Approaches to fill data gaps and evaluate process completeness in LCA—perspectives from solid waste management systems

Purpose: Large data amounts are required in an LCA, but often, site-specific data are missing and less representative surrogate data must be used to fill data gaps. No standardized rules exist on how to address data gaps and process completeness. We suggest a systematic evaluation of process completeness, identification of data gaps, and application of surrogate values to fill the gaps.

The study focus on foreground process data. Methods: A solid waste management (SWM) scenario was used to illustrate the suggested method. The expected input and output flows in a waste incineration model were identified based on legislation and expert judgment, after which process completeness scores were calculated and missing flows identified.

To illustrate the use of different types of surrogate data to fill data gaps, data gaps were selected for 16 different parameters in five SWM processes. We compared the global warming potential (GWP) from using surrogate data, and from leaving the gap, to identify the data gaps where representative surrogate data should be used.

Results and discussion: The completeness score for the material inputs to waste incineration was 78%, and the missing flows were auxiliary fuels and precipitation chemicals. The completeness score for air emissions were between 38 and 50% with and without expert judgment. If only greenhouse gases were considered (CO 2 , CH 4 , and N 2 O), the completeness score would be 67%.

Applying weighting factors according to the greenhouse gas contribution in the USA gave a completeness score of 94%. The system-wide data gaps, where representative surrogate data should be applied, were the CH 4 release from composting; electricity generation efficiency of incineration; recovery efficiencies at a material recovery facility; and composition of the plastic, metal, and paper fractions in the household waste; in these cases, leaving the gap changed the GWP results by > 5%.

Conclusions: Completeness evaluation should take into account the relevance and importance of flows; relevance depends on the considered life cycle impact methods and importance depends on the weighting of the different flows. The set of expected flows and evaluation of relevance and importance must be documented in a transparent manner.

The choice of surrogate values to fill data gaps depends on the availability of secondary data and on whether the data gap matters, i.e., significantly affects the LCA results. The suggested method can be used to properly document the identification of missing flows and to select and apply surrogate values to fill the data gaps.