Life cycle assessment needs predictive spatial modelling for biodiversity and ecosystem services

Abstract

International corporations in an increasingly globalized economy exert a major influence on the planet's land use and resources through their product design and material sourcing decisions. Many companies use life cycle assessment (LCA) to evaluate their sustainability, yet commonly-used LCA methodologies lack the spatial resolution and predictive ecological information to reveal key impacts on climate, water and biodiversity. We present advances for LCA that integrate spatially explicit modelling of land change and ecosystem services in a Land-Use Change Improved (LUCI)-LCA. Comparing increased demand for bioplastics derived from two alternative feedstock-location scenarios for maize and sugarcane, we find that the LUCI-LCA approach yields results opposite to those of standard LCA for greenhouse gas emissions and water consumption, and of different magnitudes for soil erosion and biodiversity. This approach highlights the importance of including information about where and how land-use change and related impacts will occur in supply chain and innovation decisions.

Figure 1. Conceptual framework for LUCI-LCA.

Two key innovations to standard LCA are shown in orange; the elements of the life cycle that are modified by these innovations are highlighted in yellow. First, land-change modelling (LCM) based on logistic regression with climatic and soil suitability is used to forecast plausible future agricultural expansion (including intensification), rather than attributing future footprint based on current status as done in standard attributional LCA. Second, land-use change (LUC) is translated to impacts using spatially explicit models for biodiversity (MSA) and ecosystem services (InVEST), rather than assuming linear relationships between impacts and crop production as in standard LCA. All changes to inventories and impacts occur within the agriculture stage of the life cycle, shown in expanded form (in the orange box) above the full standard LCA schematic in grey.

Figure 2. Relative differences in impacts of HDPE produced from sugarcane versus maize using LUCI-LCA and standard LCA.

Relative differences are displayed as the percent by which sugarcane-based HDPE has higher (positive values) or lower (negative values) impact than maize-based HDPE for the same production level. Red bars are calculated using standard LCA methodology (constant for all production scenarios); yellow bars using LUCI-LCA (for scenario 3=321,000 tonnes HDPE).

Figure 3. Non-linear impacts from increasing scale of production in LUCI-LCA.

Three scenarios of production impacts per tonne of HDPE production, on (a) global warming potential, (b) water consumption, (c) eutrophication potential, (d) erosion potential and (e) biodiversity damage potential, for sugarcane (in green) and maize (in blue). Solid lines show change in impacts with production volume according to LUCI-LCA. Standard LCA estimates are provided for reference (dotted lines), showing constant per tonne impacts regardless of production amounts.

Figure 4. Comparison of impacts resulting from different assumptions of land-use change under increased bio-feedstock demand.

LUCI-LCA impacts of HDPE production on (a) global warming potential, (b) eutrophication potential, (c) erosion potential and (d) biodiversity damage potential. Impacts modelled using the logistic LCM (in dark yellow) and proximity-based LCM (in light yellow) are presented per tonne of HDPE for production scenario 3 (321,000 tonnes). LUCI-LCA impacts corresponding to actual LUC (in orange) are the impacts modelled in LUCI-LCA for agricultural expansion that occurred between 2007 and 2012 in both regions (Iowa for maize and Mato Grosso for sugarcane), converted to per tonne of HDPE production possible if that amount of additional agricultural acreage were used for HDPE production from the respective bio-feedstocks. Standard LCA results (in red) are shown as the per-tonne impact for every scenario (all identical, as shown in Fig. 2). Error bars show the high and low estimates of sensitivity analysis resulting from uncertainty in InVEST parameter and crop yields (Supplementary Methods and Supplementary Table 29). LUC, land-use change.