Work package 0

Motivation

Given the interdependencies between work packages, effective project management and open communication channels are essential.

Approach

In this work package, PSI will chair online meetings every other month, and coordinate annual status meetings. Special attention will be paid to compatibility in data formats, as well as technical support throughout the project for data exchange.

PSI will also be responsible for coordinating dissemination activities, including maintaining the project website, editing the final report, and organizing a stakeholder workshop at the conclusion of the project.

Work package 1

Motivation

Understanding global supply chains of our economy and their environmental impacts is an important step towards a sustainable economy. The two widely used methodologies to study supply chains are the process-based Life Cycle Assessment (LCA) and Environmentally-Extended Multi-Regional-Input-Output models (EE-MRIO). Process-based LCA links individual processes to create a highly detailed supply chain, however, because of limited data availability, a system boundary needs to be defined where processes outside of this boundary are not being included in the supply chain. The exclusion of these processes, however, leads to an underestimation of the environmental impacts of the supply chain under study, a problem know as the truncation error. EE-MRIO analysis on the other hand, uses macro-economic data to model the supply chains in a process that provides high completeness, but at the cost of resolution.

One could describe the difference between process-based LCA and EE-MRIO analysis as the difference between precision and accuracy. However, to design effective policies that help to mitigate the environmental impacts of our economy, we need a both detailed and complete picture of the supply chains.

Hybrid-LCA, an analysis in which both process- and MRIO-data is used, is considered to be an effective means to mitigate the truncation errors in process-based LCA, and therefore produces more accurate footprint results. However, in conventional hybrid-LCA, the inclusion of MRIO data into the supply chain, leads to added uncertainty or noise due to the low product and sector resolution of this data, which in turn reduces the precision of the results. An ideal hybrid-LCA methodology therefore, would not only be able to add accuracy, but also capture the added uncertainty so that we know how precise our model is.

Goal

Work Package 1 has the goal to develop a such statistically based method for the hybridisation of LCA and MRIO data.

Approach

We seek to employ the principle of maximum entropy, which provides a means to find the least biased estimation of a quantity in an indeterminate system, to reconcile information available at different levels of resolution and on different layers (monetary and physical). The goal is to, unlike existing footprint indicators derived from hybrid methods, enable us to quantify the uncertainty of the footprints calculated and give quantitative information on where in the supply chain resolution needs to be increased and data uncertainty decreased to improve the accuracy of the footprint.

The result of this work package will be an open source Python package to hybridise the Ecoinvent LCA database with the EXIOBASE MRIO data, although the methodology will not limited to any particular data or format but aims to provide a statistical basis for any large scale hybridisation project on LCA and MRIO data. The output also service as an input for work packages 3,4 and 5.

Work package 2

Goal

In order to do a complete sustainability assessment, it is necessary to include social dimension into life cycle analysis. This is the primary goal of work package 2.

Approach

The ecoinvent centre will adapt and add quantitative social indicator data from the Social Hotspots Database to the existing process inventory datasets. Following usual ecoinvent practice, social indicators will be independently reviewed for consistency and completeness. In addition, the existing process data of ecoinvent will be tested for their appropriateness and expanded upon if necessary. Social impacts may require higher data quality and accuracy than environmental assessments in some parts of the database, and the database will there be adapted as necessary. After the integration into the ecoinvent database, the same verified and open methodology will be used to map social indicators onto the EXIOBASE database.

Work package 3

Motivation

Secure supply chains of goods and services consumed within the Swiss economy and society are essential to guarantee a sustainable development in key sectors such as food and clothing, information and commu-nication, energy provision and storage, and mobility (Richard and Wachter 2012). The supply chain of ma-terials used in products within these sectors involves both local and global actors (Nuss et al. 2016). A high number of parties within the supply chain increases the potential of different types of supply disrup-tions along the product life cycle (Figure 1). Criticality assessment, enhanced by the development of sev-eral methodologies in the past few years (European Commission 2010; Graedel and Reck 2016; NRC 2008), allows for evaluating the potential of supply disruptions in current and future supply scenarios by applying supply risk indicators (Achzet and Helbig 2013; Dewulf et al. 2016; Knoeri et al. 2013; Roelich et al. 2014). The assessment of one type of supply disruption, the mineral depletion, is established within Life Cycle Assessment by the application of various Life Cycle Impact Assessment (LCIA) indicators (Klinglmair et al. 2013; Wäger et al. 2015). First attempts have also been made to integrate other risk as-pects addressed in criticality assessment into LCA respectively the more holistic Life Cycle Sustainability Assessment (LCSA) (Bach et al. 2016; Schneider et al. 2013; Sonnemann et al. 2015).

Gaps

Mostly risks occurring at early supply stages are assessed in existing criticality assessment approaches, whereas pertinent supply risk indicators are missing that are suitable for an integrated assessment along the supply chain based on LCA methodology (Graedel and Reck 2016; Schrijvers et al. 2019). A compre-hensive quantification of the resource dependency of the Swiss economy requires the application of said indicators to a data-rich description of Swiss materials flows. For an improvement of the sustainability of Swiss supply chains, supply risks within current and future supply scenarios have to be addressed by elim-inating existing data gaps as good as possible.

Goal

The main goals of WP 3 consist in (i) evaluating of quantification opportunities for indicators suitable for a comprehensive supply risk assessment within LCSA, setup of an appropriate integration framework (Fig-ure 2) and prioritization of pertinent supply risk indicators (ii) providing the data to quantify priority sup-ply risk indicators along existing supply chains relevant for the Swiss economy and support their imple-mentation in a merged ecoinvent, EXIOBASE and Social Hotspot database, and (iii) evaluating the suitabil-ity of the supply risk indicators and the merged database to address future supply scenarios.

