In connection with our involvement in the BIZENTE project issues around LCA on ”an unknown chemical substance” as the new BIO based Resins, that have been developed in the project. To conduct an LCA on such a newly developed chemical structure, it is necessary to identify the chemical substance and obtain information about its production, use, and disposal. This includes information on the raw materials used in production, the energy and resources required for manufacturing, the chemical’s properties and behavior during use, and the potential environmental impacts associated with its disposal or release into the environment.

LCA Data is derived from databases, where product emissions are determined. Without this information, it is not possible to accurately assess the environmental impacts of the chemical substance or to compare it to other chemicals or products. In the absence of detailed information about the substance, it may be necessary to conduct further research or testing to identify its properties and potential impacts. A comparative study of similar chemical structures can help identify the environmental performance of these materials. In a common framework for the comparison, such as the use of a standardized LCA methodology or a set of criteria can enable a valuation.

When such a comparative analysis is possible and performed, we can Interpret the results and draw conclusions about the relative performance of the resins. However different production assumptions in the comparative study can lead to data problems when comparing similar resins. If the assumptions used to model the production of each resin are significantly different, then the resulting data may not be directly comparable, and the conclusions drawn from the analysis may be inaccurate. An example is seen when one production facility is using energy from renewable sources while other are using natural gas.

To address this issue, it is important to clearly define the scope and assumptions of the study at the outset, and to communicate these assumptions transparently throughout the analysis. This can help stakeholders to understand the limitations and potential biases of the analysis and to interpret the results more effectively.

By Kurt Sondergaard and Jens Olsen, European Composite Recycle Technology