How to Apply System Dynamics Sustainability Assessments (SDSA) to Circular Economy Solutions

Authors: Alexander Koch & Tomáš Slaný (GreenDelta)

The sustainability assessments of circular economy solutions linked to the 3 different key value chains upgraded in the TREASoURcE project lead to a range of innovative approaches and insightful results. Extending sustainability assessments with system dynamics provided deeper insights into the behaviour of the broader system linked to the assessed product systems. This article aims to share some of the key learnings of applying a novel system dynamics sustainability assessment (SDSA) approach to generate region-specific and time-based impact results.

Read the use cases:

The Most Important Requirements for Success

The findings in each SDSA case study generally back the need for a wider system view in sustainability assessments, as it reduces the risk of decisions that lead to unintended, indirect effects. Moreover, understanding system forces makes it possible to identify and explore conditions that leverage sustainable solutions. System dynamics models will nevertheless always depend on the available data and modelling choices. The question remains to what extent the modelled system can support the decisions to be made. Also, whether the data used is suitable and reliable enough. Involving relevant stakeholders early on, applying a validation approach and using the right tools can support in building robust and credible results. 

Available data

Finding good data is one of the biggest challenges when conducting sustainability assessments. As the SDSA extends the scope of a classic life cycle assessment (LCA) to also include wider system elements, such as the market or the regional context, the data requirements are enhanced even further. This applies in particular to regional level assessments. A flexible and iterative framework application is therefore key, meaning the goal and scope of the assessment needs to be iteratively adapted to match the data available.  

Modelling choices

In system dynamics models the defined relationships between model variables essentially determine how the system behaves. These relationships are best defined on the basis of proven statistical correlations. In most cases such data will not be available. Consequently, modellers must often rely on theoretical assumptions, expert judgment, empirical observations, and qualitative insights to formulate the causal relationships within the system. While this introduces a degree of uncertainty into the model, careful validation and sensitivity analysis can help ensure that the resulting behaviour remains realistic and useful for decision-making.

Involving stakeholders and experts

Closely involving relevant stakeholders and experts has proven to be very beneficial. Experts contribute practical experience, contextual knowledge, and technical expertise that help define realistic relationships between variables. A collaborative participation of relevant stakeholders helps identify key issues, system boundaries, relevant variables, and important feedback loops that might otherwise be overlooked. It also helps match the depth of the assessment with the decision to be made.

Validation

Ensuring that the assessment approach is reliable is essential for using the SDSA results to support the decision-making process. Within the SDSA framework, this reliability is built through a validation procedure. The validation should be performed from the onset of the project and inform every step of model building, creating an iterative workflow informed by validation feedback. The SDSA approach should be validated on several levels, starting with the goal and problem alignment, followed by the structural validation (confirming both the formal and causal aspects of the model structure) and continuing with the behaviour validation. These procedures rely on the iterative improvement of understanding of the real system, which should be obtained through the involvement of the stakeholders and the problem owner.

Applying the right tools

A combined LCA and system dynamics modelling approach can be time-consuming in terms of data collection and model development, particularly when large datasets are involved. Especially for full-scope regional assessments, where multiple products and impact categories are assessed, the work can be resource-intensive and difficult to manage. A software tool that integrates both LCA and system dynamics modelling can significantly reduce the effort required for separate data handling and model coordination. Instead of building two independent models and manually transferring data between them, an integrated tool allows users to analyse environmental impacts and system behaviour within a single framework. Such a tool was developed in the TREASoURcE project and can now be access through the newest version of openLCA.

Legislative Aspects

An advisable legislative framework for establishing SDSA practices would include requirements for transparent sustainability reporting, a standardised framework, and the integration of long-term environmental, social, and economic considerations into policy and planning processes. In addition, legislation supporting stakeholder participation, access to reliable public data, and the use of assessment tools in regional or industrial decision-making would help ensure that SDSA can be effectively implemented and consistently applied.

Finance

With the new system dynamics feature in the open-source software openLCA, practicing SDSA generally requires moderate initial investment, mainly for staff training, data acquisition, model development, computing infrastructure, and stakeholder workshops rather than software licensing itself. Locally, SDSA practices can create business opportunities for sustainability consultants, data providers, research institutions, and technology developers by supporting regional planning, circular economy initiatives, and infrastructure development.

Stakeholders

Key stakeholders in regional-level SDSA typically include public authorities and policymakers who use results for planning and regulation, industry actors who provide operational data and are affected by transition scenarios, and consulting and research institutions that ensure methodological rigor and model development. Nevertheless, the approach is flexible and can be applied in all areas, where the relevant stakeholders then need to be identified. Stakeholder engagement improves model relevance and transparency by incorporating diverse perspectives, local knowledge, and decision-making priorities.

Environment

SDSA supports better-informed decision-making by revealing long-term feedbacks, trade-offs, and unintended consequences, which can lead to policies and interventions that reduce emissions, resource depletion, and other environmental impacts. It can also improve the targeting of sustainability strategies, reducing inefficient or counterproductive actions.

Organisatio

Preparation time for an SDSA depends on the complexity of the assessment scope. Regional assessments might take longer, mainly due to the need to assess a wider system scope, which leads to an increase in data requirements. Steps during the application of an SDSA include goal & scope definition, modelling, data collection, results calculation, and validation. Such projects generally require close coordination with relevant stakeholders in order to define requirements, align assumptions, validate model structures, and interpret and review scenario results.