Approach

Step 1: Review
Review of existing criticality assessment approaches and related indicators for supply risk assessment and identification of indicator-specific data requirements along the supply chain.

Step 2: Analysis
Analysis of the application and development of commonly used supply risk indicators regarding an assess-ment along the supply chain and investigation of the applicability of supply risk indicators in the context of databases developed in the framework of LCA methodology.

Step 3: Evaluation
Prioritization and adaption of indicators, suitable to assess supply risks along the supply chain, for an inte-gration within LCSA considering limitations in data availability and acquisition as well as methodological boundary conditions of the LCSA framework.

Step 4: Modelling
Based on the priority indicators, development of a methodology for supply risk assessment integrated into an LCSA frame that quantifies major existing supply risks along the supply chains for components and products of key sectors within the Swiss economy and society.

Step 5: Approach
Evaluation of the priority supply risk indicators applied within the developed methodology and the merged database regarding their suitability to address future supply scenarios.

References

Achzet, Benjamin and Helbig, Christoph (2013), 'How to evaluate raw material supply risks—an overview', Resources Policy, 38 (4), 435-47.

Bach, Vanessa, et al. (2016), 'Integrated method to assess resource efficiency – ESSENZ', Journal of Cleaner Production, 137, 118-30.

Dewulf, Jo, et al. (2016), 'Criticality on the international scene: Quo vadis?', Resources Policy, 50, 169-76.

European Commission (2010), 'Critical raw materials for the EU - Report of the Ad-hoc Working Group on defining critical raw materials'.

Graedel, T. E. and Reck, Barbara K. (2016), 'Six Years of Criticality Assessments: What Have We Learned So Far?', Journal of Industrial Ecology, 20 (4), 692-99.

Klinglmair, Manfred, Sala, Serenella, and Brandão, Miguel (2013), 'Assessing resource depletion in LCA: a review of methods and methodological issues', The International Journal of Life Cycle Assessment, 19 (3), 580-92.

Knoeri, C., et al. (2013), 'Towards a dynamic assessment of raw materials criticality: linking agent-based demand--with material flow supply modelling approaches', Sci Total Environ, 461-462, 808-12.

NRC (2008), 'Minerals, Critical Minerals, and the US Economy', National Research Council of the National Academies, USA.

Nuss, Philip, et al. (2016), 'Mapping supply chain risk by network analysis of product platforms', Sustainable Materials and Technologies, 10, 14-22.

Richard, Christine and Wachter, Daniel (2012), 'Sustainable Development in Switzerland - A Guide', Interdepartmental Sustainable Development Committee (ISDC) c/o Federal Office for Spatial Development (ARE).

Roelich, Katy, et al. (2014), 'Assessing the dynamic material criticality of infrastructure transitions: A case of low carbon electricity', Applied Energy, 123, 378-86.

Schneider, Laura, et al. (2013), 'The economic resource scarcity potential (ESP) for evaluating resource use based on life cycle assessment', The International Journal of Life Cycle Assessment, 19 (3), 601-10.

Schrijvers, Dieuwertje, et al. (2019), 'A review of methods and data to determine raw material criticality', Resources, Conservation & Recycling: X, 100023.

Sonnemann, Guido, et al. (2015), 'From a critical review to a conceptual framework for integrating the criticality of resources into Life Cycle Sustainability Assessment', Journal of Cleaner Production, 94, 20-34.

Wäger, Patrick A., Hischier, Roland, and Widmer, Rolf (2015), 'The Material Basis of ICT', (ICT Innovations for Sustainability; Cham: Springer International Publishing), 209-21.

Work package 4

Motivation

For a high-quality evaluation of Swiss production and consumption and the associated supply chains, it is important to base the analysis on high-quality inventories. However, current data is partially lacking details in some datasets relevant for the creation of a merged database, and in some cases the required geographic resolution for e.g. social assessments. Furthermore, certain economic sectors might not be sufficiently represented. Therefore, the need for better quality data is apparent.

Goal

Extending and updating the current data represents the goal of this work package. New data will be available to the large ecoinvent user base and therefore increase the quality of LCA research and applications worldwide.

Approach

In a first step, major data gaps and quality issues in the preliminary merged LCA/MRIO data will be identified based on input data from WP1 and analysis and methodological advancements made in WP3 and WP5. These gaps and quality issues will be prioritized according to their relevance for the sustainability assessment of Swiss production and consumption. Identification and prioritization will include environmental issues (i.e. the standard entries of the ecoinvent database), social issues (based on the integration of the SHDB performed in WP2) and resource supply risk issues (based on the outcomes of WP3).

In a second step, data gaps will be filled by updating existing and creating new ecoinvent inventory datasets and creating new inventory data, which will be supplied to and integrated into the ecoinvent and the merged LCA/MRIO databases.

Data collection and processing will be shared among PSI, Empa, and ecoinvent.

Work package 5

Goal

The goal of this work package is to systematically contribute to the quality of the integrated assessment of Swiss consumption and production.

Approach

One of the main approaches to address this goal is to improve data quality in inventories by means of identifying processes that contribute the most to uncertainties in LCA results and collecting more data for them. This work will employ Global Sensitivity Analysis, where the main challenges lie in a very high-dimensional nature of the problem, as well as the issue of input correlations, and validation and interpretation of results.
Within this work package we will also update the existing modelling of Swiss consumption with newer data.