Unilever - Applying monetary valuation to predict Natural Capital impacts
Unilever assessed whether applying monetary valuation supported or altered the conclusions of a previous study using Land Use Change Improved – Life Cycle Assessment (LUCI-LCA)to predict the natural capital impacts of an expansion in the production of bioplastics.
We used the value transfer technique, which involves applying ‘proxy’ monetary values derived from other studies to quantify the environmental impacts and emissions of bioplastics expansion. We found a wide range of proxy values for greenhouse gas emissions, but a limited number of values relevant for specific geographic regions/landscape types for other impacts, such as biodiversity and water quality. It was difficult to draw robust and comprehensive conclusions from the valuation study and it did not provide additional insights to the earlier natural capital assessment using the LUCI-LCA method.
The study highlights the need to improve monetary valuation data, focusing on geographic relevance, specific ecosystem types and a wider range of impact categories.
- No, but aligns with the Protocol’s framework
- Corporate
- Product
- Project
- Monetary
- Brazil
- Upstream
- Consumer Goods
Key findings
We used the value transfer technique to avoid costly and time-consuming data collection. We consider this to be the only feasible way of mainstreaming monetary valuation to evaluate environmental impacts across extensive product value chains with varying spatial and temporal context.
Details of methods used to derive values (valuation studies or value transfer databases) are not always transparent or were found to represent different concepts of ‘value’/’cost’. In addition, data are heavily skewed to particular impact categories, e.g. GHG emissions, or particular ecosystems, e.g. tropical rainforests. Monetary values derived for some impact categories varied by an order of magnitude.
More comprehensive datasets are needed, along with clearer guidance on how to perform monetary valuation using transfer values. Monetization studies should be transparent in terms of underlying data limitations and it is important to present the data both by impact category and in an aggregated format, in order to identify trade-offs.