Price Variance in Hybrid-LCA Leads to Significant Uncertainty in Carbon Footprints

Abstract

Hybrid Life Cycle Assessment (HLCA) methods attempt to address the limitations regarding process coverage and resolution of the more traditional Process- and Input-Output Life Cycle Assessments (PLCA, IOLCA). Due to the use of different units, HLCA methods rely on commodity price information to convert the physical units used in process inventories to the monetary units commonly used in Input-Output models. However, prices for the same commodity can vary significantly between different supply chains, or even between various levels in the same supply chain. The resulting commodity price variance in turn leads to added uncertainty in the hybrid environmental footprint. In this paper we take international trading statistics from BACI/UN-COMTRADE to estimate the variance of commodity prices, and use these in an integrated HLCA model of the process database ecoinvent with the EE-MRIO database EXIOBASE. We show that geographical aggregation of PLCA processes is a significant driver in the price variance of their reference products. We analyse the effect of price variance on process carbon footprint intensities (CFIs) and find that the CFIs of hybridised processes show a median increase of 6–17% due to hybridisation, for two different double counting scenarios, and a median uncertainty of −2 to +4% due to price variance. Furthermore, we illustrate the effect of price variance on the carbon footprint uncertainty in a HLCA study of Swiss household consumption. Although the relative footprint increase due to hybridisation is small to moderate with 8–14% for two different double counting correction strategies, the uncertainty due to price variability of this contribution to the footprint is very high, with 95% confidence intervals of (−28, +90%) and (−23, +68%) relative to the median. The magnitude and high positive skewness of the uncertainty highlights the importance of taking price variance into account when performing hybrid LCA.

Where is my footprint located? Estimating the geographical variance of Hybrid-LCA footprints

“…[2021] discuss, prices for commodities vary between and within different supply chains as well as being subject to temporal variation. Moreover, Jakobs et al [2021] show, using price data from the BACI trade database [Gaulier and Zignago, 2010], that a price variance for reference products of processes is strongly linked to the regional scope of the process. With processes representing aggregated regions showing a larger price variation than 'single country' processes (see figure 2 of the mentioned study).…”

“…Moreover, as it captures potential correlations between commodity prices and environmental impacts of sectors in different regions, this may amplify the variance of the environmental impacts of the process considered. As the BACI trade database does not provide price data for all reference products, we follow Jakobs et al [2021] and use a combination of ecoinvent prices and proxy data to estimate price distributions for the reference those commodities and services without a BACI price. See Jakobs et al [2021, section 2.4 and table 2] for details on this process.…”

“…By averaging the supply chain structure and sectoral impacts over various regions, more detailed information is lost. Moreover, Jakobs et al [2021] showed that the lack of regional resolution increases the variance of the reference product prices, needed to link the monetary MRIO models to the physical supply chains of the PLCA data, leading to a higher variance in the footprint results.…”

“…In doing so, we preserve the information content contained in the full model by presenting outcome probability distributions rather then point estimates. Moreover, it also allows us to use regional price distributions in cases where we previously had to resort to regionally aggregated price distributions with a higher price variance [Jakobs et al, 2021]. We analyse the impact of geographic variance (differences between supply chain impacts of a product sector in different regions of the MRIO model) and price variance (the variability of commodity prices used to link the PLCA processes to the background MRIO model) on the footprint intensities (footprint per unit of process output), for both the Global Warming Potential 100 (GWP100) as well as the land occupation footprint.…”

“…To illustrate the impact of spatial variance and the use of regional prices on the uncertainty of a consumption footprint, we follow Jakobs et al [2021] and use the PLCA part of the model of Swiss household consumption of Froemelt et al [2018]. For this illustration case, we analyse the impact of geographical variance, price variance and the combined effect on the GWP and land-use footprint.…”