Modelling uncertainty in hybrid life cycle assessment

Medientyp: E-Book

Titel: Modelling uncertainty in hybrid life cycle assessment

Beteiligte: Jakobs, Arthur [Verfasser:in]; Pauliuk, Stefan [Akademische:r Betreuer:in]; Pauliuk, Stefan [Sonstige Person, Familie und Körperschaft]; Wiedmann, Thomas [Sonstige Person, Familie und Körperschaft]

Körperschaft: Albert-Ludwigs-Universität Freiburg, Professur für Nachhaltiges Energie- und Stoffstrommanagement ; Albert-Ludwigs-Universität Freiburg, Fakultät für Umwelt und Natürliche Ressourcen

Erschienen: Freiburg: Universität, 2023

Umfang: Online-Ressource

Sprache: Englisch

DOI: 10.6094/UNIFR/234263

Identifikator:

Schlagwörter: Nachhaltigkeit ; Umweltbilanz ; Supply Chain Management ; Ökologischer Fußabdruck ; Input-Output-Analyse ; Unsicherheitsquantifizierung ; (local)doctoralThesis

Entstehung:

Hochschulschrift: Dissertation, Universität Freiburg, 2023

Anmerkungen:

Beschreibung: Abstract: Human activity exerts a continuous pressure on the environment and climate of planet earth. To ensure a habitable environment for current and future generations, our societies must re- duce these pressures. In order to tackle this major challenge, effective policies aimed at ensuring sustainable production and consumption are needed, and to guide the design of such policies, thorough assessment of supply chains and their environmental impacts is es- sential. Over the last decades, various methods for supply chain impact modelling have been developed, the two most prominent ones being process-based life cycle assessment (PLCA) and environmentally-extended input-output analysis (EE-IOA). PLCA studies are based on detailed process inventories that list all physical inputs and outputs of the processes, i.e. economic activities, under consideration, including to and from the biosphere. These inventories are then linked in a so called product system to model the supply chain of a product or service and calculate its life cycle impacts. The granular nature of PLCA data means that, although detail on individual supply chains can be provided to a certain extent, limited economic coverage of process inventory databases leads to incomplete estimates of the supply chain impacts, a problem also known as the truncation error. EE-IOA on the other hand, is an approach rooted in macroeconomics and models the flow of the monetary value of goods and services between different industry or product sec- tors, and attributes environmental impacts to them. EE-IOA or its multi-region equivalent EE-MRIO, are considered to be complete representations of the global economy, albeit con- taining information on highly aggregated product or industry sectors. Therefore, EE-MRIO analyses are unable to provide detailed information on specific products or services. Hybrid life cycle assessment (HLCA) methods attempt to address the limitations regard- ing process coverage and resolution of PLCA and EE-MRIO models by linking the two methods. However, as with all models, HLCA models have their strengths and weaknesses, and if they are to provide a credible option to guide environmental decision making, it is necessary to understand their limitations and the uncertainties they bring with them. This thesis aims to better understand the uncertainties in the ‘tiered HLCA’ model, a commonly used incarnation of HLCA, and the implication of these when using HLCA compared to the use of the more traditional PLCA and MRIO for consumption footprint studies. The first source of uncertainty in HLCA studied in this thesis is the price variability of commodities. HLCA methods have to 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. This com- modity price variance translates to increased uncertainty in the hybrid environmental foot- print, because in tiered hybrid models the inputs from the EE-(MR)IO model are directly proportional to the commodity price. In this thesis I take international trade statistics data from BACI/UN-COMTRADE to estimate the variance of commodity prices and assess their effect on the carbon footprint in a tiered HLCA model of ecoinvent and EXIOBASE (a commonly used LCA and IO database respectively). I find that the price variance of commodities leads to considerable uncertainty (up to 10%) in the carbon footprint for both individual processes, and a case study of Swiss household consumption. The analysis also shows that the price variance of reference products is driven in large part by the lack of geographical resolution of the process inventories, i.e. commodities are traded for different prices depending on where they are made. In the second part of this thesis we made use of this finding to improve the tiered model on two fronts. Up to now, tiered HLCA models aggregate regional supply chains of EE-MRIO data- bases to match the lower geographical resolution of process LCA databases. By doing so, they fail to fully exploit the valuable regional resolution available in modern EE-MRIO mod- els. In this thesis, I propose a method for sampling the various individual regions within the aggregated regional scope of LCA processes. This way, the regional variance is pre- served, maximising the information content of the hybrid LCA footprint results, by taking into account all available data instead of reducing them to average values, and making the un- certainty explicit. In addition, it allows for the use of regional price distributions for reference products, simultaneously improving the accuracy of the hybrid model. I find that the regional variance of supply chains, particular in combination with the regional differences in commodity price distributions, leads to large and highly positively skewed uncertainties of the truncation error estimate. In the analysis we look at both the carbon- and land use footprint and show that the mag- nitude of the footprint uncertainty strongly depends on both the product sector of the LCA process and which environmental impact is considered, with some sectors better covered by the process data than others. The results show the importance of regionalisation of process LCA databases, and its potential to significantly improve both the precision and accuracy of hybrid LCA models. Finally, this thesis analyses the potential accuracy improvement as well as the uncertainty implications of HLCA with respect to PLCA for monitoring environmental footprints. For this I selected a PLCA study of European food consumption and hybridised it using the refined tiered hybrid model including regional- and price variance, developed in this thesis. A comparison of the HLCA results to the PLCA only results shows that HLCA can improve the accuracy of the PLCA model, with truncation error corrections of up to 13(+16,−4)% depending on impact category. Although the truncation error estimates themselves have large uncertainties, the hybridisation did not cause a loss of precision in the overall result, i.e. an increase in the relative uncertainty. I identify possible reasons for this and discuss cases where this might be different. Overall, this thesis assesses two different but large sources of uncertainty in tiered HLCA models, and concludes that although these sources of uncertainty are considerable, HLCA has the potential to improve PLCA models without significantly increasing the uncertainty of the footprint in most cases. Cases where the uncertainty does increase with the hybrid model, should be analysed on a case by case basis to see if the accuracy gain outweighs the loss of precision. This leads to the conclusion that HLCA is highly relevant to aide policy design aimed at ensuring sustainable consumption and production

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