A method for calculating a land‐use change carbon footprint (LUC‐CFP) for agricultural commodities – applications to Brazilian beef and soy,Indonesian palm oil

The world's agricultural system has come under increasing scrutiny recently as an important driver of global climate change, creating a demand for indicators that estimate the climatic impacts of agricultural commodities. Such carbon footprints, however, have in most cases excluded emissions from land‐use change and the proposed methodologies for including this significant emissions source suffer from different shortcomings. Here, we propose a new methodology for calculating land‐use change carbon footprints for agricultural commodities and illustrate this methodology by applying it to three of the most prominent agricultural commodities driving tropical deforestation: Brazilian beef and soybeans, and Indonesian palm oil. We estimate land‐use change carbon footprints in 2010 to be 66 tCO₂/t meat (carcass weight) for Brazilian beef, 0.89 tCO₂/t for Brazilian soybeans, and 7.5 tCO₂/t for Indonesian palm oil, using a 10 year amortization period. The main advantage of the proposed methodology is its flexibility: it can be applied in a tiered approach, using detailed data where it is available while still allowing for estimation of footprints for a broad set of countries and agricultural commodities; it can be applied at different scales, estimating both national and subnational footprints; it can be adopted to account both for direct (proximate) and indirect drivers of land‐use change. It is argued that with an increasing commercialization and globalization of the drivers of land‐use change, the proposed carbon footprint methodology could help leverage the power needed to alter environmentally destructive land‐use practices within the global agricultural system by providing a tool for assessing the environmental impacts of production, thereby informing consumers about the impacts of consumption and incentivizing producers to become more environmentally responsible.