A method for calculating a land-use change carbon footprint (LUC-CGP) 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.