  Concerns about the effect of greenhouse gases have motivated the development
of certification protocols to quantify the industrial carbon footprint (CF).
These protocols are manual, work-intensive, and expensive. All of the above
have led to a shift towards automatic data-driven approaches to estimate the
CF, including Machine Learning (ML) solutions. Unfortunately, the
decision-making processes involved in these solutions lack transparency from
the end user's point of view, who must blindly trust their outcomes compared to
intelligible traditional manual approaches. In this research, manual and
automatic methodologies for CF estimation were reviewed, taking into account
their transparency limitations. This analysis led to the proposal of a new
explainable ML solution for automatic CF calculations through bank transaction
classification. Consideration should be given to the fact that no previous
research has considered the explainability of bank transaction classification
for this purpose. For classification, different ML models have been employed
based on their promising performance in the literature, such as Support Vector
Machine, Random Forest, and Recursive Neural Networks. The results obtained
were in the 90 % range for accuracy, precision, and recall evaluation metrics.
From their decision paths, the proposed solution estimates the CO2 emissions
associated with bank transactions. The explainability methodology is based on
an agnostic evaluation of the influence of the input terms extracted from the
descriptions of transactions using locally interpretable models. The
explainability terms were automatically validated using a similarity metric
over the descriptions of the target categories. Conclusively, the explanation
performance is satisfactory in terms of the proximity of the explanations to
the associated activity sector descriptions.
