.
This campaign is for illustrative and discussion purposes only, and most importantly, for fun! It does not constitute investment advice, financial advice, or betting guidance. Past tournament results are not a reliable indicator of future results.
Model Insights
The Technology Hardware & Equipment Sector Split
A refined sector split enables more robust, like-for-like analysis.
