A scale-consistent land use impact indicator for Life Cycle Assessment and Environmentally-Extended Input-Output Analysis

Objective: Recent regionalized Life Cycle Impact Assessment (LCIA) characterization models (e.g. for biodiversity), originally applied at product level, have been adapted to Environmentally-Extended Input-Output Analysis... [ view full abstract ]

Objective: Recent regionalized Life Cycle Impact Assessment (LCIA) characterization models (e.g. for biodiversity), originally applied at product level, have been adapted to Environmentally-Extended Input-Output Analysis (EE-IOA) to estimate impacts of consumption and production at product type or sectorial level. As LCIA models become increasingly sophisticated, it is crucial to ensure that they are applicable for functional units at several scales, avoiding traps such as double-counting of product-level impacts and linearity of aggregation for bundles of products. In this work we propose a new characterization model for land use/land use change (LU/LUC) impacts valid for LCIA and EE-IOA. 

Method: We used results of simulations performed using the RothC model to assess soil organic carbon (SOC) change due to LU/LUC. Simulations were regionalized, providing a different dynamic SOC accumulation or loss curve for each combination of climate region, LU and soil type. LCIA characterization factors (CFs) were calculated as the steady-state SOC differential between each new LU and the continuation of the present LU. This differential was also assigned to the products corresponding to each LU class in the EE-IOA table in corresponding regions. Some aggregation of CFs was required to match sectors in the EE-IOA table. For example, different CFs were calculated for the same crops (depending on soil cover practices, irrigation, etc.), but economic data does not discriminate operations and production systems. At the same time, some classes had to be disaggregated (e.g. extensive and intensive meat production).

Results: We calculate regionalized CFs for 30 LU agricultural and natural classes (grasslands and forests). We demonstrate that the sum all SOC changes due to production and consumption of a unitary amount of each crop in each region is equal to the global SOC changes in the same sector. We coupled SOC changes due to land use flows and economic transactions to show the most important sectors that drive SOC depletion. Meat production, typically identified as one of the most impactful sectors for categories such as biodiversity loss or climate change, performs better than other sectors if produced extensively due to high SOC sequestration potential. Joint modelling of LU classes and production methods is crucial to obtain accurate estimations of the drivers of SOC loss.

Conclusion: This study obtained LCIA CFs obtained using a method consistent with EE-IOA. There are important limitations when matching available databases for each method, which require data adaptations that introduce uncertainty into the impact assessment model.