Spatiotemporal Tools for Regional Low-Carbon Development: Linking LCA and GIS to Assess Clusters of GHG Emissions from Cocoa Farming in Peru

Abstract

Understanding spatial and temporal variations of greenhouse gas (GHG) emissions at local level is important for both companies in the agri-food sector working to improve sustainability in their supply chains and local governments following a low-carbon development pathway. For this reason, it is necessary to count with methodologies that do not only estimate GHG emissions, but provide analytics about their location and temporal variations. Factors such as spatial distribution of farms, variations on the requirement of inputs depending on the age of the farms, as well as transportation distances for the main and complementary products, should be assessed at farm level. To accomplish this, spatial and temporal analytics can be incorporated into life cycle assessment (LCA) by joining it with geographic information systems (GIS). This paper explores how spatial statistics tools can be applied to identify spatial and temporal trends in GHG emission results obtained from an LCA conducted on cocoa farming in the region of San Martin in Peru. Results indicate that it is possible to identify temporal and spatial trends of statistically significant clusters of farms with high GHG emissions (hot spot analysis).