How do you compare and benchmark different circular economy strategies using sensitivity analysis in LCA?

2 Why use sensitivity analysis for circular economy?

Circular economy is a concept that aims to reduce waste and resource consumption by keeping materials and products in use for as long as possible, and by regenerating natural systems. Circular economy strategies, such as reuse, recycling, or remanufacturing, can have different environmental impacts depending on the context and the trade-offs involved. For example, recycling can save raw materials and energy, but also generate emissions and require additional transport and processing. Sensitivity analysis can help you compare and benchmark different circular economy strategies by showing how the LCA results vary under different scenarios and assumptions.

4 What are the benefits of sensitivity analysis in LCA for circular economy?

Sensitivity analysis in LCA for circular economy can provide several benefits, such as improving the transparency and credibility of your LCA results, identifying key drivers of environmental impacts, supporting decision-making and communication of your circular economy strategy, and enabling comparison and benchmarking of different circular economy strategies. By running sensitivity analysis, you are able to show how your results depend on the input parameters and assumptions, highlight the opportunities and challenges for improvement, provide insights into the trade-offs, uncertainties, and risks involved, and demonstrate how different strategies perform under different conditions and scenarios.

5 What are the challenges of sensitivity analysis in LCA for circular economy?

Sensitivity analysis in LCA for circular economy can also pose some challenges. It is important to choose the appropriate type, level, and scope of sensitivity analysis depending on your goal, audience, and data availability. Collecting and managing the data and information for sensitivity analysis can be especially difficult for complex and dynamic systems that involve multiple actors and feedback loops. Furthermore, interpreting and presenting the results of sensitivity analysis clearly is important to avoid confusion or misinterpretation. It is also necessary to address the limitations and uncertainties of sensitivity analysis such as the assumptions, models, and methods used, as well as the interactions and synergies between parameters.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?