Abstract
To inform decision making for the sustainable use of plastic, policymakers require accounts of all short-term and long-term use of plastic at a national level. However, the
underlying data used for plastics accounting requires extensive time and effort for collation or estimation. Generative AI has recently demonstrated promise in research tools due to its use of extensive knowledge bases. In this study, we tested whether GPT, the model behind Open Artificial Intelligence’s ChatGPT, could produce estimates for plastic accounting tasks that are comparable to experts. Fine-tuned GPT-3.5 turbo models were used for two tasks; i) estimating the material composition of imported products by plastic type, and ii) estimating the volumes of eight polymers in packaging in use in the Norwegian economy between 1951-2020. We tested whether the training dataset size and modifying model hyperparameters improved performance. Increasing the training dataset size was found to improve the performance of models trained for the first task, while increasing the number of training rounds improved the precision and accuracy of models trained for the second task. However, in both tasks models did not provide estimates comparable to those provided by experts. Although future generative AI models that are specifically trained for accounting tasks may increasingly become useful tools for reducing manual work, currently they do not provide reliable estimates.